AblationScenario

Callable

ConfigurationScenario

Configurator

Extractor

Extractor base class for extracting features from instances.

sparkle.Extractor.__init__(self: Extractor, directory: Path) → None

Initialize solver.

Args:: directory: Directory of the solver. runsolver_exec: Path to the runsolver executable.

By default, runsolver in directory.

sparkle.Extractor.__repr__(self: Extractor) → str: Return detailed representation of the extractor.

sparkle.Extractor.__str__(self: Extractor) → str: Return the string representation of the extractor.

sparkle.Extractor.build_cmd(self: Extractor, instance: Path | list[Path], feature_group: str = None, output_file: Path = None, cutoff_time: int = None, log_dir: Path = None) → list[str]

Builds a command line string seperated by space.

Args:

instance: The instance to run on feature_group: The optional feature group to run the extractor for. output_file: Optional file to write the output to. runsolver_args: The arguments for runsolver. If not present,

will run the extractor without runsolver.

cutoff_time: The maximum runtime. log_dir: Directory path for logs.

Returns:

The command seperated per item in the list.

sparkle.Extractor.get_feature_vector(self: Extractor, result: Path, runsolver_values: Path = None) → list[str]

Extracts feature vector from an output file.

Args:: result: The raw output of the extractor runsolver_values: The output of runsolver.
Returns:: A list of features. Vector of missing values upon failure.

sparkle.Extractor.run(self: Extractor, instance: Path | list[Path], feature_group: str = None, output_file: Path = None, cutoff_time: int = None, log_dir: Path = None) → list[list[Any]] | list[Any] | None

Runs an extractor job with Runrunner.

Args:

extractor_path: Path to the executable instance: Path to the instance to run on feature_group: The feature group to compute. Must be supported by the

extractor to use.

output_file: Target output. If None, piped to the RunRunner job. cutoff_time: CPU cutoff time in seconds log_dir: Directory to write logs. Defaults to CWD.

Returns:

The features or None if an output file is used, or features can not be found.

sparkle.Extractor.run_cli(self: Extractor, instance_set: InstanceSet | list[Path], feature_dataframe: FeatureDataFrame, cutoff_time: int, feature_group: str = None, run_on: Runner = Runner.SLURM, sbatch_options: list[str] = None, srun_options: list[str] = None, parallel_jobs: int = None, slurm_prepend: str | list[str] | Path = None, dependencies: list[Run] = None, log_dir: Path = None) → None

Run the Extractor CLI and write result to the FeatureDataFrame.

Args:

instance_set: The instance set to run the Extractor on. feature_dataframe: The feature dataframe to write to. cutoff_time: CPU cutoff time in seconds feature_group: The feature group to compute. If left empty,

will run on all feature groups.

run_on: The runner to use. sbatch_options: Additional options to pass to sbatch. srun_options: Additional options to pass to srun. parallel_jobs: Number of parallel jobs to run. slurm_prepend: Slurm script to prepend to the sbatch dependencies: List of dependencies to add to the job. log_dir: The directory to write logs to.

FeatureDataFrame

Class to manage feature data CSV files and common operations on them.

sparkle.FeatureDataFrame.__add__(self, other)

Get Addition of DataFrame and other, column-wise.

Equivalent to DataFrame.add(other).

Parameters

otherscalar, sequence, Series, dict or DataFrame: Object to be added to the DataFrame.

Returns

DataFrame: The result of adding other to DataFrame.

Examples

>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
...                   index=['elk', 'moose'])
>>> df
       height  weight
elk       1.5     500
moose     2.6     800

Adding a scalar affects all rows and columns.

>>> df[['height', 'weight']] + 1.5
       height  weight
elk       3.0   501.5
moose     4.1   801.5

Each element of a list is added to a column of the DataFrame, in order.

>>> df[['height', 'weight']] + [0.5, 1.5]
       height  weight
elk       2.0   501.5
moose     3.1   801.5

Keys of a dictionary are aligned to the DataFrame, based on column names; each value in the dictionary is added to the corresponding column.

>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
       height  weight
elk       2.0   501.5
moose     3.1   801.5

When other is a Series, the index of other is aligned with the columns of the DataFrame.

>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
>>> df[['height', 'weight']] + s1
       height  weight
elk       3.0   500.5
moose     4.1   800.5

Even when the index of other is the same as the index of the DataFrame, the Series will not be reoriented. If index-wise alignment is desired, DataFrame.add() should be used with axis=’index’.

>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
>>> df[['height', 'weight']] + s2
       elk  height  moose  weight
elk    NaN     NaN    NaN     NaN
moose  NaN     NaN    NaN     NaN

>>> df[['height', 'weight']].add(s2, axis='index')
       height  weight
elk       2.0   500.5
moose     4.1   801.5

When other is a DataFrame, both columns names and the index are aligned.

>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
...                      index=['elk', 'moose', 'deer'])
>>> df[['height', 'weight']] + other
       height  weight
deer      NaN     NaN
elk       1.7     NaN
moose     3.0     NaN

sparkle.FeatureDataFrame.__arrow_c_stream__(self, requested_schema=None)

Export the pandas DataFrame as an Arrow C stream PyCapsule.

This relies on pyarrow to convert the pandas DataFrame to the Arrow format (and follows the default behaviour of pyarrow.Table.from_pandas in its handling of the index, i.e. store the index as a column except for RangeIndex). This conversion is not necessarily zero-copy.

Parameters

requested_schemaPyCapsule, default None: The schema to which the dataframe should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema.

Returns

PyCapsule

sparkle.FeatureDataFrame.__contains__(self, key) → bool: True if the key is in the info axis

sparkle.FeatureDataFrame.__dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True) → DataFrameXchg

Return the dataframe interchange object implementing the interchange protocol.

Parameters

nan_as_nullbool, default False: nan_as_null is DEPRECATED and has no effect. Please avoid using it; it will be removed in a future release.
allow_copybool, default True: Whether to allow memory copying when exporting. If set to False it would cause non-zero-copy exports to fail.

Returns

DataFrame interchange object: The object which consuming library can use to ingress the dataframe.

Notes

Details on the interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html

Examples

>>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
>>> interchange_object = df_not_necessarily_pandas.__dataframe__()
>>> interchange_object.column_names()
Index(['A', 'B'], dtype='object')
>>> df_pandas = (pd.api.interchange.from_dataframe
...              (interchange_object.select_columns_by_name(['A'])))
>>> df_pandas
     A
0    1
1    2

These methods (column_names, select_columns_by_name) should work for any dataframe library which implements the interchange protocol.

sparkle.FeatureDataFrame.__dataframe_consortium_standard__(self, *, api_version: str | None = None) → Any

Provide entry point to the Consortium DataFrame Standard API.

This is developed and maintained outside of pandas. Please report any issues to https://github.com/data-apis/dataframe-api-compat.

sparkle.FeatureDataFrame.__deepcopy__(self, memo=None) → None

Parameters

memo, default None: Standard signature. Unused

sparkle.FeatureDataFrame.__delitem__(self, key) → None: Delete item

sparkle.FeatureDataFrame.__dir__(self) → list[str]: Provide method name lookup and completion.

Notes

Only provide ‘public’ methods.

sparkle.FeatureDataFrame.__eq__(self, other): Return self==value.

sparkle.FeatureDataFrame.__finalize__(self, other, method: str | None = None, **kwargs) → None

Propagate metadata from other to self.

Parameters

otherthe object from which to get the attributes that we are going: to propagate
methodstr, optional: A passed method name providing context on where __finalize__ was called.

Warning

The value passed as method are not currently considered stable across pandas releases.

sparkle.FeatureDataFrame.__ge__(self, other): Return self>=value.

sparkle.FeatureDataFrame.__getattr__(self, name: str): After regular attribute access, try looking up the name This allows simpler access to columns for interactive use.

sparkle.FeatureDataFrame.__getstate__(self) → dict[str, Any]: Helper for pickle.

sparkle.FeatureDataFrame.__gt__(self, other): Return self>value.

sparkle.FeatureDataFrame.__init__(self: FeatureDataFrame, csv_filepath: Path, instances: list[str] = [], extractor_data: dict[str, list[tuple[str, str]]] = {}) → None

Initialise a FeatureDataFrame object.

Arguments:

csv_filepath: The Path for the CSV storage. If it does not exist,: a new DataFrame will be initialised and stored here.

instances: The list of instances (Columns) to be added to the DataFrame. extractor_data: A dictionary with extractor names as key, and a list of

tuples ordered as [(feature_group, feature_name), …] as value.

sparkle.FeatureDataFrame.__iter__(self) → Iterator

Iterate over info axis.

Returns

iterator: Info axis as iterator.

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> for x in df:
...     print(x)
A
B

sparkle.FeatureDataFrame.__le__(self, other): Return self<=value.

sparkle.FeatureDataFrame.__len__(self) → int: Returns length of info axis, but here we use the index.

sparkle.FeatureDataFrame.__lt__(self, other): Return self<value.

sparkle.FeatureDataFrame.__matmul__(self, other: AnyArrayLike | DataFrame) → DataFrame | Series: Matrix multiplication using binary @ operator.

sparkle.FeatureDataFrame.__ne__(self, other): Return self!=value.

sparkle.FeatureDataFrame.__or__(self, other): Return self|value.

sparkle.FeatureDataFrame.__repr__(self) → str: Return a string representation for a particular DataFrame.

sparkle.FeatureDataFrame.__rmatmul__(self, other) → DataFrame: Matrix multiplication using binary @ operator.

sparkle.FeatureDataFrame.__ror__(self, other): Return value|self.

sparkle.FeatureDataFrame.__setattr__(self, name: str, value) → None: After regular attribute access, try setting the name This allows simpler access to columns for interactive use.

sparkle.FeatureDataFrame.__sizeof__(self) → int: Generates the total memory usage for an object that returns either a value or Series of values

sparkle.FeatureDataFrame._align_for_op(self, other, axis: AxisInt, flex: bool | None = False, level: Level | None = None): Convert rhs to meet lhs dims if input is list, tuple or np.ndarray.

Parameters

left : DataFrame right : Any axis : int flex : bool or None, default False

Whether this is a flex op, in which case we reindex. None indicates not to check for alignment.

level : int or level name, default None

Returns

left : DataFrame right : Any

sparkle.FeatureDataFrame._arith_method_with_reindex(self, right: DataFrame, op) → DataFrame: For DataFrame-with-DataFrame operations that require reindexing, operate only on shared columns, then reindex.

Parameters

right : DataFrame op : binary operator

Returns

DataFrame

sparkle.FeatureDataFrame._as_manager(self, typ: str, copy: bool = True) → None

Private helper function to create a DataFrame with specific manager.

Parameters

typ : {“block”, “array”} copy : bool, default True

Only controls whether the conversion from Block->ArrayManager copies the 1D arrays (to ensure proper/contiguous memory layout).

Returns

DataFrame: New DataFrame using specified manager type. Is not guaranteed to be a copy or not.

sparkle.FeatureDataFrame._box_col_values(self, values: SingleDataManager, loc: int) → Series: Provide boxed values for a column.

sparkle.FeatureDataFrame._check_is_chained_assignment_possible(self) → bool

Check if we are a view, have a cacher, and are of mixed type. If so, then force a setitem_copy check.

Should be called just near setting a value

Will return a boolean if it we are a view and are cached, but a single-dtype meaning that the cacher should be updated following setting.

sparkle.FeatureDataFrame._check_label_or_level_ambiguity(self, key: Hashable, axis: int | Literal['index', 'columns', 'rows'] = 0) → None

Check whether key is ambiguous.

By ambiguous, we mean that it matches both a level of the input axis and a label of the other axis.

Parameters

keyHashable: Label or level name.
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns).

Raises

ValueError: key is ambiguous

sparkle.FeatureDataFrame._check_setitem_copy(self, t: str = 'setting', force: bool = False): Parameters

t : str, the type of setting error force : bool, default False

If True, then force showing an error.

validate if we are doing a setitem on a chained copy.

It is technically possible to figure out that we are setting on a copy even WITH a multi-dtyped pandas object. In other words, some blocks may be views while other are not. Currently _is_view will ALWAYS return False for multi-blocks to avoid having to handle this case.

df = DataFrame(np.arange(0,9), columns=[‘count’]) df[‘group’] = ‘b’

# This technically need not raise SettingWithCopy if both are view # (which is not generally guaranteed but is usually True. However, # this is in general not a good practice and we recommend using .loc. df.iloc[0:5][‘group’] = ‘a’

sparkle.FeatureDataFrame._consolidate(self): Compute NDFrame with “consolidated” internals (data of each dtype grouped together in a single ndarray).

Returns

consolidated : same type as caller

sparkle.FeatureDataFrame._consolidate_inplace(self) → None: Consolidate data in place and return None

sparkle.FeatureDataFrame._construct_axes_dict(self, axes: Sequence[Axis] | None = None, **kwargs): Return an axes dictionary for myself.

sparkle.FeatureDataFrame._construct_result(self, result) → DataFrame: Wrap the result of an arithmetic, comparison, or logical operation.

Parameters

result : DataFrame

Returns

DataFrame

sparkle.FeatureDataFrame._create_data_for_split_and_tight_to_dict(self, are_all_object_dtype_cols: bool, object_dtype_indices: list[int]) → list: Simple helper method to create data for to to_dict(orient="split") and to_dict(orient="tight") to create the main output data

sparkle.FeatureDataFrame._dir_additions(self) → set[str]: add the string-like attributes from the info_axis. If info_axis is a MultiIndex, its first level values are used.

sparkle.FeatureDataFrame._dir_deletions(self) → set[str]: Delete unwanted __dir__ for this object.

sparkle.FeatureDataFrame._dispatch_frame_op(self, right, func: Callable, axis: AxisInt | None = None) → DataFrame: Evaluate the frame operation func(left, right) by evaluating column-by-column, dispatching to the Series implementation.

Parameters

right : scalar, Series, or DataFrame func : arithmetic or comparison operator axis : {None, 0, 1}

Returns

DataFrame

Notes

Caller is responsible for setting np.errstate where relevant.

sparkle.FeatureDataFrame._drop_axis(self, labels, axis, level=None, errors: Literal['ignore', 'raise'] = 'raise', only_slice: bool = False) → None

Drop labels from specified axis. Used in the drop method internally.

Parameters

labels : single label or list-like axis : int or axis name level : int or level name, default None

For MultiIndex

errors{‘ignore’, ‘raise’}, default ‘raise’: If ‘ignore’, suppress error and existing labels are dropped.
only_slicebool, default False: Whether indexing along columns should be view-only.

sparkle.FeatureDataFrame._drop_labels_or_levels(self, keys, axis: int = 0)

Drop labels and/or levels for the given axis.

For each key in keys:

(axis=0): If key matches a column label then drop the column. Otherwise if key matches an index level then drop the level.
(axis=1): If key matches an index label then drop the row. Otherwise if key matches a column level then drop the level.

Parameters

keysstr or list of str: labels or levels to drop
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

dropped: DataFrame

Raises

ValueError: if any keys match neither a label nor a level

sparkle.FeatureDataFrame._ensure_valid_index(self, value) → None: Ensure that if we don’t have an index, that we can create one from the passed value.

sparkle.FeatureDataFrame._find_valid_index(self, *, how: str) → Hashable | None

Retrieves the index of the first valid value.

Parameters

how{‘first’, ‘last’}: Use this parameter to change between the first or last valid index.

Returns

idx_first_valid : type of index

sparkle.FeatureDataFrame._get_agg_axis(self, axis_num: int) → Index: Let’s be explicit about this.

sparkle.FeatureDataFrame._get_cleaned_column_resolvers(self) → dict[Hashable, Series]

Return the special character free column resolvers of a dataframe.

Column names with special characters are ‘cleaned up’ so that they can be referred to by backtick quoting. Used in DataFrame.eval().

sparkle.FeatureDataFrame._get_column_array(self, i: int) → ArrayLike

Get the values of the i’th column (ndarray or ExtensionArray, as stored in the Block)

Warning! The returned array is a view but doesn’t handle Copy-on-Write, so this should be used with caution (for read-only purposes).

sparkle.FeatureDataFrame._get_item_cache(self, item: Hashable) → Series: Return the cached item, item represents a label indexer.

sparkle.FeatureDataFrame._get_label_or_level_values(self, key: Hashable, axis: int = 0) → ExtensionArray | ndarray

Return a 1-D array of values associated with key, a label or level from the given axis.

Retrieval logic:

(axis=0): Return column values if key matches a column label. Otherwise return index level values if key matches an index level.
(axis=1): Return row values if key matches an index label. Otherwise return column level values if ‘key’ matches a column level

Parameters

keyHashable: Label or level name.
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

np.ndarray or ExtensionArray

Raises

KeyError: if key matches neither a label nor a level
ValueError: if key matches multiple labels

sparkle.FeatureDataFrame._get_value(self, index, col, takeable: bool = False) → Scalar: Quickly retrieve single value at passed column and index.

Parameters

index : row label col : column label takeable : interpret the index/col as indexers, default False

Returns

scalar

Notes

Assumes that both self.index._index_as_unique and self.columns._index_as_unique; Caller is responsible for checking.

sparkle.FeatureDataFrame._getitem_nocopy(self, key: list): Behaves like __getitem__, but returns a view in cases where __getitem__ would make a copy.

sparkle.FeatureDataFrame._getitem_slice(self, key: slice) → None: __getitem__ for the case where the key is a slice object.

sparkle.FeatureDataFrame._gotitem(self, key: IndexLabel, ndim: int, subset: DataFrame | Series | None = None) → DataFrame | Series

Sub-classes to define. Return a sliced object.

Parameters

key : string / list of selections ndim : {1, 2}

requested ndim of result

subsetobject, default None: subset to act on

sparkle.FeatureDataFrame._info_repr(self) → bool: True if the repr should show the info view.

sparkle.FeatureDataFrame._inplace_method(self, other, op) → None: Wrap arithmetic method to operate inplace.

sparkle.FeatureDataFrame._is_label_or_level_reference(self, key: Hashable, axis: int = 0) → bool

Test whether a key is a label or level reference for a given axis.

To be considered either a label or a level reference, key must be a string that:

(axis=0): Matches a column label or an index level

(axis=1): Matches an index label or a column level

Parameters

keyHashable: Potential label or level name
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

bool

sparkle.FeatureDataFrame._is_label_reference(self, key: Hashable, axis: int | Literal['index', 'columns', 'rows'] = 0) → bool

Test whether a key is a label reference for a given axis.

To be considered a label reference, key must be a string that:

(axis=0): Matches a column label
(axis=1): Matches an index label

Parameters

keyHashable: Potential label name, i.e. Index entry.
axisint, default 0: Axis perpendicular to the axis that labels are associated with (0 means search for column labels, 1 means search for index labels)

Returns

is_label: bool

sparkle.FeatureDataFrame._is_level_reference(self, key: Hashable, axis: int | Literal['index', 'columns', 'rows'] = 0) → bool

Test whether a key is a level reference for a given axis.

To be considered a level reference, key must be a string that:

(axis=0): Matches the name of an index level and does NOT match a column label.
(axis=1): Matches the name of a column level and does NOT match an index label.

Parameters

keyHashable: Potential level name for the given axis
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

is_level : bool

sparkle.FeatureDataFrame._iter_column_arrays(self) → Iterator[ArrayLike]

Iterate over the arrays of all columns in order. This returns the values as stored in the Block (ndarray or ExtensionArray).

Warning! The returned array is a view but doesn’t handle Copy-on-Write, so this should be used with caution (for read-only purposes).

sparkle.FeatureDataFrame._ixs(self, i: int, axis: AxisInt = 0) → Series: Parameters

i : int axis : int

Returns

Series

sparkle.FeatureDataFrame._maybe_align_series_as_frame(self, series: Series, axis: AxisInt): If the Series operand is not EA-dtype, we can broadcast to 2D and operate blockwise.

sparkle.FeatureDataFrame._maybe_cache_changed(self, item, value: Series, inplace: bool) → None: The object has called back to us saying maybe it has changed.

sparkle.FeatureDataFrame._maybe_update_cacher(self, clear: bool = False, verify_is_copy: bool = True, inplace: bool = False) → None

See if we need to update our parent cacher if clear, then clear our cache.

Parameters

clearbool, default False: Clear the item cache.
verify_is_copybool, default True: Provide is_copy checks.

sparkle.FeatureDataFrame._needs_reindex_multi(self, axes, method, level: Hashable | None) → bool: Check if we do need a multi reindex.

sparkle.FeatureDataFrame._protect_consolidate(self, f): Consolidate _mgr – if the blocks have changed, then clear the cache

sparkle.FeatureDataFrame._reduce_axis1(self, name: str, func, skipna: bool) → Series

Special case for _reduce to try to avoid a potentially-expensive transpose.

Apply the reduction block-wise along axis=1 and then reduce the resulting 1D arrays.

sparkle.FeatureDataFrame._reindex_axes(self, axes, level: Level | None, limit: int | None, tolerance, method, fill_value: Scalar | None, copy: bool_t | None) → Self: Perform the reindex for all the axes.

sparkle.FeatureDataFrame._reindex_multi(self, axes: dict[str, Index], copy: bool, fill_value) → DataFrame: We are guaranteed non-Nones in the axes.

sparkle.FeatureDataFrame._reindex_with_indexers(self, reindexers, fill_value=None, copy: bool | None = False, allow_dups: bool = False) → None: allow_dups indicates an internal call here

sparkle.FeatureDataFrame._replace_columnwise(self, mapping: dict[Hashable, tuple[Any, Any]], inplace: bool, regex)

Dispatch to Series.replace column-wise.

Parameters

mappingdict: of the form {col: (target, value)}

inplace : bool regex : bool or same types as to_replace in DataFrame.replace

Returns

DataFrame or None

sparkle.FeatureDataFrame._repr_data_resource_(self): Not a real Jupyter special repr method, but we use the same naming convention.

sparkle.FeatureDataFrame._repr_fits_horizontal_(self) → bool: Check if full repr fits in horizontal boundaries imposed by the display options width and max_columns.

sparkle.FeatureDataFrame._repr_fits_vertical_(self) → bool: Check length against max_rows.

sparkle.FeatureDataFrame._repr_html_(self) → str | None

Return a html representation for a particular DataFrame.

Mainly for IPython notebook.

sparkle.FeatureDataFrame._repr_latex_(self): Returns a LaTeX representation for a particular object. Mainly for use with nbconvert (jupyter notebook conversion to pdf).

sparkle.FeatureDataFrame._reset_cache(self, key: str | None = None) → None: Reset cached properties. If key is passed, only clears that key.

sparkle.FeatureDataFrame._reset_cacher(self) → None: Reset the cacher.

sparkle.FeatureDataFrame._sanitize_column(self, value) → tuple[ArrayLike, BlockValuesRefs | None]: Ensures new columns (which go into the BlockManager as new blocks) are always copied (or a reference is being tracked to them under CoW) and converted into an array.

Parameters

value : scalar, Series, or array-like

Returns

tuple of numpy.ndarray or ExtensionArray and optional BlockValuesRefs

sparkle.FeatureDataFrame._set_axis(self, axis: AxisInt, labels: AnyArrayLike | list) → None: This is called from the cython code when we set the index attribute directly, e.g. series.index = [1, 2, 3].

sparkle.FeatureDataFrame._set_axis_name(self, name, axis: int | Literal['index', 'columns', 'rows'] = 0, inplace: bool = False, copy: bool | None = True)

Set the name(s) of the axis.

Parameters

namestr or list of str: Name(s) to set.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to set the label. The value 0 or ‘index’ specifies index, and the value 1 or ‘columns’ specifies columns.
inplacebool, default False: If True, do operation inplace and return None.
copy:: Whether to make a copy of the result.

Returns

Series, DataFrame, or None: The same type as the caller or None if inplace is True.

Examples

>>> df = pd.DataFrame({"num_legs": [4, 4, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs
dog            4
cat            4
monkey         2
>>> df._set_axis_name("animal")
        num_legs
animal
dog            4
cat            4
monkey         2
>>> df.index = pd.MultiIndex.from_product(
...                [["mammal"], ['dog', 'cat', 'monkey']])
>>> df._set_axis_name(["type", "name"])
               num_legs
type   name
mammal dog        4
       cat        4
       monkey     2

sparkle.FeatureDataFrame._set_item(self, key, value) → None

Add series to DataFrame in specified column.

If series is a numpy-array (not a Series/TimeSeries), it must be the same length as the DataFrames index or an error will be thrown.

Series/TimeSeries will be conformed to the DataFrames index to ensure homogeneity.

sparkle.FeatureDataFrame._set_value(self, index: IndexLabel, col, value: Scalar, takeable: bool = False) → None

Put single value at passed column and index.

Parameters

indexLabel: row label
colLabel: column label

value : scalar takeable : bool, default False

Sets whether or not index/col interpreted as indexers

sparkle.FeatureDataFrame._should_reindex_frame_op(self, right, op, axis: int, fill_value, level) → bool: Check if this is an operation between DataFrames that will need to reindex.

sparkle.FeatureDataFrame._slice(self, slobj: slice, axis: int = 0) → None

Construct a slice of this container.

Slicing with this method is always positional.

sparkle.FeatureDataFrame._take_with_is_copy(self, indices, axis: int | Literal['index', 'columns', 'rows'] = 0) → None

Internal version of the take method that sets the _is_copy attribute to keep track of the parent dataframe (using in indexing for the SettingWithCopyWarning).

For Series this does the same as the public take (it never sets _is_copy).

See the docstring of take for full explanation of the parameters.

sparkle.FeatureDataFrame._to_dict_of_blocks(self)

Return a dict of dtype -> Constructor Types that each is a homogeneous dtype.

Internal ONLY - only works for BlockManager

Render object to a LaTeX tabular, longtable, or nested table.

Uses the Styler implementation with the following, ordered, method chaining:

Parameters

bufstr, Path or StringIO-like, optional, default None: Buffer to write to. If None, the output is returned as a string.
hidedict, list of dict: Keyword args to pass to the method call of Styler.hide. If a list will call the method numerous times.
relabel_indexdict, list of dict: Keyword args to pass to the method of Styler.relabel_index. If a list will call the method numerous times.
formatdict, list of dict: Keyword args to pass to the method call of Styler.format. If a list will call the method numerous times.
format_indexdict, list of dict: Keyword args to pass to the method call of Styler.format_index. If a list will call the method numerous times.
render_kwargsdict: Keyword args to pass to the method call of Styler.to_latex.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

sparkle.FeatureDataFrame._update_inplace(self, result, verify_is_copy: bool = True) → None: Replace self internals with result.

Parameters

result : same type as self verify_is_copy : bool, default True

Provide is_copy checks.

sparkle.FeatureDataFrame._where(self, cond, other=<no_default>, inplace: bool = False, axis: int | ~typing.Literal['index', 'columns', 'rows'] | None = None, level=None, warn: bool = True): Equivalent to public method where, except that other is not applied as a function even if callable. Used in __setitem__.

sparkle.FeatureDataFrame.abs(self) → None

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns

abs: Series/DataFrame containing the absolute value of each element.

Notes

For complex inputs, 1.2 + 1j, the absolute value is \(\sqrt{ a^2 + b^2 }\).

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0   1 days
dtype: timedelta64[ns]

Select rows with data closest to certain value using argsort (from StackOverflow).

>>> df = pd.DataFrame({
...     'a': [4, 5, 6, 7],
...     'b': [10, 20, 30, 40],
...     'c': [100, 50, -30, -50]
... })
>>> df
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50

sparkle.FeatureDataFrame.add(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Addition of dataframe and other, element-wise (binary operator add).

Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, radd.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.add_extractor(self: FeatureDataFrame, extractor: str, extractor_features: list[tuple[str, str]], values: list[list[float]] = None) → None

Add an extractor and its feature names to the dataframe.

Arguments:: extractor: Name of the extractor extractor_features: Tuples of [FeatureGroup, FeatureName] values: Initial values of the Extractor per instance in the dataframe.

Defaults to FeatureDataFrame.missing_value.

sparkle.FeatureDataFrame.add_instances(self: FeatureDataFrame, instance: str | list[str], values: list[float] = None) → None: Add one or more instances to the dataframe.

sparkle.FeatureDataFrame.add_prefix(self, prefix: str, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters

prefixstr: The string to add before each label.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Axis to add prefix on

Added in version 2.0.0.

Returns

Series or DataFrame: New Series or DataFrame with updated labels.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_prefix('item_')
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_prefix('col_')
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6

sparkle.FeatureDataFrame.add_suffix(self, suffix: str, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters

suffixstr: The string to add after each label.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Axis to add suffix on

Added in version 2.0.0.

Returns

Series or DataFrame: New Series or DataFrame with updated labels.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_suffix('_item')
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_suffix('_col')
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6

sparkle.FeatureDataFrame.agg(self, func=None, axis: Axis = 0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters

funcfunction, str, list or dict

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:

function
string function name
list of functions and/or function names, e.g. [np.sum, 'mean']
dict of axis labels -> functions, function names or list of such.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns

scalar, Series or DataFrame

The return can be:

scalar : when Series.agg is called with single function

Series : when DataFrame.agg is called with a single function

DataFrame : when DataFrame.agg is called with several functions

Notes

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d) as opposed to numpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples

>>> df = pd.DataFrame([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64

sparkle.FeatureDataFrame.aggregate(self, func=None, axis: Axis = 0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters

funcfunction, str, list or dict

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:

function
string function name
list of functions and/or function names, e.g. [np.sum, 'mean']
dict of axis labels -> functions, function names or list of such.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns

scalar, Series or DataFrame

The return can be:

scalar : when Series.agg is called with single function

Series : when DataFrame.agg is called with a single function

DataFrame : when DataFrame.agg is called with several functions

Notes

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d) as opposed to numpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples

>>> df = pd.DataFrame([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters

other : DataFrame or Series join : {‘outer’, ‘inner’, ‘left’, ‘right’}, default ‘outer’

Type of alignment to be performed.

left: use only keys from left frame, preserve key order.

right: use only keys from right frame, preserve key order.

outer: use union of keys from both frames, sort keys lexicographically.

inner: use intersection of keys from both frames, preserve the order of the left keys.

axisallowed axis of the other object, default None

Align on index (0), columns (1), or both (None).

levelint or level name, default None

Broadcast across a level, matching Index values on the passed MultiIndex level.

copybool, default True

Always returns new objects. If copy=False and no reindexing is required then original objects are returned.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

fill_valuescalar, default np.nan

Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

method{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None

Method to use for filling holes in reindexed Series:

pad / ffill: propagate last valid observation forward to next valid.
backfill / bfill: use NEXT valid observation to fill gap.

Deprecated since version 2.1.

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

Deprecated since version 2.1.

fill_axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame, default 0

Filling axis, method and limit.

Deprecated since version 2.1.

broadcast_axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame, default None

Broadcast values along this axis, if aligning two objects of different dimensions.

Deprecated since version 2.1.

Returns

tuple of (Series/DataFrame, type of other): Aligned objects.

Examples

>>> df = pd.DataFrame(
...     [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
... )
>>> other = pd.DataFrame(
...     [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
...     columns=["A", "B", "C", "D"],
...     index=[2, 3, 4],
... )
>>> df
   D  B  E  A
1  1  2  3  4
2  6  7  8  9
>>> other
    A    B    C    D
2   10   20   30   40
3   60   70   80   90
4  600  700  800  900

Align on columns:

>>> left, right = df.align(other, join="outer", axis=1)
>>> left
   A  B   C  D  E
1  4  2 NaN  1  3
2  9  7 NaN  6  8
>>> right
    A    B    C    D   E
2   10   20   30   40 NaN
3   60   70   80   90 NaN
4  600  700  800  900 NaN

We can also align on the index:

>>> left, right = df.align(other, join="outer", axis=0)
>>> left
    D    B    E    A
1  1.0  2.0  3.0  4.0
2  6.0  7.0  8.0  9.0
3  NaN  NaN  NaN  NaN
4  NaN  NaN  NaN  NaN
>>> right
    A      B      C      D
1    NaN    NaN    NaN    NaN
2   10.0   20.0   30.0   40.0
3   60.0   70.0   80.0   90.0
4  600.0  700.0  800.0  900.0

Finally, the default axis=None will align on both index and columns:

>>> left, right = df.align(other, join="outer", axis=None)
>>> left
     A    B   C    D    E
1  4.0  2.0 NaN  1.0  3.0
2  9.0  7.0 NaN  6.0  8.0
3  NaN  NaN NaN  NaN  NaN
4  NaN  NaN NaN  NaN  NaN
>>> right
       A      B      C      D   E
1    NaN    NaN    NaN    NaN NaN
2   10.0   20.0   30.0   40.0 NaN
3   60.0   70.0   80.0   90.0 NaN
4  600.0  700.0  800.0  900.0 NaN

sparkle.FeatureDataFrame.all(self, axis: Axis | None = 0, bool_only: bool = False, skipna: bool = True, **kwargs) → Series | bool

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.

bool_onlybool, default False

Include only boolean columns. Not implemented for Series.

skipnabool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

**kwargsany, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: If level is specified, then, DataFrame is returned; otherwise, Series is returned.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if values in each column all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if values in each row all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False

sparkle.FeatureDataFrame.any(self, *, axis: Axis | None = 0, bool_only: bool = False, skipna: bool = True, **kwargs) → Series | bool

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.

bool_onlybool, default False

Include only boolean columns. Not implemented for Series.

skipnabool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

**kwargsany, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: If level is specified, then, DataFrame is returned; otherwise, Series is returned.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
   A  B  C
0  1  0  0
1  2  2  0

>>> df.any()
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2

>>> df.any(axis='columns')
0    True
1    True
dtype: bool

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0

>>> df.any(axis='columns')
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)

sparkle.FeatureDataFrame.apply(self, func: AggFuncType, axis: Axis = 0, raw: bool = False, result_type: Literal['expand', 'reduce', 'broadcast'] | None = None, args=(), by_row: Literal[False, 'compat'] = 'compat', engine: Literal['python', 'numba'] = 'python', engine_kwargs: dict[str, bool] | None = None, **kwargs)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

Parameters

funcfunction

Function to apply to each column or row.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Axis along which the function is applied:

0 or ‘index’: apply function to each column.
1 or ‘columns’: apply function to each row.

rawbool, default False

Determines if row or column is passed as a Series or ndarray object:

False : passes each row or column as a Series to the function.
True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.

result_type{‘expand’, ‘reduce’, ‘broadcast’, None}, default None

These only act when axis=1 (columns):

‘expand’ : list-like results will be turned into columns.
‘reduce’ : returns a Series if possible rather than expanding list-like results. This is the opposite of ‘expand’.
‘broadcast’ : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.

The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.

argstuple

Positional arguments to pass to func in addition to the array/series.

by_rowFalse or “compat”, default “compat”

Only has an effect when func is a listlike or dictlike of funcs and the func isn’t a string. If “compat”, will if possible first translate the func into pandas methods (e.g. Series().apply(np.sum) will be translated to Series().sum()). If that doesn’t work, will try call to apply again with by_row=True and if that fails, will call apply again with by_row=False (backward compatible). If False, the funcs will be passed the whole Series at once.

Added in version 2.1.0.

engine{‘python’, ‘numba’}, default ‘python’

Choose between the python (default) engine or the numba engine in apply.

The numba engine will attempt to JIT compile the passed function, which may result in speedups for large DataFrames. It also supports the following engine_kwargs :

nopython (compile the function in nopython mode)
nogil (release the GIL inside the JIT compiled function)
parallel (try to apply the function in parallel over the DataFrame)

Note: Due to limitations within numba/how pandas interfaces with numba, you should only use this if raw=True

Note: The numba compiler only supports a subset of valid Python/numpy operations.

Please read more about the supported python features and supported numpy features in numba to learn what you can or cannot use in the passed function.

Added in version 2.2.0.

engine_kwargsdict

Pass keyword arguments to the engine. This is currently only used by the numba engine, see the documentation for the engine argument for more information.

**kwargs

Additional keyword arguments to pass as keywords arguments to func.

Returns

Series or DataFrame: Result of applying func along the given axis of the DataFrame.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

Examples

>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
   A  B
0  4  9
1  4  9
2  4  9

Using a numpy universal function (in this case the same as np.sqrt(df)):

>>> df.apply(np.sqrt)
     A    B
0  2.0  3.0
1  2.0  3.0
2  2.0  3.0

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A    12
B    27
dtype: int64

>>> df.apply(np.sum, axis=1)
0    13
1    13
2    13
dtype: int64

Returning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
1
1  2
1  2
1  2

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2

Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.

>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
   A  B
0  1  2
1  1  2
2  1  2

sparkle.FeatureDataFrame.applymap(self, func: PythonFuncType, na_action: NaAction | None = None, **kwargs) → DataFrame

Apply a function to a Dataframe elementwise.

Deprecated since version 2.1.0: DataFrame.applymap has been deprecated. Use DataFrame.map instead.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters

funccallable: Python function, returns a single value from a single value.
na_action{None, ‘ignore’}, default None: If ‘ignore’, propagate NaN values, without passing them to func.
**kwargs: Additional keyword arguments to pass as keywords arguments to func.

Returns

DataFrame: Transformed DataFrame.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.map(lambda x: len(str(x)))
1
3  4
5  5

sparkle.FeatureDataFrame.asfreq(self, freq: Frequency, method: FillnaOptions | None = None, how: Literal['start', 'end'] | None = None, normalize: bool_t = False, fill_value: Hashable | None = None) → Self

Convert time series to specified frequency.

Returns the original data conformed to a new index with the specified frequency.

If the index of this Series/DataFrame is a PeriodIndex, the new index is the result of transforming the original index with PeriodIndex.asfreq (so the original index will map one-to-one to the new index).

Otherwise, the new index will be equivalent to pd.date_range(start, end, freq=freq) where start and end are, respectively, the first and last entries in the original index (see pandas.date_range()). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see the method parameter below).

The resample() method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.

Parameters

freqDateOffset or str

Frequency DateOffset or string.

method{‘backfill’/’bfill’, ‘pad’/’ffill’}, default None

Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):

‘pad’ / ‘ffill’: propagate last valid observation forward to next valid
‘backfill’ / ‘bfill’: use NEXT valid observation to fill.

how{‘start’, ‘end’}, default end

For PeriodIndex only (see PeriodIndex.asfreq).

normalizebool, default False

Whether to reset output index to midnight.

fill_valuescalar, optional

Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).

Returns

Series/DataFrame: Series/DataFrame object reindexed to the specified frequency.

Notes

To learn more about the frequency strings, please see this link.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='min')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s': series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30s')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30s', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30s', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0

sparkle.FeatureDataFrame.asof(self, where, subset=None)

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters

wheredate or array-like of dates: Date(s) before which the last row(s) are returned.
subsetstr or array-like of str, default None: For DataFrame, if not None, only use these columns to check for NaNs.

Returns

scalar, Series, or DataFrame

The return can be:

scalar : when self is a Series and where is a scalar

Series: when self is a Series and where is an array-like, or when self is a DataFrame and where is a scalar

DataFrame : when self is a DataFrame and where is an array-like

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64

>>> s.asof(20)
2.0

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
2.0

Take all columns into consideration

>>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
...                    'b': [None, None, None, None, 500]},
...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
...                                           '2018-02-27 09:02:00',
...                                           '2018-02-27 09:03:00',
...                                           '2018-02-27 09:04:00',
...                                           '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']),
...         subset=['a'])
                        a   b
2018-02-27 09:03:30  30.0 NaN
2018-02-27 09:04:30  40.0 NaN

sparkle.FeatureDataFrame.assign(self, **kwargs) → DataFrame

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters

**kwargsdict of {str: callable or Series}: The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

Returns

DataFrame: A new DataFrame with the new columns in addition to all the existing columns.

Notes

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Examples

>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15

sparkle.FeatureDataFrame.astype(self, dtype, copy: bool | None = None, errors: Literal['ignore', 'raise'] = 'raise') → None

Cast a pandas object to a specified dtype dtype.

Parameters

dtypestr, data type, Series or Mapping of column name -> data type

Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.

copybool, default True

Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

errors{‘raise’, ‘ignore’}, default ‘raise’

Control raising of exceptions on invalid data for provided dtype.

raise : allow exceptions to be raised
ignore : suppress exceptions. On error return original object.

Returns

same type as caller

Notes

Changed in version 2.0.0: Using astype to convert from timezone-naive dtype to timezone-aware dtype will raise an exception. Use Series.dt.tz_localize() instead.

Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype('int32').dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({'col1': 'int32'}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype('int64')
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')
0    1
1    2
dtype: category
Categories (2, int32): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
...     categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[ns]

sparkle.FeatureDataFrame.at_time(self, time, asof: bool = False, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Select values at particular time of day (e.g., 9:30AM).

Parameters

timedatetime.time or str: The values to select.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: For Series this parameter is unused and defaults to 0.

Returns

Series or DataFrame

Raises

TypeError: If the index is not a DatetimeIndex

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='12h')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-09 12:00:00  2
2018-04-10 00:00:00  3
2018-04-10 12:00:00  4

>>> ts.at_time('12:00')
                     A
2018-04-09 12:00:00  2
2018-04-10 12:00:00  4

Fill NA/NaN values by using the next valid observation to fill the gap.

Deprecated since version 2.0: Series/DataFrame.backfill is deprecated. Use Series/DataFrame.bfill instead.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

Please see examples for DataFrame.bfill() or Series.bfill().

sparkle.FeatureDataFrame.between_time(self, start_time, end_time, inclusive: Literal['left', 'right', 'both', 'neither'] = 'both', axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Select values between particular times of the day (e.g., 9:00-9:30 AM).

By setting start_time to be later than end_time, you can get the times that are not between the two times.

Parameters

start_timedatetime.time or str: Initial time as a time filter limit.
end_timedatetime.time or str: End time as a time filter limit.
inclusive{“both”, “neither”, “left”, “right”}, default “both”: Include boundaries; whether to set each bound as closed or open.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Determine range time on index or columns value. For Series this parameter is unused and defaults to 0.

Returns

Series or DataFrame: Data from the original object filtered to the specified dates range.

Raises

TypeError: If the index is not a DatetimeIndex

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4

>>> ts.between_time('0:15', '0:45')
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are not between two times by setting start_time later than end_time:

>>> ts.between_time('0:45', '0:15')
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4

Fill NA/NaN values by using the next valid observation to fill the gap.

Parameters

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

limit_area{None, ‘inside’, ‘outside’}, default None

If limit is specified, consecutive NaNs will be filled with this restriction.

None: No fill restriction.
‘inside’: Only fill NaNs surrounded by valid values (interpolate).
‘outside’: Only fill NaNs outside valid values (extrapolate).

Added in version 2.2.0.

downcastdict, default is None

A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Deprecated since version 2.2.0.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

For Series:

>>> s = pd.Series([1, None, None, 2])
>>> s.bfill()
0    1.0
1    2.0
2    2.0
3    2.0
dtype: float64
>>> s.bfill(limit=1)
0    1.0
1    NaN
2    2.0
3    2.0
dtype: float64

With DataFrame:

>>> df = pd.DataFrame({'A': [1, None, None, 4], 'B': [None, 5, None, 7]})
>>> df
      A     B
0   1.0   NaN
1   NaN   5.0
2   NaN   NaN
3   4.0   7.0
>>> df.bfill()
      A     B
0   1.0   5.0
1   4.0   5.0
2   4.0   7.0
3   4.0   7.0
>>> df.bfill(limit=1)
      A     B
0   1.0   5.0
1   NaN   5.0
2   4.0   7.0
3   4.0   7.0

sparkle.FeatureDataFrame.bool(self) → bool

Return the bool of a single element Series or DataFrame.

Deprecated since version 2.1.0: bool is deprecated and will be removed in future version of pandas. For Series use pandas.Series.item.

This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).

Returns

bool: The value in the Series or DataFrame.

Examples

The method will only work for single element objects with a boolean value:

>>> pd.Series([True]).bool()  
True
>>> pd.Series([False]).bool()  
False

>>> pd.DataFrame({'col': [True]}).bool()  
True
>>> pd.DataFrame({'col': [False]}).bool()  
False

This is an alternative method and will only work for single element objects with a boolean value:

>>> pd.Series([True]).item()  
True
>>> pd.Series([False]).item()  
False

sparkle.FeatureDataFrame.boxplot(self: DataFrame, column=None, by=None, ax=None, fontsize: int | None = None, rot: int = 0, grid: bool = True, figsize: tuple[float, float] | None = None, layout=None, return_type=None, backend=None, **kwargs)

Make a box plot from DataFrame columns.

Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns. A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. By default, they extend no more than 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box, ending at the farthest data point within that interval. Outliers are plotted as separate dots.

For further details see Wikipedia’s entry for boxplot.

Parameters

columnstr or list of str, optional

Column name or list of names, or vector. Can be any valid input to pandas.DataFrame.groupby().

bystr or array-like, optional

Column in the DataFrame to pandas.DataFrame.groupby(). One box-plot will be done per value of columns in by.

axobject of class matplotlib.axes.Axes, optional

The matplotlib axes to be used by boxplot.

fontsizefloat or str

Tick label font size in points or as a string (e.g., large).

rotfloat, default 0

The rotation angle of labels (in degrees) with respect to the screen coordinate system.

gridbool, default True

Setting this to True will show the grid.

figsizeA tuple (width, height) in inches

The size of the figure to create in matplotlib.

layouttuple (rows, columns), optional

For example, (3, 5) will display the subplots using 3 rows and 5 columns, starting from the top-left.

return_type{‘axes’, ‘dict’, ‘both’} or None, default ‘axes’

The kind of object to return. The default is axes.

‘axes’ returns the matplotlib axes the boxplot is drawn on.
‘dict’ returns a dictionary whose values are the matplotlib Lines of the boxplot.
‘both’ returns a namedtuple with the axes and dict.
when grouping with by, a Series mapping columns to return_type is returned.

If return_type is None, a NumPy array of axes with the same shape as layout is returned.

backendstr, default None

Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.

**kwargs

All other plotting keyword arguments to be passed to matplotlib.pyplot.boxplot().

Returns

result: See Notes.

Notes

The return type depends on the return_type parameter:

‘axes’ : object of class matplotlib.axes.Axes
‘dict’ : dict of matplotlib.lines.Line2D objects
‘both’ : a namedtuple with structure (ax, lines)

For data grouped with by, return a Series of the above or a numpy array:

Series
array (for return_type = None)

Use return_type='dict' when you want to tweak the appearance of the lines after plotting. In this case a dict containing the Lines making up the boxes, caps, fliers, medians, and whiskers is returned.

Examples

Boxplots can be created for every column in the dataframe by df.boxplot() or indicating the columns to be used:

Boxplots of variables distributions grouped by the values of a third variable can be created using the option by. For instance:

A list of strings (i.e. ['X', 'Y']) can be passed to boxplot in order to group the data by combination of the variables in the x-axis:

The layout of boxplot can be adjusted giving a tuple to layout:

Additional formatting can be done to the boxplot, like suppressing the grid (grid=False), rotating the labels in the x-axis (i.e. rot=45) or changing the fontsize (i.e. fontsize=15):

The parameter return_type can be used to select the type of element returned by boxplot. When return_type='axes' is selected, the matplotlib axes on which the boxplot is drawn are returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes')
>>> type(boxplot)
<class 'matplotlib.axes._axes.Axes'>

When grouping with by, a Series mapping columns to return_type is returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
...                      return_type='axes')
>>> type(boxplot)
<class 'pandas.core.series.Series'>

If return_type is None, a NumPy array of axes with the same shape as layout is returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
...                      return_type=None)
>>> type(boxplot)
<class 'numpy.ndarray'>

sparkle.FeatureDataFrame.clip(self, lower=None, upper=None, *, axis: Axis | None = None, inplace: bool_t = False, **kwargs) → Self | None

Trim values at input threshold(s).

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters

lowerfloat or array-like, default None: Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
upperfloat or array-like, default None: Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
axis{{0 or ‘index’, 1 or ‘columns’, None}}, default None: Align object with lower and upper along the given axis. For Series this parameter is unused and defaults to None.
inplacebool, default False: Whether to perform the operation in place on the data.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns

Series or DataFrame or None: Same type as calling object with the values outside the clip boundaries replaced or None if inplace=True.

Examples

>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
   col_0  col_1
0      9     -2
1     -3     -7
2      0      6
3     -1      8
4      5     -5

Clips per column using lower and upper thresholds:

>>> df.clip(-4, 6)
   col_0  col_1
    6     -2
   -3     -4
    0      6
   -1      6
    5     -4

Clips using specific lower and upper thresholds per column:

>>> df.clip([-2, -1], [4, 5])
    col_0  col_1
    4     -1
   -2     -1
    0      5
   -1      5
    4     -1

Clips using specific lower and upper thresholds per column element:

>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0    2
1   -4
2   -1
3    6
4    3
dtype: int64

>>> df.clip(t, t + 4, axis=0)
   col_0  col_1
    6      2
   -3     -4
    0      3
    6      8
    5      3

Clips using specific lower threshold per column element, with missing values:

>>> t = pd.Series([2, -4, np.nan, 6, 3])
>>> t
0    2.0
1   -4.0
2    NaN
3    6.0
4    3.0
dtype: float64

>>> df.clip(t, axis=0)
col_0  col_1
    9      2
   -3     -4
    0      6
    6      8
    5      3

sparkle.FeatureDataFrame.combine(self, other: DataFrame, func: Callable[[Series, Series], Series | Hashable], fill_value=None, overwrite: bool = True) → DataFrame

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters

otherDataFrame: The DataFrame to merge column-wise.
funcfunction: Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
fill_valuescalar value, default None: The value to fill NaNs with prior to passing any column to the merge func.
overwritebool, default True: If True, columns in self that do not exist in other will be overwritten with NaNs.

Returns

DataFrame: Combination of the provided DataFrames.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
   A    B
0  0 -5.0
1  0  4.0

However, if the same element in both dataframes is None, that None is preserved

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
    A    B
0  0 -5.0
1  0  3.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0

>>> df1.combine(df2, take_smaller, overwrite=False)
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
   A    B   C
0  0.0  NaN NaN
1  0.0  3.0 NaN
2  NaN  3.0 NaN

>>> df2.combine(df1, take_smaller, overwrite=False)
     A    B   C
0  0.0  NaN NaN
1  0.0  3.0 1.0
2  NaN  3.0 1.0

sparkle.FeatureDataFrame.combine_first(self, other: DataFrame) → DataFrame

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the ‘first’ dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).

Parameters

otherDataFrame: Provided DataFrame to use to fill null values.

Returns

DataFrame: The result of combining the provided DataFrame with the other object.

Examples

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0

sparkle.FeatureDataFrame.compare(self, other: DataFrame, align_axis: Axis = 1, keep_shape: bool = False, keep_equal: bool = False, result_names: Suffixes = ('self', 'other')) → DataFrame

Compare to another DataFrame and show the differences.

Parameters

otherDataFrame

Object to compare with.

align_axis{0 or ‘index’, 1 or ‘columns’}, default 1

Determine which axis to align the comparison on.

0, or ‘index’Resulting differences are stacked vertically
with rows drawn alternately from self and other.
1, or ‘columns’Resulting differences are aligned horizontally
with columns drawn alternately from self and other.

keep_shapebool, default False

If true, all rows and columns are kept. Otherwise, only the ones with different values are kept.

keep_equalbool, default False

If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.

result_namestuple, default (‘self’, ‘other’)

Set the dataframes names in the comparison.

Added in version 1.5.0.

Returns

DataFrame

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level.

Raises

ValueError: When the two DataFrames don’t have identical labels or shape.

Notes

Matching NaNs will not appear as a difference.

Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames

Examples

>>> df = pd.DataFrame(
...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0

>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Align the differences on columns

>>> df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Assign result_names

>>> df.compare(df2, result_names=("left", "right"))
  col1       col3
  left right left right
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0

sparkle.FeatureDataFrame.convert_dtypes(self, infer_objects: bool = True, convert_string: bool = True, convert_integer: bool = True, convert_boolean: bool = True, convert_floating: bool = True, dtype_backend: Literal['pyarrow', 'numpy_nullable'] = 'numpy_nullable') → None

Convert columns to the best possible dtypes using dtypes supporting pd.NA.

Parameters

infer_objectsbool, default True

Whether object dtypes should be converted to the best possible types.

convert_stringbool, default True

Whether object dtypes should be converted to StringDtype().

convert_integerbool, default True

Whether, if possible, conversion can be done to integer extension types.

convert_booleanbool, defaults True

Whether object dtypes should be converted to BooleanDtypes().

convert_floatingbool, defaults True

Whether, if possible, conversion can be done to floating extension types. If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultant DataFrame (still experimental). Behaviour is as follows:

"numpy_nullable": returns nullable-dtype-backed DataFrame (default).
"pyarrow": returns pyarrow-backed nullable ArrowDtype DataFrame.

Added in version 2.0.

Returns

Series or DataFrame: Copy of input object with new dtype.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_floating, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples

>>> df = pd.DataFrame(
...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0

>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10   <NA>
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0

>>> dfn.dtypes
a             Int32
b    string[python]
c           boolean
d    string[python]
e             Int64
f           Float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: object

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string

sparkle.FeatureDataFrame.copy(self, deep: bool | None = True) → None

Make a copy of this object’s indices and data.

When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below).

When deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa).

Note

The deep=False behaviour as described above will change in pandas 3.0. Copy-on-Write will be enabled by default, which means that the “shallow” copy is that is returned with deep=False will still avoid making an eager copy, but changes to the data of the original will no longer be reflected in the shallow copy (or vice versa). Instead, it makes use of a lazy (deferred) copy mechanism that will copy the data only when any changes to the original or shallow copy is made.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Parameters

deepbool, default True: Make a deep copy, including a copy of the data and the indices. With deep=False neither the indices nor the data are copied.

Returns

Series or DataFrame: Object type matches caller.

Notes

When deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below).

While Index objects are copied when deep=True, the underlying numpy array is not copied for performance reasons. Since Index is immutable, the underlying data can be safely shared and a copy is not needed.

Since pandas is not thread safe, see the gotchas when copying in a threading environment.

When copy_on_write in pandas config is set to True, the copy_on_write config takes effect even when deep=False. This means that any changes to the copied data would make a new copy of the data upon write (and vice versa). Changes made to either the original or copied variable would not be reflected in the counterpart. See Copy_on_Write for more information.

Examples

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: int64

>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: int64

Shallow copy versus default (deep) copy:

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)

Shallow copy shares data and index with original.

>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True

Deep copy has own copy of data and index.

>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False

Updates to the data shared by shallow copy and original is reflected in both (NOTE: this will no longer be true for pandas >= 3.0); deep copy remains unchanged.

>>> s.iloc[0] = 3
>>> shallow.iloc[1] = 4
>>> s
a    3
b    4
dtype: int64
>>> shallow
a    3
b    4
dtype: int64
>>> deep
a    1
b    2
dtype: int64

Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy.

>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0    [10, 2]
1     [3, 4]
dtype: object
>>> deep
0    [10, 2]
1     [3, 4]
dtype: object

Copy-on-Write is set to true, the shallow copy is not modified when the original data is changed:

>>> with pd.option_context("mode.copy_on_write", True):
...     s = pd.Series([1, 2], index=["a", "b"])
...     copy = s.copy(deep=False)
...     s.iloc[0] = 100
...     s
a    100
b      2
dtype: int64
>>> copy
a    1
b    2
dtype: int64

sparkle.FeatureDataFrame.corr(self, method: CorrelationMethod = 'pearson', min_periods: int = 1, numeric_only: bool = False) → DataFrame

Compute pairwise correlation of columns, excluding NA/null values.

Parameters

method{‘pearson’, ‘kendall’, ‘spearman’} or callable

Method of correlation:

pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

min_periodsint, optional

Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

numeric_onlybool, default False

Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns

DataFrame: Correlation matrix.

Notes

Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

Examples

>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0

>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
...                   columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)
      dogs  cats
dogs   1.0   NaN
cats   NaN   1.0

sparkle.FeatureDataFrame.corrwith(self, other: DataFrame | Series, axis: Axis = 0, drop: bool = False, method: CorrelationMethod = 'pearson', numeric_only: bool = False) → Series

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Parameters

otherDataFrame, Series

Object with which to compute correlations.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to use. 0 or ‘index’ to compute row-wise, 1 or ‘columns’ for column-wise.

dropbool, default False

Drop missing indices from result.

method{‘pearson’, ‘kendall’, ‘spearman’} or callable

Method of correlation:

pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
callable: callable with input two 1d ndarrays
and returning a float.

numeric_onlybool, default False

Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns

Series: Pairwise correlations.

Examples

>>> index = ["a", "b", "c", "d", "e"]
>>> columns = ["one", "two", "three", "four"]
>>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
>>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
>>> df1.corrwith(df2)
one      1.0
two      1.0
three    1.0
four     1.0
dtype: float64

>>> df2.corrwith(df1, axis=1)
a    1.0
b    1.0
c    1.0
d    1.0
e    NaN
dtype: float64

sparkle.FeatureDataFrame.count(self, axis: Axis = 0, numeric_only: bool = False)

Count non-NA cells for each column or row.

The values None, NaN, NaT, pandas.NA are considered NA.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
numeric_onlybool, default False: Include only float, int or boolean data.

Returns

Series: For each column/row the number of non-NA/null entries.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis='columns')
  3
  2
  3
  3
  3
dtype: int64

sparkle.FeatureDataFrame.cov(self, min_periods: int | None = None, ddof: int | None = 1, numeric_only: bool = False) → DataFrame

Compute pairwise covariance of columns, excluding NA/null values.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters

min_periodsint, optional: Minimum number of observations required per pair of columns to have a valid result.
ddofint, default 1: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. This argument is applicable only when no nan is in the dataframe.
numeric_onlybool, default False: Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns

DataFrame: The covariance matrix of the series of the DataFrame.

Notes

Returns the covariance matrix of the DataFrame’s time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices for more details.

Examples

>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
...                   columns=['dogs', 'cats'])
>>> df.cov()
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202

sparkle.FeatureDataFrame.cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative maximum of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
  2.0
  NaN
  5.0
  5.0
  5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0

sparkle.FeatureDataFrame.cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative minimum of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
  2.0
  NaN
  2.0
 -1.0
 -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

sparkle.FeatureDataFrame.cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative product of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
   2.0
   NaN
  10.0
 -10.0
  -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0

sparkle.FeatureDataFrame.cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative sum of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
  2.0
  NaN
  7.0
  6.0
  6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0

sparkle.FeatureDataFrame.describe(self, percentiles=None, include=None, exclude=None) → None

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters

percentileslist-like of numbers, optional

The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.

include‘all’, list-like of dtypes or None (default), optional

A white list of data types to include in the result. Ignored for Series. Here are the options:

‘all’ : All columns of the input will be included in the output.
A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
None (default) : The result will include all numeric columns.

excludelist-like of dtypes or None (default), optional,

A black list of data types to omit from the result. Ignored for Series. Here are the options:

A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(exclude=['O'])). To exclude pandas categorical columns, use 'category'
None (default) : The result will exclude nothing.

Returns

Series or DataFrame: Summary statistics of the Series or Dataframe provided.

Notes

For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...     np.datetime64("2000-01-01"),
...     np.datetime64("2010-01-01"),
...     np.datetime64("2010-01-01")
... ])
>>> s.describe()
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                    })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')  
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])  
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])
       categorical
count            3
unique           3
top              d
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])  
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])  
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0

sparkle.FeatureDataFrame.diff(self, periods: int = 1, axis: Axis = 0) → DataFrame

First discrete difference of element.

Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row).

Parameters

periodsint, default 1: Periods to shift for calculating difference, accepts negative values.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Take difference over rows (0) or columns (1).

Returns

DataFrame: First differences of the Series.

Notes

For boolean dtypes, this uses operator.xor() rather than operator.sub(). The result is calculated according to current dtype in DataFrame, however dtype of the result is always float64.

Examples

Difference with previous row

>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
...                    'b': [1, 1, 2, 3, 5, 8],
...                    'c': [1, 4, 9, 16, 25, 36]})
>>> df
   a  b   c
0  1  1   1
1  2  1   4
2  3  2   9
3  4  3  16
4  5  5  25
5  6  8  36

>>> df.diff()
     a    b     c
NaN  NaN   NaN
1.0  0.0   3.0
1.0  1.0   5.0
1.0  1.0   7.0
1.0  2.0   9.0
1.0  3.0  11.0

Difference with previous column

>>> df.diff(axis=1)
    a  b   c
NaN  0   0
NaN -1   3
NaN -1   7
NaN -1  13
NaN  0  20
NaN  2  28

Difference with 3rd previous row

>>> df.diff(periods=3)
     a    b     c
NaN  NaN   NaN
NaN  NaN   NaN
NaN  NaN   NaN
3.0  2.0  15.0
3.0  4.0  21.0
3.0  6.0  27.0

Difference with following row

>>> df.diff(periods=-1)
     a    b     c
-1.0  0.0  -3.0
-1.0 -1.0  -5.0
-1.0 -1.0  -7.0
-1.0 -2.0  -9.0
-1.0 -3.0 -11.0
NaN  NaN   NaN

Overflow in input dtype

>>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
>>> df.diff()
       a
0    NaN
1  255.0

sparkle.FeatureDataFrame.div(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.divide(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.dot(self, other: AnyArrayLike | DataFrame) → DataFrame | Series

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using self @ other.

Parameters

otherSeries, DataFrame or array-like: The other object to compute the matrix product with.

Returns

Series or DataFrame: If other is a Series, return the matrix product between self and other as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0    -4
1     5
dtype: int64

Here we multiply a DataFrame with another DataFrame.

>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
    0   1
0   1   4
1   2   2

Note that the dot method give the same result as @

>>> df @ other
 1
 1   4
 2   2

The dot method works also if other is an np.array.

>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
    0   1
0   1   4
1   2   2

Note how shuffling of the objects does not change the result.

>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0    -4
1     5
dtype: int64

sparkle.FeatureDataFrame.drop(self, labels: IndexLabel | None = None, *, axis: Axis = 0, index: IndexLabel | None = None, columns: IndexLabel | None = None, level: Level | None = None, inplace: bool = False, errors: IgnoreRaise = 'raise') → DataFrame | None

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by directly specifying index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide for more information about the now unused levels.

Parameters

labelssingle label or list-like: Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
indexsingle label or list-like: Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).
columnssingle label or list-like: Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).
levelint or level name, optional: For MultiIndex, level from which the labels will be removed.
inplacebool, default False: If False, return a copy. Otherwise, do operation in place and return None.
errors{‘ignore’, ‘raise’}, default ‘raise’: If ‘ignore’, suppress error and only existing labels are dropped.

Returns

DataFrame or None: Returns DataFrame or None DataFrame with the specified index or column labels removed or None if inplace=True.

Raises

KeyError: If any of the labels is not found in the selected axis.

Examples

>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2

Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination 'falcon' and 'weight', which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight'))
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        length  0.3     0.2

>>> df.drop(index='cow', columns='small')
                big
llama   speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3

>>> df.drop(index='length', level=1)
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8

sparkle.FeatureDataFrame.drop_duplicates(self, subset: Hashable | Sequence[Hashable] | None = None, *, keep: DropKeep = 'first', inplace: bool = False, ignore_index: bool = False) → DataFrame | None

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters

subsetcolumn label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the columns.

keep{‘first’, ‘last’, False}, default ‘first’

Determines which duplicates (if any) to keep.

‘first’ : Drop duplicates except for the first occurrence.
‘last’ : Drop duplicates except for the last occurrence.
False : Drop all duplicates.

inplacebool, default False

Whether to modify the DataFrame rather than creating a new one.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

Returns

DataFrame or None: DataFrame with duplicates removed or None if inplace=True.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0

sparkle.FeatureDataFrame.droplevel(self, level: Hashable | Sequence[Hashable], axis: int | Literal['index', 'columns', 'rows'] = 0) → None

Return Series/DataFrame with requested index / column level(s) removed.

Parameters

levelint, str, or list-like

If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Axis along which the level(s) is removed:

0 or ‘index’: remove level(s) in column.
1 or ‘columns’: remove level(s) in row.

For Series this parameter is unused and defaults to 0.

Returns

Series/DataFrame: Series/DataFrame with requested index / column level(s) removed.

Examples

>>> df = pd.DataFrame([
...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])

>>> df.columns = pd.MultiIndex.from_tuples([
...     ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])

>>> df
level_1   c   d
level_2   e   f
a b
1 2      3   4
5 6      7   8
9 10    11  12

>>> df.droplevel('a')
level_1   c   d
level_2   e   f
b
2        3   4
6        7   8
10      11  12

>>> df.droplevel('level_2', axis=1)
level_1   c   d
a b
1 2      3   4
5 6      7   8
9 10    11  12

sparkle.FeatureDataFrame.dropna(self, *, axis: Axis = 0, how: AnyAll | lib.NoDefault = <no_default>, thresh: int | lib.NoDefault = <no_default>, subset: IndexLabel | None = None, inplace: bool = False, ignore_index: bool = False) → DataFrame | None

Remove missing values.

See the User Guide for more on which values are considered missing, and how to work with missing data.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Determine if rows or columns which contain missing values are removed.

0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.

Only a single axis is allowed.

how{‘any’, ‘all’}, default ‘any’

Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

‘any’ : If any NA values are present, drop that row or column.
‘all’ : If all values are NA, drop that row or column.

threshint, optional

Require that many non-NA values. Cannot be combined with how.

subsetcolumn label or sequence of labels, optional

Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.

inplacebool, default False

Whether to modify the DataFrame rather than creating a new one.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

Added in version 2.0.0.

Returns

DataFrame or None: DataFrame with NA entries dropped from it or None if inplace=True.

Examples

>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

sparkle.FeatureDataFrame.duplicated(self, subset: Hashable | Sequence[Hashable] | None = None, keep: DropKeep = 'first') → Series

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters

subsetcolumn label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the columns.

keep{‘first’, ‘last’, False}, default ‘first’

Determines which duplicates (if any) to mark.

first : Mark duplicates as True except for the first occurrence.
last : Mark duplicates as True except for the last occurrence.
False : Mark all duplicates as True.

Returns

Series: Boolean series for each duplicated rows.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, for each set of duplicated values, the first occurrence is set on False and all others on True.

>>> df.duplicated()
  False
   True
  False
  False
  False
dtype: bool

By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True.

>>> df.duplicated(keep='last')
   True
  False
  False
  False
  False
dtype: bool

By setting keep on False, all duplicates are True.

>>> df.duplicated(keep=False)
   True
   True
  False
  False
  False
dtype: bool

To find duplicates on specific column(s), use subset.

>>> df.duplicated(subset=['brand'])
  False
   True
  False
   True
   True
dtype: bool

sparkle.FeatureDataFrame.eq(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Equal to of dataframe and other, element-wise (binary operator eq).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.FeatureDataFrame.equals(self, other: object) → bool

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns and index must be of the same dtype.

Parameters

otherSeries or DataFrame: The other Series or DataFrame to be compared with the first.

Returns

bool: True if all elements are the same in both objects, False otherwise.

Examples

>>> df = pd.DataFrame({1: [10], 2: [20]})
>>> df
    1   2
0  10  20

DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.

>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.

>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
      1     2
0  10.0  20.0
>>> df.equals(different_data_type)
False

sparkle.FeatureDataFrame.eval(self, expr: str, *, inplace: bool = False, **kwargs) → Any | None

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters

exprstr: The expression string to evaluate.
inplacebool, default False: If the expression contains an assignment, whether to perform the operation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
**kwargs: See the documentation for eval() for complete details on the keyword arguments accepted by query().

Returns

ndarray, scalar, pandas object, or None: The result of the evaluation or None if inplace=True.

Notes

For more details see the API documentation for eval(). For detailed examples see enhancing performance with eval.

Examples

>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')
   A   B   C
1  10  11
2   8  10
3   6   9
4   4   8
5   2   7
>>> df
   A   B
1  10
2   8
3   6
4   4
5   2

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3

Provide exponentially weighted (EW) calculations.

Exactly one of com, span, halflife, or alpha must be provided if times is not provided. If times is provided, halflife and one of com, span or alpha may be provided.

Parameters

comfloat, optional

Specify decay in terms of center of mass

\(\alpha = 1 / (1 + com)\), for \(com \geq 0\).

spanfloat, optional

Specify decay in terms of span

\(\alpha = 2 / (span + 1)\), for \(span \geq 1\).

halflifefloat, str, timedelta, optional

Specify decay in terms of half-life

\(\alpha = 1 - \exp\left(-\ln(2) / halflife\right)\), for \(halflife > 0\).

If times is specified, a timedelta convertible unit over which an observation decays to half its value. Only applicable to mean(), and halflife value will not apply to the other functions.

alphafloat, optional

Specify smoothing factor \(\alpha\) directly

\(0 < \alpha \leq 1\).

min_periodsint, default 0

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

adjustbool, default True

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average).

When adjust=True (default), the EW function is calculated using weights \(w_i = (1 - \alpha)^i\). For example, the EW moving average of the series [\(x_0, x_1, ..., x_t\)] would be:

\[y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}\]

When adjust=False, the exponentially weighted function is calculated recursively:

\[\begin{split}\begin{split} y_0 &= x_0\\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}\end{split}\]

ignore_nabool, default False

Ignore missing values when calculating weights.

When ignore_na=False (default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-\alpha)^2\) and \(1\) if adjust=True, and \((1-\alpha)^2\) and \(\alpha\) if adjust=False.
When ignore_na=True, weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-\alpha\) and \(1\) if adjust=True, and \(1-\alpha\) and \(\alpha\) if adjust=False.

axis{0, 1}, default 0

If 0 or 'index', calculate across the rows.

If 1 or 'columns', calculate across the columns.

For Series this parameter is unused and defaults to 0.

times : np.ndarray, Series, default None

Only applicable to mean().

Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype.

If 1-D array like, a sequence with the same shape as the observations.

methodstr {‘single’, ‘table’}, default ‘single’

Added in version 1.4.0.

Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Only applicable to mean()

Returns

pandas.api.typing.ExponentialMovingWindow

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

>>> df.ewm(com=0.5).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213
>>> df.ewm(alpha=2 / 3).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213

adjust

>>> df.ewm(com=0.5, adjust=True).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213
>>> df.ewm(com=0.5, adjust=False).mean()
          B
0.000000
0.666667
1.555556
1.555556
3.650794

ignore_na

>>> df.ewm(com=0.5, ignore_na=True).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.225000
>>> df.ewm(com=0.5, ignore_na=False).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213

times

Exponentially weighted mean with weights calculated with a timedelta halflife relative to times.

>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
          B
0  0.000000
1  0.585786
2  1.523889
3  1.523889
4  3.233686

sparkle.FeatureDataFrame.expanding(self, min_periods: int = 1, axis: int | ~typing.Literal['index', 'columns', 'rows'] | ~typing.Literal[_NoDefault.no_default] = <no_default>, method: ~typing.Literal['single', 'table'] = 'single') → Expanding

Provide expanding window calculations.

Parameters

min_periodsint, default 1

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

axisint or str, default 0

If 0 or 'index', roll across the rows.

If 1 or 'columns', roll across the columns.

For Series this parameter is unused and defaults to 0.

methodstr {‘single’, ‘table’}, default ‘single’

Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Added in version 1.3.0.

Returns

pandas.api.typing.Expanding

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

min_periods

Expanding sum with 1 vs 3 observations needed to calculate a value.

>>> df.expanding(1).sum()
     B
0.0
1.0
3.0
3.0
7.0
>>> df.expanding(3).sum()
     B
NaN
NaN
3.0
3.0
7.0

sparkle.FeatureDataFrame.explode(self, column: IndexLabel, ignore_index: bool = False) → DataFrame

Transform each element of a list-like to a row, replicating index values.

Parameters

columnIndexLabel: Column(s) to explode. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.

Added in version 1.3.0: Multi-column explode
ignore_indexbool, default False: If True, the resulting index will be labeled 0, 1, …, n - 1.

Returns

DataFrame: Exploded lists to rows of the subset columns; index will be duplicated for these rows.

Raises

ValueError :

If columns of the frame are not unique.
If specified columns to explode is empty list.
If specified columns to explode have not matching count of elements rowwise in the frame.

Notes

This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
...                    'B': 1,
...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode('A')
     A  B          C
  0  1  [a, b, c]
  1  1  [a, b, c]
  2  1  [a, b, c]
foo  1        NaN
NaN  1         []
  3  1     [d, e]
  4  1     [d, e]

Multi-column explode.

>>> df.explode(list('AC'))
     A  B    C
  0  1    a
  1  1    b
  2  1    c
foo  1  NaN
NaN  1  NaN
  3  1    d
  4  1    e

Fill NA/NaN values by propagating the last valid observation to next valid.

Parameters

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

limit_area{None, ‘inside’, ‘outside’}, default None

If limit is specified, consecutive NaNs will be filled with this restriction.

None: No fill restriction.
‘inside’: Only fill NaNs surrounded by valid values (interpolate).
‘outside’: Only fill NaNs outside valid values (extrapolate).

Added in version 2.2.0.

downcastdict, default is None

A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Deprecated since version 2.2.0.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, np.nan],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

>>> df.ffill()
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  3.0  4.0 NaN  1.0
3  3.0  3.0 NaN  4.0

>>> ser = pd.Series([1, np.nan, 2, 3])
>>> ser.ffill()
0   1.0
1   1.0
2   2.0
3   3.0
dtype: float64

Fill NA/NaN values using the specified method.

Parameters

valuescalar, dict, Series, or DataFrame

Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.

method{‘backfill’, ‘bfill’, ‘ffill’, None}, default None

Method to use for filling holes in reindexed Series:

ffill: propagate last valid observation forward to next valid.
backfill / bfill: use next valid observation to fill gap.

Deprecated since version 2.1.0: Use ffill or bfill instead.

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

downcastdict, default is None

A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Deprecated since version 2.2.0.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, np.nan],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

Replace all NaN elements with 0s.

>>> df.fillna(0)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  0.0
3  0.0  3.0  0.0  4.0

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  2.0  1.0
2  0.0  1.0  2.0  3.0
3  0.0  3.0  2.0  4.0

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  NaN  1.0
2  NaN  1.0  NaN  3.0
3  NaN  3.0  NaN  4.0

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  NaN
3  0.0  3.0  0.0  4.0

Note that column D is not affected since it is not present in df2.

sparkle.FeatureDataFrame.filter(self, items=None, like: str | None = None, regex: str | None = None, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Subset the dataframe rows or columns according to the specified index labels.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Parameters

itemslist-like: Keep labels from axis which are in items.
likestr: Keep labels from axis for which “like in label == True”.
regexstr (regular expression): Keep labels from axis for which re.search(regex, label) == True.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, ‘columns’ for DataFrame. For Series this parameter is unused and defaults to None.

Returns

same type as input object

Notes

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

Examples

>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])
>>> df
        one  two  three
mouse     1    2      3
rabbit    4    5      6

>>> # select columns by name
>>> df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6

>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
         one  three
mouse     1      3
rabbit    4      6

>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6

sparkle.FeatureDataFrame.first(self, offset) → None

Select initial periods of time series data based on a date offset.

Deprecated since version 2.1: first() is deprecated and will be removed in a future version. Please create a mask and filter using .loc instead.

For a DataFrame with a sorted DatetimeIndex, this function can select the first few rows based on a date offset.

Parameters

offsetstr, DateOffset or dateutil.relativedelta: The offset length of the data that will be selected. For instance, ‘1ME’ will display all the rows having their index within the first month.

Returns

Series or DataFrame: A subset of the caller.

Raises

TypeError: If the index is not a DatetimeIndex

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the first 3 days:

>>> ts.first('3D')
            A
2018-04-09  1
2018-04-11  2

Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.

sparkle.FeatureDataFrame.first_valid_index(self) → Hashable | None

Return index for first non-NA value or None, if no non-NA value is found.

Returns

type of index

Examples

For Series:

>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2

>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If all elements in Series are NA/null, returns None.

>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If Series is empty, returns None.

For DataFrame:

>>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
>>> df
     A      B
0  NaN    NaN
1  NaN    3.0
2  2.0    4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2

>>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
>>> df
     A      B
0  None   None
1  None   None
2  None   None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If all elements in DataFrame are NA/null, returns None.

>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If DataFrame is empty, returns None.

sparkle.FeatureDataFrame.floordiv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Integer division of dataframe and other, element-wise (binary operator floordiv).

Equivalent to dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.ge(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.FeatureDataFrame.get(self, key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

Parameters

key : object

Returns

same type as items contained in object

Examples

>>> df = pd.DataFrame(
...     [
...         [24.3, 75.7, "high"],
...         [31, 87.8, "high"],
...         [22, 71.6, "medium"],
...         [35, 95, "medium"],
...     ],
...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
...     index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
... )

>>> df
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium

>>> df.get(["temp_celsius", "windspeed"])
            temp_celsius windspeed
2014-02-12          24.3      high
2014-02-13          31.0      high
2014-02-14          22.0    medium
2014-02-15          35.0    medium

>>> ser = df['windspeed']
>>> ser.get('2014-02-13')
'high'

If the key isn’t found, the default value will be used.

>>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
'default_value'

>>> ser.get('2014-02-10', '[unknown]')
'[unknown]'

sparkle.FeatureDataFrame.get_feature_groups(self: FeatureDataFrame, extractor: str | list[str] = None) → list[str]

Retrieve the feature groups in the dataframe.

Args:

extractor: Optional. If extractor(s) are given,: yields only feature groups of that extractor.

Returns:

A list of feature groups.

sparkle.FeatureDataFrame.get_instance(self: FeatureDataFrame, instance: str) → list[float]: Return the feature vector of an instance.

sparkle.FeatureDataFrame.get_value(self: FeatureDataFrame, instance: str, extractor: str, feature_group: str, feature_name: str) → None: Return a value in the dataframe.

sparkle.FeatureDataFrame.groupby(self, by=None, axis: Axis | lib.NoDefault = <no_default>, level: IndexLabel | None = None, as_index: bool = True, sort: bool = True, group_keys: bool = True, observed: bool | lib.NoDefault = <no_default>, dropna: bool = True) → DataFrameGroupBy

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters

bymapping, function, label, pd.Grouper or list of such: Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If a list or ndarray of length equal to the selected axis is passed (see the groupby user guide), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.

Deprecated since version 2.1.0: Will be removed and behave like axis=0 in a future version. For axis=1, do frame.T.groupby(...) instead.
levelint, level name, or sequence of such, default None: If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.
as_indexbool, default True: Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).
sortbool, default True: Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. If False, the groups will appear in the same order as they did in the original DataFrame. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).

Changed in version 2.0.0: Specifying sort=False with an ordered categorical grouper will no longer sort the values.
group_keysbool, default True: When calling apply and the by argument produces a like-indexed (i.e. a transform) result, add group keys to index to identify pieces. By default group keys are not included when the result’s index (and column) labels match the inputs, and are included otherwise.

Changed in version 1.5.0: Warns that group_keys will no longer be ignored when the result from apply is a like-indexed Series or DataFrame. Specify group_keys explicitly to include the group keys or not.

Changed in version 2.0.0: group_keys now defaults to True.
observedbool, default False: This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Deprecated since version 2.1.0: The default value will change to True in a future version of pandas.
dropnabool, default True: If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

Returns

pandas.api.typing.DataFrameGroupBy: Returns a groupby object that contains information about the groups.

Notes

See the user guide for more detailed usage and examples, including splitting an object into groups, iterating through groups, selecting a group, aggregation, and more.

Examples

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True.

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5

>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4

>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0

>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0

When using .apply(), use group_keys to include or exclude the group keys. The group_keys argument defaults to True (include).

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
          Max Speed
Animal
Falcon 0      380.0
       1      370.0
Parrot 2       24.0
       3       26.0

>>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
   Max Speed
0      380.0
1      370.0
2       24.0
3       26.0

sparkle.FeatureDataFrame.gt(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than of dataframe and other, element-wise (binary operator gt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.FeatureDataFrame.has_missing_value(self: FeatureDataFrame) → bool: Return whether there are missing values in the feature data.

sparkle.FeatureDataFrame.has_missing_vectors(self: FeatureDataFrame) → bool: Returns True if there are any Extractors still to be run on any instance.

sparkle.FeatureDataFrame.head(self, n: int = 5) → None

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last |n| rows, equivalent to df[:n].

If n is larger than the number of rows, this function returns all rows.

Parameters

nint, default 5: Number of rows to select.

Returns

same type as caller: The first n rows of the caller object.

Examples

>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the first 5 lines

>>> df.head()
      animal
alligator
      bee
   falcon
     lion
   monkey

Viewing the first n lines (three in this case)

>>> df.head(3)
      animal
0  alligator
1        bee
2     falcon

For negative values of n

>>> df.head(-3)
      animal
alligator
      bee
   falcon
     lion
   monkey
   parrot

sparkle.FeatureDataFrame.hist(data: DataFrame, column: IndexLabel | None = None, by=None, grid: bool = True, xlabelsize: int | None = None, xrot: float | None = None, ylabelsize: int | None = None, yrot: float | None = None, ax=None, sharex: bool = False, sharey: bool = False, figsize: tuple[int, int] | None = None, layout: tuple[int, int] | None = None, bins: int | Sequence[int] = 10, backend: str | None = None, legend: bool = False, **kwargs)

Make a histogram of the DataFrame’s columns.

A `histogram`_ is a representation of the distribution of data. This function calls matplotlib.pyplot.hist(), on each series in the DataFrame, resulting in one histogram per column.

Parameters

dataDataFrame: The pandas object holding the data.
columnstr or sequence, optional: If passed, will be used to limit data to a subset of columns.
byobject, optional: If passed, then used to form histograms for separate groups.
gridbool, default True: Whether to show axis grid lines.
xlabelsizeint, default None: If specified changes the x-axis label size.
xrotfloat, default None: Rotation of x axis labels. For example, a value of 90 displays the x labels rotated 90 degrees clockwise.
ylabelsizeint, default None: If specified changes the y-axis label size.
yrotfloat, default None: Rotation of y axis labels. For example, a value of 90 displays the y labels rotated 90 degrees clockwise.
axMatplotlib axes object, default None: The axes to plot the histogram on.
sharexbool, default True if ax is None else False: In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in. Note that passing in both an ax and sharex=True will alter all x axis labels for all subplots in a figure.
shareybool, default False: In case subplots=True, share y axis and set some y axis labels to invisible.
figsizetuple, optional: The size in inches of the figure to create. Uses the value in matplotlib.rcParams by default.
layouttuple, optional: Tuple of (rows, columns) for the layout of the histograms.
binsint or sequence, default 10: Number of histogram bins to be used. If an integer is given, bins + 1 bin edges are calculated and returned. If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, bins is returned unmodified.
backendstr, default None: Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.
legendbool, default False: Whether to show the legend.
**kwargs: All other plotting keyword arguments to be passed to matplotlib.pyplot.hist().

Returns

matplotlib.AxesSubplot or numpy.ndarray of them

Examples

This example draws a histogram based on the length and width of some animals, displayed in three bins

sparkle.FeatureDataFrame.idxmax(self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False) → Series

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

Added in version 1.5.0.

Returns

Series: Indexes of maxima along the specified axis.

Raises

ValueError

If the row/column is empty

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                     'co2_emissions': [37.2, 19.66, 1712]},
...                   index=['Pork', 'Wheat Products', 'Beef'])

>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object

sparkle.FeatureDataFrame.idxmin(self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False) → Series

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

Added in version 1.5.0.

Returns

Series: Indexes of minima along the specified axis.

Raises

ValueError

If the row/column is empty

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                     'co2_emissions': [37.2, 19.66, 1712]},
...                   index=['Pork', 'Wheat Products', 'Beef'])

>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object

sparkle.FeatureDataFrame.impute_missing_values(self: FeatureDataFrame) → None: Imputes all NaN values by taking the average feature value.

sparkle.FeatureDataFrame.infer_objects(self, copy: bool | None = None) → None

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

Parameters

copybool, default True: Whether to make a copy for non-object or non-inferable columns or Series.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

same type as input object

Examples

>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3

>>> df.dtypes
A    object
dtype: object

>>> df.infer_objects().dtypes
A    int64
dtype: object

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.

Parameters

verbosebool, optional

Whether to print the full summary. By default, the setting in pandas.options.display.max_info_columns is followed.

bufwritable buffer, defaults to sys.stdout

Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output.

max_colsint, optional

When to switch from the verbose to the truncated output. If the DataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.

memory_usagebool, str, optional

Specifies whether total memory usage of the DataFrame elements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting.

True always show memory usage. False never shows memory usage. A value of ‘deep’ is equivalent to “True with deep introspection”. Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources. See the Frequently Asked Questions for more details.

show_countsbool, optional

Whether to show the non-null counts. By default, this is shown only if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.

Returns

None: This method prints a summary of a DataFrame and returns None.

Examples

>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
...                   "float_col": float_values})
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  
...     f.write(s)
260

The memory_usage parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 22.9+ MB

>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 165.9 MB

sparkle.FeatureDataFrame.insert(self, loc: int, column: Hashable, value: Scalar | AnyArrayLike, allow_duplicates: bool | lib.NoDefault = <no_default>) → None

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

Parameters

locint: Insertion index. Must verify 0 <= loc <= len(columns).
columnstr, number, or hashable object: Label of the inserted column.
valueScalar, Series, or array-like: Content of the inserted column.
allow_duplicatesbool, optional, default lib.no_default: Allow duplicate column labels to be created.

Examples

>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
>>> df.insert(1, "newcol", [99, 99])
>>> df
   col1  newcol  col2
0     1      99     3
1     2      99     4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
   col1  col1  newcol  col2
0   100     1      99     3
1   100     2      99     4

Notice that pandas uses index alignment in case of value from type Series:

>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
   col0  col1  col1  newcol  col2
0   NaN   100     1      99     3
1   5.0   100     2      99     4

sparkle.FeatureDataFrame.interpolate(self, method: InterpolateOptions = 'linear', *, axis: Axis = 0, limit: int | None = None, inplace: bool_t = False, limit_direction: Literal['forward', 'backward', 'both'] | None = None, limit_area: Literal['inside', 'outside'] | None = None, downcast: Literal['infer'] | None | lib.NoDefault = <no_default>, **kwargs) → Self | None

Fill NaN values using an interpolation method.

Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex.

Parameters

methodstr, default ‘linear’

Interpolation technique to use. One of:

‘linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
‘time’: Works on daily and higher resolution data to interpolate given length of interval.
‘index’, ‘values’: use the actual numerical values of the index.
‘pad’: Fill in NaNs using existing values.
‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d, whereas ‘spline’ is passed to scipy.interpolate.UnivariateSpline. These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5). Note that, slinear method in Pandas refers to the Scipy first order spline instead of Pandas first order spline.
‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’, ‘cubicspline’: Wrappers around the SciPy interpolation methods of similar names. See Notes.
‘from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives.

axis{{0 or ‘index’, 1 or ‘columns’, None}}, default None

Axis to interpolate along. For Series this parameter is unused and defaults to 0.

limitint, optional

Maximum number of consecutive NaNs to fill. Must be greater than 0.

inplacebool, default False

Update the data in place if possible.

limit_direction{{‘forward’, ‘backward’, ‘both’}}, Optional

Consecutive NaNs will be filled in this direction.

If limit is specified:

If ‘method’ is ‘pad’ or ‘ffill’, ‘limit_direction’ must be ‘forward’.
If ‘method’ is ‘backfill’ or ‘bfill’, ‘limit_direction’ must be ‘backwards’.

If ‘limit’ is not specified:

If ‘method’ is ‘backfill’ or ‘bfill’, the default is ‘backward’
else the default is ‘forward’

raises ValueError if limit_direction is ‘forward’ or ‘both’ and

method is ‘backfill’ or ‘bfill’.

raises ValueError if limit_direction is ‘backward’ or ‘both’ and

method is ‘pad’ or ‘ffill’.

limit_area{{None, ‘inside’, ‘outside’}}, default None

If limit is specified, consecutive NaNs will be filled with this restriction.

None: No fill restriction.
‘inside’: Only fill NaNs surrounded by valid values (interpolate).
‘outside’: Only fill NaNs outside valid values (extrapolate).

downcastoptional, ‘infer’ or None, defaults to None

Downcast dtypes if possible.

Deprecated since version 2.1.0.

**kwargsoptional

Keyword arguments to pass on to the interpolating function.

Returns

Series or DataFrame or None: Returns the same object type as the caller, interpolated at some or all NaN values or None if inplace=True.

Notes

The ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’ and ‘akima’ methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the SciPy documentation.

Examples

Filling in NaN in a Series via linear interpolation.

>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0    0.0
1    1.0
2    NaN
3    3.0
dtype: float64
>>> s.interpolate()
0    0.0
1    1.0
2    2.0
3    3.0
dtype: float64

Filling in NaN in a Series via polynomial interpolation or splines: Both ‘polynomial’ and ‘spline’ methods require that you also specify an order (int).

>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method='polynomial', order=2)
0    0.000000
1    2.000000
2    4.666667
3    8.000000
dtype: float64

Fill the DataFrame forward (that is, going down) along each column using linear interpolation.

Note how the last entry in column ‘a’ is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column ‘b’ remains NaN, because there is no entry before it to use for interpolation.

>>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
...                    (np.nan, 2.0, np.nan, np.nan),
...                    (2.0, 3.0, np.nan, 9.0),
...                    (np.nan, 4.0, -4.0, 16.0)],
...                   columns=list('abcd'))
>>> df
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  NaN  2.0  NaN   NaN
2  2.0  3.0  NaN   9.0
3  NaN  4.0 -4.0  16.0
>>> df.interpolate(method='linear', limit_direction='forward', axis=0)
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  1.0  2.0 -2.0   5.0
2  2.0  3.0 -3.0   9.0
3  2.0  4.0 -4.0  16.0

Using polynomial interpolation.

>>> df['d'].interpolate(method='polynomial', order=2)
0     1.0
1     4.0
2     9.0
3    16.0
Name: d, dtype: float64

sparkle.FeatureDataFrame.isetitem(self, loc, value) → None

Set the given value in the column with position loc.

This is a positional analogue to __setitem__.

Parameters

locint or sequence of ints: Index position for the column.
valuescalar or arraylike: Value(s) for the column.

Notes

frame.isetitem(loc, value) is an in-place method as it will modify the DataFrame in place (not returning a new object). In contrast to frame.iloc[:, i] = value which will try to update the existing values in place, frame.isetitem(loc, value) will not update the values of the column itself in place, it will instead insert a new array.

In cases where frame.columns is unique, this is equivalent to frame[frame.columns[i]] = value.

sparkle.FeatureDataFrame.isin(self, values: Series | DataFrame | Sequence | Mapping) → DataFrame

Whether each element in the DataFrame is contained in values.

Parameters

valuesiterable, Series, DataFrame or dict: The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.

Returns

DataFrame: DataFrame of booleans showing whether each element in the DataFrame is contained in values.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
...                   index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

To check if values is not in the DataFrame, use the ~ operator:

>>> ~df.isin([0, 2])
        num_legs  num_wings
falcon     False      False
dog         True      False

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in other.

>>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
...                      index=['spider', 'falcon'])
>>> df.isin(other)
        num_legs  num_wings
falcon     False       True
dog        False      False

sparkle.FeatureDataFrame.isna(self) → DataFrame

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

sparkle.FeatureDataFrame.isnull(self) → DataFrame

DataFrame.isnull is an alias for DataFrame.isna.

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

sparkle.FeatureDataFrame.items(self) → Iterable[tuple[Hashable, Series]]

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields

labelobject: The column names for the DataFrame being iterated over.
contentSeries: The column entries belonging to each label, as a Series.

Examples

>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64

sparkle.FeatureDataFrame.iterrows(self) → Iterable[tuple[Hashable, Series]]

Iterate over DataFrame rows as (index, Series) pairs.

Yields

indexlabel or tuple of label: The index of the row. A tuple for a MultiIndex.
dataSeries: The data of the row as a Series.

Notes

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames).

To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.
You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

Examples

>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

sparkle.FeatureDataFrame.itertuples(self, index: bool = True, name: str | None = 'Pandas') → Iterable[tuple[Any, ...]]

Iterate over DataFrame rows as namedtuples.

Parameters

indexbool, default True: If True, return the index as the first element of the tuple.
namestr or None, default “Pandas”: The name of the returned namedtuples or None to return regular tuples.

Returns

iterator: An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore.

Examples

>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
...                   index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):
...     print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name='Animal'):
...     print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)

sparkle.FeatureDataFrame.join(self, other: DataFrame | Series | Iterable[DataFrame | Series], on: IndexLabel | None = None, how: MergeHow = 'left', lsuffix: str = '', rsuffix: str = '', sort: bool = False, validate: JoinValidate | None = None) → DataFrame

Join columns of another DataFrame.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters

otherDataFrame, Series, or a list containing any combination of them

Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.

onstr, list of str, or array-like, optional

Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.

how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘left’

How to handle the operation of the two objects.

left: use calling frame’s index (or column if on is specified)
right: use other’s index.
outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it lexicographically.
inner: form intersection of calling frame’s index (or column if on is specified) with other’s index, preserving the order of the calling’s one.
cross: creates the cartesian product from both frames, preserves the order of the left keys.

lsuffixstr, default ‘’

Suffix to use from left frame’s overlapping columns.

rsuffixstr, default ‘’

Suffix to use from right frame’s overlapping columns.

sortbool, default False

Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).

validatestr, optional

If specified, checks if join is of specified type.

“one_to_one” or “1:1”: check if join keys are unique in both left and right datasets.
“one_to_many” or “1:m”: check if join keys are unique in left dataset.
“many_to_one” or “m:1”: check if join keys are unique in right dataset.
“many_to_many” or “m:m”: allowed, but does not result in checks.

Added in version 1.5.0.

Returns

DataFrame: A dataframe containing columns from both the caller and other.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Examples

>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> df
  key   A
K0  A0
K1  A1
K2  A2
K3  A3
K4  A4
K5  A5

>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})

>>> other
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix='_caller', rsuffix='_other')
  key_caller   A key_other    B
       K0  A0        K0   B0
       K1  A1        K1   B1
       K2  A2        K2   B2
       K3  A3       NaN  NaN
       K4  A4       NaN  NaN
       K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index('key').join(other.set_index('key'))
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result.

>>> df.join(other.set_index('key'), on='key')
  key   A    B
K0  A0   B0
K1  A1   B1
K2  A2   B2
K3  A3  NaN
K4  A4  NaN
K5  A5  NaN

Using non-unique key values shows how they are matched.

>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> df
  key   A
K0  A0
K1  A1
K1  A2
K3  A3
K0  A4
K1  A5

>>> df.join(other.set_index('key'), on='key', validate='m:1')
  key   A    B
K0  A0   B0
K1  A1   B1
K1  A2   B1
K3  A3  NaN
K0  A4   B0
K1  A5   B1

sparkle.FeatureDataFrame.keys(self) → Index

Get the ‘info axis’ (see Indexing for more).

This is index for Series, columns for DataFrame.

Returns

Index: Info axis.

Examples

>>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]},
...                  index=['a', 'b', 'c'])
>>> d
   A  B
a  1  0
b  2  4
c  3  8
>>> d.keys()
Index(['A', 'B'], dtype='object')

sparkle.FeatureDataFrame.kurt(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
>>> s
cat    1
dog    2
dog    2
mouse  3
dtype: int64
>>> s.kurt()
1.5

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
...                   index=['cat', 'dog', 'dog', 'mouse'])
>>> df
       a   b
  cat  1   3
  dog  2   4
  dog  2   4
mouse  3   4
>>> df.kurt()
a   1.5
b   4.0
dtype: float64

With axis=None

>>> df.kurt(axis=None).round(6)
-0.988693

Using axis=1

>>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
...                   index=['cat', 'dog'])
>>> df.kurt(axis=1)
cat   -6.0
dog   -6.0
dtype: float64

sparkle.FeatureDataFrame.kurtosis(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
>>> s
cat    1
dog    2
dog    2
mouse  3
dtype: int64
>>> s.kurt()
1.5

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
...                   index=['cat', 'dog', 'dog', 'mouse'])
>>> df
       a   b
  cat  1   3
  dog  2   4
  dog  2   4
mouse  3   4
>>> df.kurt()
a   1.5
b   4.0
dtype: float64

With axis=None

>>> df.kurt(axis=None).round(6)
-0.988693

Using axis=1

>>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
...                   index=['cat', 'dog'])
>>> df.kurt(axis=1)
cat   -6.0
dog   -6.0
dtype: float64

sparkle.FeatureDataFrame.last(self, offset) → None

Select final periods of time series data based on a date offset.

Deprecated since version 2.1: last() is deprecated and will be removed in a future version. Please create a mask and filter using .loc instead.

For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.

Parameters

offsetstr, DateOffset, dateutil.relativedelta: The offset length of the data that will be selected. For instance, ‘3D’ will display all the rows having their index within the last 3 days.

Returns

Series or DataFrame: A subset of the caller.

Raises

TypeError: If the index is not a DatetimeIndex

Notes

Deprecated since version 2.1.0: Please create a mask and filter using .loc instead

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the last 3 days:

>>> ts.last('3D')  
            A
2018-04-13  3
2018-04-15  4

Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.

sparkle.FeatureDataFrame.last_valid_index(self) → Hashable | None

Return index for last non-NA value or None, if no non-NA value is found.

Returns

type of index

Examples

For Series:

>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2

>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If all elements in Series are NA/null, returns None.

>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If Series is empty, returns None.

For DataFrame:

>>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
>>> df
     A      B
0  NaN    NaN
1  NaN    3.0
2  2.0    4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2

>>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
>>> df
     A      B
0  None   None
1  None   None
2  None   None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If all elements in DataFrame are NA/null, returns None.

>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If DataFrame is empty, returns None.

sparkle.FeatureDataFrame.le(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Less than or equal to of dataframe and other, element-wise (binary operator le).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.FeatureDataFrame.lt(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Less than of dataframe and other, element-wise (binary operator lt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.FeatureDataFrame.map(self, func: PythonFuncType, na_action: str | None = None, **kwargs) → DataFrame

Apply a function to a Dataframe elementwise.

Added in version 2.1.0: DataFrame.applymap was deprecated and renamed to DataFrame.map.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters

funccallable: Python function, returns a single value from a single value.
na_action{None, ‘ignore’}, default None: If ‘ignore’, propagate NaN values, without passing them to func.
**kwargs: Additional keyword arguments to pass as keywords arguments to func.

Returns

DataFrame: Transformed DataFrame.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.map(lambda x: len(str(x)))
1
3  4
5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
     0  1
0  NaN  4
1  5.0  5

It is also possible to use map with functions that are not lambda functions:

>>> df.map(round, ndigits=1)
  1
1.0  2.1
3.4  4.6

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.map(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it’s better to avoid map in that case.

>>> df ** 2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

sparkle.FeatureDataFrame.mask(self, cond, other=<no_default>, *, inplace: bool_t = False, axis: Axis | None = None, level: Level | None = None) → Self | None

Replace values where the condition is True.

Parameters

condbool Series/DataFrame, array-like, or callable: Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
otherscalar, Series/DataFrame, or callable: Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).
inplacebool, default False: Whether to perform the operation in place on the data.
axisint, default None: Alignment axis if needed. For Series this parameter is unused and defaults to 0.
levelint, default None: Alignment level if needed.

Returns

Same type as caller or None if inplace=True.

Notes

The mask method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is False the element is used; otherwise the corresponding element from the DataFrame other is used. If the axis of other does not align with axis of cond Series/DataFrame, the misaligned index positions will be filled with True.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the mask documentation in indexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)
0    99
1     1
2    99
3    99
4    99
dtype: int64

>>> s.where(s > 1, 10)
  10
  10
  2
  3
  4
dtype: int64
>>> s.mask(s > 1, 10)
   0
   1
  10
  10
  10
dtype: int64

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

sparkle.FeatureDataFrame.max(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.max()
8

sparkle.FeatureDataFrame.mean(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return the mean of the values over the requested axis.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 3])
>>> s.mean()
2.0

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.mean()
a   1.5
b   2.5
dtype: float64

Using axis=1

>>> df.mean(axis=1)
tiger   1.5
zebra   2.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
...                   index=['tiger', 'zebra'])
>>> df.mean(numeric_only=True)
a   1.5
dtype: float64

sparkle.FeatureDataFrame.median(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return the median of the values over the requested axis.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 3])
>>> s.median()
2.0

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.median()
a   1.5
b   2.5
dtype: float64

Using axis=1

>>> df.median(axis=1)
tiger   1.5
zebra   2.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
...                   index=['tiger', 'zebra'])
>>> df.median(numeric_only=True)
a   1.5
dtype: float64

sparkle.FeatureDataFrame.melt(self, id_vars=None, value_vars=None, var_name=None, value_name: Hashable = 'value', col_level: Level | None = None, ignore_index: bool = True) → DataFrame

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters

id_varsscalar, tuple, list, or ndarray, optional: Column(s) to use as identifier variables.
value_varsscalar, tuple, list, or ndarray, optional: Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_namescalar, default None: Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_namescalar, default ‘value’: Name to use for the ‘value’ column, can’t be an existing column label.
col_levelscalar, optional: If columns are a MultiIndex then use this level to melt.
ignore_indexbool, default True: If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

Returns

DataFrame: Unpivoted DataFrame.

Notes

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
   A variable  value
a        B      1
b        B      3
c        B      5
a        C      2
b        C      4
c        C      6

The names of ‘variable’ and ‘value’ columns can be customized:

>>> df.melt(id_vars=['A'], value_vars=['B'],
...         var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

Original index values can be kept around:

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
   A variable  value
a        B      1
b        B      3
c        B      5
a        C      2
b        C      4
c        C      6

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]
>>> df
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5

sparkle.FeatureDataFrame.memory_usage(self, index: bool = True, deep: bool = False) → Series

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters

indexbool, default True: Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.
deepbool, default False: If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values.

Returns

Series: A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Notes

See the Frequently Asked Questions for more details.

Examples

>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
...              for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
   int64  float64            complex128  object  bool
0      1      1.0              1.0+0.0j       1  True
1      1      1.0              1.0+0.0j       1  True
2      1      1.0              1.0+0.0j       1  True
3      1      1.0              1.0+0.0j       1  True
4      1      1.0              1.0+0.0j       1  True

>>> df.memory_usage()
Index           128
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)
Index            128
int64          40000
float64        40000
complex128     80000
object        180000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['object'].astype('category').memory_usage(deep=True)
5244

sparkle.FeatureDataFrame.merge(self, right: DataFrame | Series, how: MergeHow = 'inner', on: IndexLabel | AnyArrayLike | None = None, left_on: IndexLabel | AnyArrayLike | None = None, right_on: IndexLabel | AnyArrayLike | None = None, left_index: bool = False, right_index: bool = False, sort: bool = False, suffixes: Suffixes = ('_x', '_y'), copy: bool | None = None, indicator: str | bool = False, validate: MergeValidate | None = None) → DataFrame

Merge DataFrame or named Series objects with a database-style join.

A named Series object is treated as a DataFrame with a single named column.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Warning

If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

Parameters

rightDataFrame or named Series

Object to merge with.

how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘inner’

Type of merge to be performed.

left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
cross: creates the cartesian product from both frames, preserves the order of the left keys.

onlabel or list

Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.

left_onlabel or list, or array-like

Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.

right_onlabel or list, or array-like

Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.

left_indexbool, default False

Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.

right_indexbool, default False

Use the index from the right DataFrame as the join key. Same caveats as left_index.

sortbool, default False

Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword).

suffixeslist-like, default is (“_x”, “_y”)

A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.

copybool, default True

If False, avoid copy if possible.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

indicatorbool or str, default False

If True, adds a column to the output DataFrame called “_merge” with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left DataFrame, “right_only” for observations whose merge key only appears in the right DataFrame, and “both” if the observation’s merge key is found in both DataFrames.

validatestr, optional

If specified, checks if merge is of specified type.

“one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
“one_to_many” or “1:m”: check if merge keys are unique in left dataset.
“many_to_one” or “m:1”: check if merge keys are unique in right dataset.
“many_to_many” or “m:m”: allowed, but does not result in checks.

Returns

DataFrame: A DataFrame of the two merged objects.

Examples

>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
>>> df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
foo        1  foo        5
foo        1  foo        8
bar        2  bar        6
baz        3  baz        7
foo        5  foo        5
foo        5  foo        8

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',
...           suffixes=('_left', '_right'))
  lkey  value_left rkey  value_right
0  foo           1  foo            5
1  foo           1  foo            8
2  bar           2  bar            6
3  baz           3  baz            7
4  foo           5  foo            5
5  foo           5  foo            8

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
    Index(['value'], dtype='object')

>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
      a  b
0   foo  1
1   bar  2
>>> df2
      a  c
0   foo  3
1   baz  4

>>> df1.merge(df2, how='inner', on='a')
      a  b  c
0   foo  1  3

>>> df1.merge(df2, how='left', on='a')
      a  b  c
0   foo  1  3.0
1   bar  2  NaN

>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
    left
0   foo
1   bar
>>> df2
    right
0   7
1   8

>>> df1.merge(df2, how='cross')
   left  right
0   foo      7
1   foo      8
2   bar      7
3   bar      8

sparkle.FeatureDataFrame.min(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.min()
0

sparkle.FeatureDataFrame.mod(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Modulo of dataframe and other, element-wise (binary operator mod).

Equivalent to dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmod.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.mode(self, axis: Axis = 0, numeric_only: bool = False, dropna: bool = True) → DataFrame

Get the mode(s) of each element along the selected axis.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to iterate over while searching for the mode:

0 or ‘index’ : get mode of each column
1 or ‘columns’ : get mode of each row.

numeric_onlybool, default False

If True, only apply to numeric columns.

dropnabool, default True

Don’t consider counts of NaN/NaT.

Returns

DataFrame: The modes of each column or row.

Examples

>>> df = pd.DataFrame([('bird', 2, 2),
...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN.

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis='columns', numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN

sparkle.FeatureDataFrame.mul(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.multiply(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.ne(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Not equal to of dataframe and other, element-wise (binary operator ne).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.FeatureDataFrame.nlargest(self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = 'first') → DataFrame

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters

nint

Number of rows to return.

columnslabel or list of labels

Column label(s) to order by.

keep{‘first’, ‘last’, ‘all’}, default ‘first’

Where there are duplicate values:

first : prioritize the first occurrence(s)
last : prioritize the last occurrence(s)
all : keep all the ties of the smallest item even if it means selecting more than n items.

Returns

DataFrame: The first n rows ordered by the given columns in descending order.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
...                                   434000, 434000, 337000, 11300,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column “population”.

>>> df.nlargest(3, 'population')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'population', keep='last')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', the number of element kept can go beyond n if there are duplicate values for the smallest element, all the ties are kept:

>>> df.nlargest(3, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

However, nlargest does not keep n distinct largest elements:

>>> df.nlargest(5, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['population', 'GDP'])
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

sparkle.FeatureDataFrame.notna(self) → DataFrame

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()
0     True
1     True
2    False
dtype: bool

sparkle.FeatureDataFrame.notnull(self) → DataFrame

DataFrame.notnull is an alias for DataFrame.notna.

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()
0     True
1     True
2    False
dtype: bool

sparkle.FeatureDataFrame.nsmallest(self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = 'first') → DataFrame

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters

nint

Number of items to retrieve.

columnslist or str

Column name or names to order by.

keep{‘first’, ‘last’, ‘all’}, default ‘first’

Where there are duplicate values:

first : take the first occurrence.
last : take the last occurrence.
all : keep all the ties of the largest item even if it means selecting more than n items.

Returns

DataFrame

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
...                                   434000, 434000, 337000, 337000,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column “population”.

>>> df.nsmallest(3, 'population')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, 'population', keep='last')
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', the number of element kept can go beyond n if there are duplicate values for the largest element, all the ties are kept.

>>> df.nsmallest(3, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

However, nsmallest does not keep n distinct smallest elements:

>>> df.nsmallest(4, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ['population', 'GDP'])
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR

sparkle.FeatureDataFrame.nunique(self, axis: Axis = 0, dropna: bool = True) → Series

Count number of distinct elements in specified axis.

Return Series with number of distinct elements. Can ignore NaN values.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
dropnabool, default True: Don’t include NaN in the counts.

Returns

Series

Examples

>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})
>>> df.nunique()
A    3
B    2
dtype: int64

>>> df.nunique(axis=1)
0    1
1    2
2    2
dtype: int64

Fill NA/NaN values by propagating the last valid observation to next valid.

Deprecated since version 2.0: Series/DataFrame.pad is deprecated. Use Series/DataFrame.ffill instead.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

Please see examples for DataFrame.ffill() or Series.ffill().

sparkle.FeatureDataFrame.pct_change(self, periods: int = 1, fill_method: ~typing.Literal['backfill', 'bfill', 'ffill', 'pad'] | None | ~typing.Literal[_NoDefault.no_default] = <no_default>, limit: int | None | ~typing.Literal[_NoDefault.no_default] = <no_default>, freq=None, **kwargs) → None

Fractional change between the current and a prior element.

Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.

Note

Despite the name of this method, it calculates fractional change (also known as per unit change or relative change) and not percentage change. If you need the percentage change, multiply these values by 100.

Parameters

periodsint, default 1: Periods to shift for forming percent change.
fill_method{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default ‘pad’: How to handle NAs before computing percent changes.

Deprecated since version 2.1: All options of fill_method are deprecated except fill_method=None.
limitint, default None: The number of consecutive NAs to fill before stopping.

Deprecated since version 2.1.
freqDateOffset, timedelta, or str, optional: Increment to use from time series API (e.g. ‘ME’ or BDay()).
**kwargs: Additional keyword arguments are passed into DataFrame.shift or Series.shift.

Returns

Series or DataFrame: The same type as the calling object.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64

>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64

>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64

>>> s.ffill().pct_change()
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13

>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351

>>> df.pct_change(axis='columns', periods=-1)
          2016      2015  2014
GOOG  0.179241  0.094112   NaN
APPL -0.252395 -0.011860   NaN

sparkle.FeatureDataFrame.pipe(self, func: Callable[[...], T] | tuple[Callable[[...], T], str], *args, **kwargs) → T

Apply chainable functions that expect Series or DataFrames.

Parameters

funcfunction: Function to apply to the Series/DataFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
*argsiterable, optional: Positional arguments passed into func.
**kwargsmapping, optional: A dictionary of keyword arguments passed into func.

Returns

the return type of func.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects.

Examples

Constructing a income DataFrame from a dictionary.

>>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
>>> df = pd.DataFrame(data, columns=['Salary', 'Others'])
>>> df
   Salary  Others
0    8000  1000.0
1    9500     NaN
2    5000  2000.0

Functions that perform tax reductions on an income DataFrame.

>>> def subtract_federal_tax(df):
...     return df * 0.9
>>> def subtract_state_tax(df, rate):
...     return df * (1 - rate)
>>> def subtract_national_insurance(df, rate, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)

Instead of writing

>>> subtract_national_insurance(
...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
...     rate=0.05,
...     rate_increase=0.02)  

You can write

>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose national_insurance takes its data as df in the second argument:

>>> def subtract_national_insurance(rate, df, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)
>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(
...         (subtract_national_insurance, 'df'),
...         rate=0.05,
...         rate_increase=0.02
...     )
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12

sparkle.FeatureDataFrame.pivot(self, *, columns, index=<no_default>, values=<no_default>) → DataFrame

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a “pivot” table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the User Guide for more on reshaping.

Parameters

columnsstr or object or a list of str: Column to use to make new frame’s columns.
indexstr or object or a list of str, optional: Column to use to make new frame’s index. If not given, uses existing index.
valuesstr, object or a list of the previous, optional: Column(s) to use for populating new frame’s values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.

Returns

DataFrame: Returns reshaped DataFrame.

Raises

ValueError:: When there are any index, columns combinations with multiple values. DataFrame.pivot_table when you need to aggregate.

Notes

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
...                            'two'],
...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
...                    'baz': [1, 2, 3, 4, 5, 6],
...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
    foo   bar  baz  zoo
0   one   A    1    x
1   one   B    2    y
2   one   C    3    z
3   two   A    4    q
4   two   B    5    w
5   two   C    6    t

>>> df.pivot(index='foo', columns='bar', values='baz')
bar  A   B   C
foo
one  1   2   3
two  4   5   6

>>> df.pivot(index='foo', columns='bar')['baz']
bar  A   B   C
foo
one  1   2   3
two  4   5   6

>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>> df = pd.DataFrame({
...        "lev1": [1, 1, 1, 2, 2, 2],
...        "lev2": [1, 1, 2, 1, 1, 2],
...        "lev3": [1, 2, 1, 2, 1, 2],
...        "lev4": [1, 2, 3, 4, 5, 6],
...        "values": [0, 1, 2, 3, 4, 5]})
>>> df
    lev1 lev2 lev3 lev4 values
0   1    1    1    1    0
1   1    1    2    2    1
2   1    2    1    3    2
3   2    1    2    4    3
4   2    1    1    5    4
5   2    2    2    6    5

>>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
lev2    1         2
lev3    1    2    1    2
lev1
1     0.0  1.0  2.0  NaN
2     4.0  3.0  NaN  5.0

>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
      lev3    1    2
lev1  lev2
   1     1  0.0  1.0
         2  2.0  NaN
   2     1  4.0  3.0
         2  NaN  5.0

A ValueError is raised if there are any duplicates.

>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
...                    "bar": ['A', 'A', 'B', 'C'],
...                    "baz": [1, 2, 3, 4]})
>>> df
   foo bar  baz
0  one   A    1
1  one   A    2
2  two   B    3
3  two   C    4

Notice that the first two rows are the same for our index and columns arguments.

>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
   ...
ValueError: Index contains duplicate entries, cannot reshape

sparkle.FeatureDataFrame.pivot_table(self, values=None, index=None, columns=None, aggfunc: AggFuncType = 'mean', fill_value=None, margins: bool = False, dropna: bool = True, margins_name: Level = 'All', observed: bool | lib.NoDefault = <no_default>, sort: bool = True) → DataFrame

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters

valueslist-like or scalar, optional: Column or columns to aggregate.
indexcolumn, Grouper, array, or list of the previous: Keys to group by on the pivot table index. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.
columnscolumn, Grouper, array, or list of the previous: Keys to group by on the pivot table column. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.
aggfuncfunction, list of functions, dict, default “mean”: If a list of functions is passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves). If a dict is passed, the key is column to aggregate and the value is function or list of functions. If margin=True, aggfunc will be used to calculate the partial aggregates.
fill_valuescalar, default None: Value to replace missing values with (in the resulting pivot table, after aggregation).
marginsbool, default False: If margins=True, special All columns and rows will be added with partial group aggregates across the categories on the rows and columns.
dropnabool, default True: Do not include columns whose entries are all NaN. If True, rows with a NaN value in any column will be omitted before computing margins.
margins_namestr, default ‘All’: Name of the row / column that will contain the totals when margins is True.
observedbool, default False: This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Deprecated since version 2.2.0: The default value of False is deprecated and will change to True in a future version of pandas.
sortbool, default True: Specifies if the result should be sorted.

Added in version 1.3.0.

Returns

DataFrame: An Excel style pivot table.

Notes

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9

This first example aggregates values by taking the sum.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                        columns=['C'], aggfunc="sum")
>>> table
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

We can also fill missing values using the fill_value parameter.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                        columns=['C'], aggfunc="sum", fill_value=0)
>>> table
C        large  small
A   B
bar one      4      5
    two      7      6
foo one      4      1
    two      0      6

The next example aggregates by taking the mean across multiple columns.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                        aggfunc={'D': "mean", 'E': "mean"})
>>> table
                D         E
A   C
bar large  5.500000  7.500000
    small  5.500000  8.500000
foo large  2.000000  4.500000
    small  2.333333  4.333333

We can also calculate multiple types of aggregations for any given value column.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                        aggfunc={'D': "mean",
...                                 'E': ["min", "max", "mean"]})
>>> table
                  D   E
               mean max      mean  min
A   C
bar large  5.500000   9  7.500000    6
    small  5.500000   9  8.500000    8
foo large  2.000000   5  4.500000    4
    small  2.333333   6  4.333333    2

sparkle.FeatureDataFrame.pop(self, item: Hashable) → Series

Return item and drop from frame. Raise KeyError if not found.

Parameters

itemlabel: Label of column to be popped.

Returns

Series

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

>>> df.pop('class')
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object

>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN

sparkle.FeatureDataFrame.pow(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator pow).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.prod(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs)

Return the product of the values over the requested axis.

Parameters

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.prod with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0

>>> pd.Series([np.nan]).prod(min_count=1)
nan

sparkle.FeatureDataFrame.product(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs)

Return the product of the values over the requested axis.

Parameters

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.prod with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0

>>> pd.Series([np.nan]).prod(min_count=1)
nan

sparkle.FeatureDataFrame.quantile(self, q: float | AnyArrayLike | Sequence[float] = 0.5, axis: Axis = 0, numeric_only: bool = False, interpolation: QuantileInterpolation = 'linear', method: Literal['single', 'table'] = 'single') → Series | DataFrame

Return values at the given quantile over requested axis.

Parameters

qfloat or array-like, default 0.5 (50% quantile)

Value between 0 <= q <= 1, the quantile(s) to compute.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

numeric_onlybool, default False

Include only float, int or boolean data.

Changed in version 2.0.0: The default value of numeric_only is now False.

interpolation{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:

linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
lower: i.
higher: j.
nearest: i or j whichever is nearest.
midpoint: (i + j) / 2.

method{‘single’, ‘table’}, default ‘single’

Whether to compute quantiles per-column (‘single’) or over all columns (‘table’). When ‘table’, the only allowed interpolation methods are ‘nearest’, ‘lower’, and ‘higher’.

Returns

Series or DataFrame

If q is an array, a DataFrame will be returned where the
index is q, the columns are the columns of self, and the values are the quantiles.

If q is a float, a Series will be returned where the
index is the columns of self and the values are the quantiles.

Examples

>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
...                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying method=’table’ will compute the quantile over all columns.

>>> df.quantile(.1, method="table", interpolation="nearest")
a    1
b    1
Name: 0.1, dtype: int64
>>> df.quantile([.1, .5], method="table", interpolation="nearest")
     a    b
0.1  1    1
0.5  3  100

Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

>>> df = pd.DataFrame({'A': [1, 2],
...                    'B': [pd.Timestamp('2010'),
...                          pd.Timestamp('2011')],
...                    'C': [pd.Timedelta('1 days'),
...                          pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object

sparkle.FeatureDataFrame.query(self, expr: str, *, inplace: bool = False, **kwargs) → DataFrame | None

Query the columns of a DataFrame with a boolean expression.

Parameters

exprstr

The query string to evaluate.

You can refer to variables in the environment by prefixing them with an ‘@’ character like @a + b.

You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named “Area (cm^2)” would be referenced as `Area (cm^2)`). Column names which are Python keywords (like “list”, “for”, “import”, etc) cannot be used.

For example, if one of your columns is called a a and you want to sum it with b, your query should be `a a` + b.

inplacebool

Whether to modify the DataFrame rather than creating a new one.

**kwargs

See the documentation for eval() for complete details on the keyword arguments accepted by DataFrame.query().

Returns

DataFrame or None: DataFrame resulting from the provided query expression or None if inplace=True.

Notes

The result of the evaluation of this expression is first passed to DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to DataFrame.__getitem__().

This method uses the top-level eval() function to evaluate the passed query.

The query() method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, and and or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The DataFrame.index and DataFrame.columns attributes of the DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.

For further details and examples see the query documentation in indexing.

Backtick quoted variables

Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.

During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped).

In a special case, quotes that make a pair around a backtick can confuse the parser. For example, `it's` > `that's` will raise an error, as it forms a quoted string ('s > `that') with a backtick inside.

See also the Python documentation about lexical analysis (https://docs.python.org/3/reference/lexical_analysis.html) in combination with the source code in pandas.core.computation.parsing.

Examples

>>> df = pd.DataFrame({'A': range(1, 6),
...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
   A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.query('A > B')
   A  B  C C
4  5  2    6

The previous expression is equivalent to

>>> df[df.A > df.B]
   A  B  C C
4  5  2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
   A   B  C C
0  1  10   10

The previous expression is equivalent to

>>> df[df.B == df['C C']]
   A   B  C C
0  1  10   10

sparkle.FeatureDataFrame.radd(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Addition of dataframe and other, element-wise (binary operator radd).

Equivalent to other + dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, add.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.rank(self, axis: int | Literal['index', 'columns', 'rows'] = 0, method: Literal['average', 'min', 'max', 'first', 'dense'] = 'average', numeric_only: bool = False, na_option: Literal['keep', 'top', 'bottom'] = 'keep', ascending: bool = True, pct: bool = False) → None

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Index to direct ranking. For Series this parameter is unused and defaults to 0.

method{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’

How to rank the group of records that have the same value (i.e. ties):

average: average rank of the group
min: lowest rank in the group
max: highest rank in the group
first: ranks assigned in order they appear in the array
dense: like ‘min’, but rank always increases by 1 between groups.

numeric_onlybool, default False

For DataFrame objects, rank only numeric columns if set to True.

Changed in version 2.0.0: The default value of numeric_only is now False.

na_option{‘keep’, ‘top’, ‘bottom’}, default ‘keep’

How to rank NaN values:

keep: assign NaN rank to NaN values
top: assign lowest rank to NaN values
bottom: assign highest rank to NaN values

ascendingbool, default True

Whether or not the elements should be ranked in ascending order.

pctbool, default False

Whether or not to display the returned rankings in percentile form.

Returns

same type as caller: Return a Series or DataFrame with data ranks as values.

Examples

>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

Ties are assigned the mean of the ranks (by default) for the group.

>>> s = pd.Series(range(5), index=list("abcde"))
>>> s["d"] = s["b"]
>>> s.rank()
a    1.0
b    2.5
c    4.0
d    2.5
e    5.0
dtype: float64

The following example shows how the method behaves with the above parameters:

default_rank: this is the default behaviour obtained without using any parameter.
max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.)
NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.
pct_rank: when setting pct = True, the ranking is expressed as percentile rank.

>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN

sparkle.FeatureDataFrame.rdiv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.reindex(self, labels=None, *, index=None, columns=None, axis: Axis | None = None, method: ReindexMethod | None = None, copy: bool | None = None, level: Level | None = None, fill_value: Scalar | None = nan, limit: int | None = None, tolerance=None) → DataFrame

Conform DataFrame to new index with optional filling logic.

Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters

labelsarray-like, optional

New labels / index to conform the axis specified by ‘axis’ to.

indexarray-like, optional

New labels for the index. Preferably an Index object to avoid duplicating data.

columnsarray-like, optional

New labels for the columns. Preferably an Index object to avoid duplicating data.

axisint or str, optional

Axis to target. Can be either the axis name (‘index’, ‘columns’) or number (0, 1).

method{None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}

Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

None (default): don’t fill gaps
pad / ffill: Propagate last valid observation forward to next valid.
backfill / bfill: Use next valid observation to fill gap.
nearest: Use nearest valid observations to fill gap.

copybool, default True

Return a new object, even if the passed indexes are the same.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

levelint or name

Broadcast across a level, matching Index values on the passed MultiIndex level.

fill_valuescalar, default np.nan

Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

limitint, default None

Maximum number of consecutive elements to forward or backward fill.

toleranceoptional

Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns

DataFrame with changed index.

Examples

DataFrame.reindex supports two calling conventions

(index=index_labels, columns=column_labels, ...)
(labels, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Create a dataframe with some fictional data.

>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301],
...                   'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
...                   index=index)
>>> df
           http_status  response_time
Firefox            200           0.04
Chrome             200           0.02
Safari             404           0.07
IE10               404           0.08
Konqueror          301           1.00

Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned NaN.

>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...              'Chrome']
>>> df.reindex(new_index)
               http_status  response_time
Safari               404.0           0.07
Iceweasel              NaN            NaN
Comodo Dragon          NaN            NaN
IE10                 404.0           0.08
Chrome               200.0           0.02

We can fill in the missing values by passing a value to the keyword fill_value. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

>>> df.reindex(new_index, fill_value=0)
               http_status  response_time
Safari                 404           0.07
Iceweasel                0           0.00
Comodo Dragon            0           0.00
IE10                   404           0.08
Chrome                 200           0.02

>>> df.reindex(new_index, fill_value='missing')
              http_status response_time
Safari                404          0.07
Iceweasel         missing       missing
Comodo Dragon     missing       missing
IE10                  404          0.08
Chrome                200          0.02

We can also reindex the columns.

>>> df.reindex(columns=['http_status', 'user_agent'])
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

Or we can use “axis-style” keyword arguments

>>> df.reindex(['http_status', 'user_agent'], axis="columns")
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

To further illustrate the filling functionality in reindex, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates).

>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
...                    index=date_index)
>>> df2
            prices
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0

Suppose we decide to expand the dataframe to cover a wider date range.

>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
            prices
2009-12-29     NaN
2009-12-30     NaN
2009-12-31     NaN
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

The index entries that did not have a value in the original data frame (for example, ‘2009-12-29’) are by default filled with NaN. If desired, we can fill in the missing values using one of several options.

For example, to back-propagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

>>> df2.reindex(date_index2, method='bfill')
            prices
2009-12-29   100.0
2009-12-30   100.0
2009-12-31   100.0
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

Please note that the NaN value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original dataframe, use the fillna() method.

See the user guide for more.

sparkle.FeatureDataFrame.reindex_like(self, other, method: Literal['backfill', 'bfill', 'pad', 'ffill', 'nearest'] | None = None, copy: bool | None = None, limit: int | None = None, tolerance=None) → None

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters

otherObject of the same data type

Its row and column indices are used to define the new indices of this object.

method{None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}

Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

None (default): don’t fill gaps
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use next valid observation to fill gap
nearest: use nearest valid observations to fill gap.

copybool, default True

Return a new object, even if the passed indexes are the same.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

limitint, default None

Maximum number of consecutive labels to fill for inexact matches.

toleranceoptional

Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns

Series or DataFrame: Same type as caller, but with changed indices on each axis.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...).

Examples

>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
...                     [31, 87.8, 'high'],
...                     [22, 71.6, 'medium'],
...                     [35, 95, 'medium']],
...                    columns=['temp_celsius', 'temp_fahrenheit',
...                             'windspeed'],
...                    index=pd.date_range(start='2014-02-12',
...                                        end='2014-02-15', freq='D'))

>>> df1
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium

>>> df2 = pd.DataFrame([[28, 'low'],
...                     [30, 'low'],
...                     [35.1, 'medium']],
...                    columns=['temp_celsius', 'windspeed'],
...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
...                                            '2014-02-15']))

>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium

>>> df2.reindex_like(df1)
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       NaN
2014-02-15          35.1              NaN    medium

sparkle.FeatureDataFrame.remaining_jobs(self: FeatureDataFrame) → list[tuple[str, str, str]]

Determines needed feature computations per instance/extractor/group.

Returns:

list: A list of tuples representing (Extractor, Instance, Feature Group).: that needs to be computed.

sparkle.FeatureDataFrame.remove_extractor(self: FeatureDataFrame, extractor: str) → None: Remove an extractor from the dataframe.

sparkle.FeatureDataFrame.remove_instances(self: FeatureDataFrame, instances: str | list[str]) → None: Remove an instance from the dataframe.

Rename columns or index labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

See the user guide for more.

Parameters

mapperdict-like or function: Dict-like or function transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.
indexdict-like or function: Alternative to specifying axis (mapper, axis=0 is equivalent to index=mapper).
columnsdict-like or function: Alternative to specifying axis (mapper, axis=1 is equivalent to columns=mapper).
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.
copybool, default True: Also copy underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one. If True then value of copy is ignored.
levelint or level name, default None: In case of a MultiIndex, only rename labels in the specified level.
errors{‘ignore’, ‘raise’}, default ‘ignore’: If ‘raise’, raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed. If ‘ignore’, existing keys will be renamed and extra keys will be ignored.

Returns

DataFrame or None: DataFrame with the renamed axis labels or None if inplace=True.

Raises

KeyError: If any of the labels is not found in the selected axis and “errors=’raise’”.

Examples

DataFrame.rename supports two calling conventions

(index=index_mapper, columns=columns_mapper, ...)
(mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')

>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters:

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6

>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6

sparkle.FeatureDataFrame.rename_axis(self, mapper: IndexLabel | lib.NoDefault = <no_default>, *, index=<no_default>, columns=<no_default>, axis: Axis = 0, copy: bool_t | None = None, inplace: bool_t = False) → Self | None

Set the name of the axis for the index or columns.

Parameters

mapperscalar, list-like, optional

Value to set the axis name attribute.

index, columnsscalar, list-like, dict-like or function, optional

A scalar, list-like, dict-like or functions transformations to apply to that axis’ values. Note that the columns parameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects.

Use either mapper and axis to specify the axis to target with mapper, or index and/or columns.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to rename. For Series this parameter is unused and defaults to 0.

copybool, default None

Also copy underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

inplacebool, default False

Modifies the object directly, instead of creating a new Series or DataFrame.

Returns

Series, DataFrame, or None: The same type as the caller or None if inplace=True.

Notes

DataFrame.rename_axis supports two calling conventions

(index=index_mapper, columns=columns_mapper, ...)
(mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

Series

>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0       dog
1       cat
2    monkey
dtype: object
>>> s.rename_axis("animal")
animal
0    dog
1    cat
2    monkey
dtype: object

DataFrame

>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
...                    "num_arms": [0, 0, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product([['mammal'],
...                                        ['dog', 'cat', 'monkey']],
...                                       names=['type', 'name'])
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(index={'type': 'class'})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2

sparkle.FeatureDataFrame.reorder_levels(self, order: Sequence[int | str], axis: Axis = 0) → DataFrame

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters

orderlist of int or list of str: List representing new level order. Reference level by number (position) or by key (label).
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Where to reorder levels.

Returns

DataFrame

Examples

>>> data = {
...     "class": ["Mammals", "Mammals", "Reptiles"],
...     "diet": ["Omnivore", "Carnivore", "Carnivore"],
...     "species": ["Humans", "Dogs", "Snakes"],
... }
>>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
>>> df = df.set_index(["class", "diet"])
>>> df
                                  species
class      diet
Mammals    Omnivore                Humans
           Carnivore                 Dogs
Reptiles   Carnivore               Snakes

Let’s reorder the levels of the index:

>>> df.reorder_levels(["diet", "class"])
                                  species
diet      class
Omnivore  Mammals                  Humans
Carnivore Mammals                    Dogs
          Reptiles                 Snakes

sparkle.FeatureDataFrame.replace(self, to_replace=None, value=<no_default>, *, inplace: bool_t = False, limit: int | None = None, regex: bool_t = False, method: Literal['pad', 'ffill', 'bfill'] | lib.NoDefault = <no_default>) → Self | None

Replace values given in to_replace with value.

Values of the Series/DataFrame are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Parameters

to_replacestr, regex, list, dict, Series, int, float, or None

How to find the values that will be replaced.

numeric, str or regex:
- numeric: numeric values equal to to_replace will be replaced with value
- str: string exactly matching to_replace will be replaced with value
- regex: regexs matching to_replace will be replaced with value
list of str, regex, or numeric:
- First, if to_replace and value are both lists, they must be the same length.
- Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn’t matter much for value since there are only a few possible substitution regexes you can use.
- str, regex and numeric rules apply as above.
dict:
- Dicts can be used to specify different replacement values for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value ‘a’ with ‘b’ and ‘y’ with ‘z’. To use a dict in this way, the optional value parameter should not be given.
- For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column ‘a’ and the value ‘z’ in column ‘b’ and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.
- For a DataFrame nested dictionaries, e.g., {'a': {'b': np.nan}}, are read as follows: look in column ‘a’ for the value ‘b’ and replace it with NaN. The optional value parameter should not be specified to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
None:
- This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.

See the examples section for examples of each of these.

valuescalar, dict, list, str, regex, default None

Value to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.

inplacebool, default False

If True, performs operation inplace and returns None.

limitint, default None

Maximum size gap to forward or backward fill.

Deprecated since version 2.1.0.

regexbool or same types as to_replace, default False

Whether to interpret to_replace and/or value as regular expressions. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.

method{‘pad’, ‘ffill’, ‘bfill’}

The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

Deprecated since version 2.1.0.

Returns

Series/DataFrame: Object after replacement.

Raises

AssertionError

If regex is not a bool and to_replace is not None.

TypeError

If to_replace is not a scalar, array-like, dict, or None
If to_replace is a dict and value is not a list, dict, ndarray, or Series
If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series.
When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced

ValueError

If a list or an ndarray is passed to to_replace and value but they are not the same length.

Notes

Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.
When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.

Examples

Scalar `to_replace` and `value`

>>> s = pd.Series([1, 2, 3, 4, 5])
>>> s.replace(1, 5)
0    5
1    2
2    3
3    4
4    5
dtype: int64

>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
...                    'B': [5, 6, 7, 8, 9],
...                    'C': ['a', 'b', 'c', 'd', 'e']})
>>> df.replace(0, 5)
    A  B  C
0  5  5  a
1  1  6  b
2  2  7  c
3  3  8  d
4  4  9  e

List-like `to_replace`

>>> df.replace([0, 1, 2, 3], 4)
    A  B  C
4  5  a
4  6  b
4  7  c
4  8  d
4  9  e

>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
    A  B  C
4  5  a
3  6  b
2  7  c
1  8  d
4  9  e

>>> s.replace([1, 2], method='bfill')
  3
  3
  3
  4
  5
dtype: int64

dict-like `to_replace`

>>> df.replace({0: 10, 1: 100})
        A  B  C
 10  5  a
100  6  b
  2  7  c
  3  8  d
  4  9  e

>>> df.replace({'A': 0, 'B': 5}, 100)
        A    B  C
100  100  a
  1    6  b
  2    7  c
  3    8  d
  4    9  e

>>> df.replace({'A': {0: 100, 4: 400}})
        A  B  C
100  5  a
  1  6  b
  2  7  c
  3  8  d
400  9  e

Regular expression `to_replace`

>>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
...                    'B': ['abc', 'bar', 'xyz']})
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
        A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
        A    B
0   new  abc
1   foo  bar
2  bait  xyz

>>> df.replace(regex=r'^ba.$', value='new')
        A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})
        A    B
0   new  abc
1   xyz  new
2  bait  xyz

>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
        A    B
0   new  abc
1   new  new
2  bait  xyz

Compare the behavior of s.replace({'a': None}) and s.replace('a', None) to understand the peculiarities of the to_replace parameter:

>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])

When one uses a dict as the to_replace value, it is like the value(s) in the dict are equal to the value parameter. s.replace({'a': None}) is equivalent to s.replace(to_replace={'a': None}, value=None, method=None):

>>> s.replace({'a': None})
    10
  None
  None
     b
  None
dtype: object

When value is not explicitly passed and to_replace is a scalar, list or tuple, replace uses the method parameter (default ‘pad’) to do the replacement. So this is why the ‘a’ values are being replaced by 10 in rows 1 and 2 and ‘b’ in row 4 in this case.

>>> s.replace('a')
  10
  10
  10
   b
   b
dtype: object

Deprecated since version 2.1.0: The ‘method’ parameter and padding behavior are deprecated.

On the other hand, if None is explicitly passed for value, it will be respected:

>>> s.replace('a', None)
    10
  None
  None
     b
  None
dtype: object

Changed in version 1.4.0: Previously the explicit None was silently ignored.

When regex=True, value is not None and to_replace is a string, the replacement will be applied in all columns of the DataFrame.

>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
...                    'B': ['a', 'b', 'c', 'd', 'e'],
...                    'C': ['f', 'g', 'h', 'i', 'j']})

>>> df.replace(to_replace='^[a-g]', value='e', regex=True)
    A  B  C
0  e  e
1  e  e
2  e  h
3  e  i
4  e  j

If value is not None and to_replace is a dictionary, the dictionary keys will be the DataFrame columns that the replacement will be applied.

>>> df.replace(to_replace={'B': '^[a-c]', 'C': '^[h-j]'}, value='e', regex=True)
    A  B  C
0  e  f
1  e  g
2  e  e
3  d  e
4  e  e

sparkle.FeatureDataFrame.resample(self, rule, axis: Axis | lib.NoDefault = <no_default>, closed: Literal['right', 'left'] | None = None, label: Literal['right', 'left'] | None = None, convention: Literal['start', 'end', 's', 'e'] | lib.NoDefault = <no_default>, kind: Literal['timestamp', 'period'] | None | lib.NoDefault = <no_default>, on: Level | None = None, level: Level | None = None, origin: str | TimestampConvertibleTypes = 'start_day', offset: TimedeltaConvertibleTypes | None = None, group_keys: bool_t = False) → Resampler

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the on/level keyword parameter.

Parameters

ruleDateOffset, Timedelta or str

The offset string or object representing target conversion.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Which axis to use for up- or down-sampling. For Series this parameter is unused and defaults to 0. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.

Deprecated since version 2.0.0: Use frame.T.resample(…) instead.

closed{‘right’, ‘left’}, default None

Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

label{‘right’, ‘left’}, default None

Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

convention{‘start’, ‘end’, ‘s’, ‘e’}, default ‘start’

For PeriodIndex only, controls whether to use the start or end of rule.

Deprecated since version 2.2.0: Convert PeriodIndex to DatetimeIndex before resampling instead.

kind{‘timestamp’, ‘period’}, optional, default None

Pass ‘timestamp’ to convert the resulting index to a DateTimeIndex or ‘period’ to convert it to a PeriodIndex. By default the input representation is retained.

Deprecated since version 2.2.0: Convert index to desired type explicitly instead.

onstr, optional

For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

levelstr or int, optional

For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.

originTimestamp or str, default ‘start_day’

The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be one of the following:

‘epoch’: origin is 1970-01-01
‘start’: origin is the first value of the timeseries
‘start_day’: origin is the first day at midnight of the timeseries
‘end’: origin is the last value of the timeseries
‘end_day’: origin is the ceiling midnight of the last day

Added in version 1.3.0.

Note

Only takes effect for Tick-frequencies (i.e. fixed frequencies like days, hours, and minutes, rather than months or quarters).

offsetTimedelta or str, default is None

An offset timedelta added to the origin.

group_keysbool, default False

Whether to include the group keys in the result index when using .apply() on the resampled object.

Added in version 1.5.0: Not specifying group_keys will retain values-dependent behavior from pandas 1.4 and earlier (see pandas 1.5.0 Release notes for examples).

Changed in version 2.0.0: group_keys now defaults to False.

Returns

pandas.api.typing.Resampler: Resampler object.

Notes

See the user guide for more.

To learn more about the offset strings, please see this link.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='min')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: min, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample('3min').sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3min, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3).

>>> series.resample('3min', label='right').sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3min, dtype: int64

To include this value close the right side of the bin interval, as shown below.

>>> series.resample('3min', label='right', closed='right').sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3min, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample('30s').asfreq()[0:5]   # Select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30s, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the ffill method.

>>> series.resample('30s').ffill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30s, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample('30s').bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30s, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(arraylike):
...     return np.sum(arraylike) + 5
...
>>> series.resample('3min').apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3min, dtype: int64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
...                                     periods=8,
...                                     freq='W')
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
>>> df.resample('ME', on='week_starting').mean()
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df2 = pd.DataFrame(
...     d2,
...     index=pd.MultiIndex.from_product(
...         [days, ['morning', 'afternoon']]
...     )
... )
>>> df2
                      price  volume
2000-01-01 morning       10      50
           afternoon     11      60
2000-01-02 morning        9      40
           afternoon     13     100
2000-01-03 morning       14      50
           afternoon     18     100
2000-01-04 morning       17      40
           afternoon     19      50
>>> df2.resample('D', level=0).sum()
            price  volume
2000-01-01     21     110
2000-01-02     22     140
2000-01-03     32     150
2000-01-04     36      90

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7min, dtype: int64

>>> ts.resample('17min').sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17min, dtype: int64

>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17min, dtype: int64

>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17min, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17min, dtype: int64

>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17min, dtype: int64

If you want to take the largest Timestamp as the end of the bins:

>>> ts.resample('17min', origin='end').sum()
2000-10-01 23:35:00     0
2000-10-01 23:52:00    18
2000-10-02 00:09:00    27
2000-10-02 00:26:00    63
Freq: 17min, dtype: int64

In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

>>> ts.resample('17min', origin='end_day').sum()
2000-10-01 23:38:00     3
2000-10-01 23:55:00    15
2000-10-02 00:12:00    45
2000-10-02 00:29:00    45
Freq: 17min, dtype: int64

sparkle.FeatureDataFrame.reset_dataframe(self: FeatureDataFrame) → bool: Resets all values to FeatureDataFrame.missing_value.

sparkle.FeatureDataFrame.reset_index(self, level: IndexLabel | None = None, *, drop: bool = False, inplace: bool = False, col_level: Hashable = 0, col_fill: Hashable = '', allow_duplicates: bool | lib.NoDefault = <no_default>, names: Hashable | Sequence[Hashable] | None = None) → DataFrame | None

Reset the index, or a level of it.

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters

levelint, str, tuple, or list, default None: Only remove the given levels from the index. Removes all levels by default.
dropbool, default False: Do not try to insert index into dataframe columns. This resets the index to the default integer index.
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one.
col_levelint or str, default 0: If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level.
col_fillobject, default ‘’: If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.
allow_duplicatesbool, optional, default lib.no_default: Allow duplicate column labels to be created.

Added in version 1.5.0.
namesint, str or 1-dimensional list, default None: Using the given string, rename the DataFrame column which contains the index data. If the DataFrame has a MultiIndex, this has to be a list or tuple with length equal to the number of levels.

Added in version 1.5.0.

Returns

DataFrame or None: DataFrame with the new index or None if inplace=True.

Examples

>>> df = pd.DataFrame([('bird', 389.0),
...                    ('bird', 24.0),
...                    ('mammal', 80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
...                    (24.0, 'fly'),
...                    (80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

Using the names parameter, choose a name for the index column:

>>> df.reset_index(names=['classes', 'names'])
  classes   names  speed species
                     max    type
0    bird  falcon  389.0     fly
1    bird  parrot   24.0     fly
2  mammal    lion   80.5     run
3  mammal  monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

sparkle.FeatureDataFrame.rfloordiv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).

Equivalent to other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, floordiv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.rmod(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Modulo of dataframe and other, element-wise (binary operator rmod).

Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mod.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.rmul(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator rmul).

Equivalent to other * dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

Provide rolling window calculations.

Parameters

windowint, timedelta, str, offset, or BaseIndexer subclass

Size of the moving window.

If an integer, the fixed number of observations used for each window.

If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. To learn more about the offsets & frequency strings, please see this link.

If a BaseIndexer subclass, the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, closed and step will be passed to get_window_bounds.

min_periodsint, default None

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

For a window that is specified by an offset, min_periods will default to 1.

For a window that is specified by an integer, min_periods will default to the size of the window.

centerbool, default False

If False, set the window labels as the right edge of the window index.

If True, set the window labels as the center of the window index.

win_typestr, default None

If None, all points are evenly weighted.

If a string, it must be a valid scipy.signal window function.

Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature.

onstr, optional

For a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame’s index.

Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.

axisint or str, default 0

If 0 or 'index', roll across the rows.

If 1 or 'columns', roll across the columns.

For Series this parameter is unused and defaults to 0.

Deprecated since version 2.1.0: The axis keyword is deprecated. For axis=1, transpose the DataFrame first instead.

closedstr, default None

If 'right', the first point in the window is excluded from calculations.

If 'left', the last point in the window is excluded from calculations.

If 'both', the no points in the window are excluded from calculations.

If 'neither', the first and last points in the window are excluded from calculations.

Default None ('right').

step : int, default None

Added in version 1.5.0.

Evaluate the window at every step result, equivalent to slicing as [::step]. window must be an integer. Using a step argument other than None or 1 will produce a result with a different shape than the input.

method : str {‘single’, ‘table’}, default ‘single’

Added in version 1.3.0.

Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Returns

pandas.api.typing.Window or pandas.api.typing.Rolling: An instance of Window is returned if win_type is passed. Otherwise, an instance of Rolling is returned.

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

window

Rolling sum with a window length of 2 observations.

>>> df.rolling(2).sum()
     B
NaN
1.0
3.0
NaN
NaN

Rolling sum with a window span of 2 seconds.

>>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
...                        index=[pd.Timestamp('20130101 09:00:00'),
...                               pd.Timestamp('20130101 09:00:02'),
...                               pd.Timestamp('20130101 09:00:03'),
...                               pd.Timestamp('20130101 09:00:05'),
...                               pd.Timestamp('20130101 09:00:06')])

>>> df_time
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

>>> df_time.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Rolling sum with forward looking windows with 2 observations.

>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

min_periods

Rolling sum with a window length of 2 observations, but only needs a minimum of 1 observation to calculate a value.

>>> df.rolling(2, min_periods=1).sum()
     B
0.0
1.0
3.0
2.0
4.0

center

Rolling sum with the result assigned to the center of the window index.

>>> df.rolling(3, min_periods=1, center=True).sum()
     B
1.0
3.0
3.0
6.0
4.0

>>> df.rolling(3, min_periods=1, center=False).sum()
     B
0.0
1.0
3.0
3.0
6.0

step

Rolling sum with a window length of 2 observations, minimum of 1 observation to calculate a value, and a step of 2.

>>> df.rolling(2, min_periods=1, step=2).sum()
     B
0  0.0
2  3.0
4  4.0

win_type

Rolling sum with a window length of 2, using the Scipy 'gaussian' window type. std is required in the aggregation function.

>>> df.rolling(2, win_type='gaussian').sum(std=3)
          B
     NaN
0.986207
2.958621
     NaN
     NaN

on

Rolling sum with a window length of 2 days.

>>> df = pd.DataFrame({
...     'A': [pd.to_datetime('2020-01-01'),
...           pd.to_datetime('2020-01-01'),
...           pd.to_datetime('2020-01-02'),],
...     'B': [1, 2, 3], },
...     index=pd.date_range('2020', periods=3))

>>> df
                    A  B
2020-01-01 2020-01-01  1
2020-01-02 2020-01-01  2
2020-01-03 2020-01-02  3

>>> df.rolling('2D', on='A').sum()
                    A    B
2020-01-01 2020-01-01  1.0
2020-01-02 2020-01-01  3.0
2020-01-03 2020-01-02  6.0

sparkle.FeatureDataFrame.round(self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs) → DataFrame

Round a DataFrame to a variable number of decimal places.

Parameters

decimalsint, dict, Series: Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.
*args: Additional keywords have no effect but might be accepted for compatibility with numpy.
**kwargs: Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns

DataFrame: A DataFrame with the affected columns rounded to the specified number of decimal places.

Examples

>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
...                   columns=['dogs', 'cats'])
>>> df
    dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18

By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({'dogs': 1, 'cats': 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

sparkle.FeatureDataFrame.rpow(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.rsub(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator rsub).

Equivalent to other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, sub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.rtruediv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters

nint, optional: Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.
fracfloat, optional: Fraction of axis items to return. Cannot be used with n.
replacebool, default False: Allow or disallow sampling of the same row more than once.
weightsstr or ndarray-like, optional: Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.
random_stateint, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional: If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given.

Changed in version 1.4.0: np.random.Generator objects now accepted
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Axis to sample. Accepts axis number or name. Default is stat axis for given data type. For Series this parameter is unused and defaults to None.
ignore_indexbool, default False: If True, the resulting index will be labeled 0, 1, …, n - 1.

Added in version 1.3.0.

Returns

Series or DataFrame: A new object of same type as caller containing n items randomly sampled from the caller object.

Notes

If frac > 1, replacement should be set to True.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
...                    'num_wings': [2, 0, 0, 0],
...                    'num_specimen_seen': [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df['num_legs'].sample(n=3, random_state=1)
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1)
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8

sparkle.FeatureDataFrame.save_csv(self: FeatureDataFrame, csv_filepath: Path = None) → None

Write a CSV to the given path.

Args:: csv_filepath: String path to the csv file. Defaults to self.csv_filepath.

sparkle.FeatureDataFrame.select_dtypes(self, include=None, exclude=None) → Self

Return a subset of the DataFrame’s columns based on the column dtypes.

Parameters

include, excludescalar or list-like: A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

Returns

DataFrame: The subset of the frame including the dtypes in include and excluding the dtypes in exclude.

Raises

ValueError

If both of include and exclude are empty
If include and exclude have overlapping elements
If any kind of string dtype is passed in.

Notes

To select all numeric types, use np.number or 'number'
To select strings you must use the object dtype, but note that this will return all object dtype columns. With pd.options.future.infer_string enabled, using "str" will work to select all string columns.
See the numpy dtype hierarchy
To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'
To select Pandas categorical dtypes, use 'category'
To select Pandas datetimetz dtypes, use 'datetimetz' or 'datetime64[ns, tz]'

Examples

>>> df = pd.DataFrame({'a': [1, 2] * 3,
...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0

>>> df.select_dtypes(include='bool')
   b
True
False
True
False
True
False

>>> df.select_dtypes(include=['float64'])
   c
1.0
2.0
1.0
2.0
1.0
2.0

>>> df.select_dtypes(exclude=['int64'])
       b    c
 True  1.0
False  2.0
 True  1.0
False  2.0
 True  1.0
False  2.0

sparkle.FeatureDataFrame.sem(self, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters

axis{index (0), columns (1)}: For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.sem with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.

Returns

Series or DataFrame (if level specified)

>>> s = pd.Series([1, 2, 3])
>>> s.sem().round(6)
0.57735

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.sem()
a   0.5
b   0.5
dtype: float64

Using axis=1

>>> df.sem(axis=1)
tiger   0.5
zebra   0.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
...                   index=['tiger', 'zebra'])
>>> df.sem(numeric_only=True)
a   0.5
dtype: float64

sparkle.FeatureDataFrame.set_axis(self, labels, *, axis: Axis = 0, copy: bool | None = None) → DataFrame

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or Index.

Parameters

labelslist-like, Index: The values for the new index.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to update. The value 0 identifies the rows. For Series this parameter is unused and defaults to 0.
copybool, default True: Whether to make a copy of the underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: An object of type DataFrame.

Parameters

copybool, default False: Specify if a copy of the object should be made.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True
allows_duplicate_labelsbool, optional: Whether the returned object allows duplicate labels.

Returns

Series or DataFrame: The same type as the caller.

Notes

This method returns a new object that’s a view on the same data as the input. Mutating the input or the output values will be reflected in the other.

This method is intended to be used in method chains.

“Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in DataFrame.attrs.

Examples

>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False

sparkle.FeatureDataFrame.set_index(self, keys, *, drop: bool = True, append: bool = False, inplace: bool = False, verify_integrity: bool = False) → DataFrame | None

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.

Parameters

keyslabel or array-like or list of labels/arrays: This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, “array” encompasses Series, Index, np.ndarray, and instances of Iterator.
dropbool, default True: Delete columns to be used as the new index.
appendbool, default False: Whether to append columns to existing index.
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one.
verify_integritybool, default False: Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method.

Returns

DataFrame or None: Changed row labels or None if inplace=True.

Examples

>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
...                    'year': [2012, 2014, 2013, 2014],
...                    'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31

Set the index to become the ‘month’ column:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31

Create a MultiIndex using columns ‘year’ and ‘month’:

>>> df.set_index(['year', 'month'])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a MultiIndex using an Index and a column:

>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
      month  year  sale
1 1       1  2012    55
2 4       4  2014    40
3 9       7  2013    84
4 16     10  2014    31

sparkle.FeatureDataFrame.set_value(self: FeatureDataFrame, instance: str, extractor: str, feature_group: str, feature_name: str, value: float) → None: Set a value in the dataframe.

sparkle.FeatureDataFrame.shift(self, periods: int | Sequence[int] = 1, freq: Frequency | None = None, axis: Axis = 0, fill_value: Hashable = <no_default>, suffix: str | None = None) → DataFrame

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as “infer” as long as either freq or inferred_freq attribute is set in the index.

Parameters

periodsint or Sequence: Number of periods to shift. Can be positive or negative. If an iterable of ints, the data will be shifted once by each int. This is equivalent to shifting by one value at a time and concatenating all resulting frames. The resulting columns will have the shift suffixed to their column names. For multiple periods, axis must not be 1.
freqDateOffset, tseries.offsets, timedelta, or str, optional: Offset to use from the tseries module or time rule (e.g. ‘EOM’). If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as “infer” then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Shift direction. For Series this parameter is unused and defaults to 0.
fill_valueobject, optional: The scalar value to use for newly introduced missing values. the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. NaT is used. For extension dtypes, self.dtype.na_value is used.
suffixstr, optional: If str and periods is an iterable, this is added after the column name and before the shift value for each shifted column name.

Returns

DataFrame: Copy of input object, shifted.

Examples

>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],
...                    "Col2": [13, 23, 18, 33, 48],
...                    "Col3": [17, 27, 22, 37, 52]},
...                   index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52

>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0

>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01   NaN    10    13
2020-01-02   NaN    20    23
2020-01-03   NaN    15    18
2020-01-04   NaN    30    33
2020-01-05   NaN    45    48

>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27

>>> df.shift(periods=3, freq="D")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52

>>> df.shift(periods=3, freq="infer")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52

>>> df['Col1'].shift(periods=[0, 1, 2])
            Col1_0  Col1_1  Col1_2
2020-01-01      10     NaN     NaN
2020-01-02      20    10.0     NaN
2020-01-03      15    20.0    10.0
2020-01-04      30    15.0    20.0
2020-01-05      45    30.0    15.0

sparkle.FeatureDataFrame.skew(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 3])
>>> s.skew()
0.0

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
...                   index=['tiger', 'zebra', 'cow'])
>>> df
        a   b   c
tiger   1   2   1
zebra   2   3   3
cow     3   4   5
>>> df.skew()
a   0.0
b   0.0
c   0.0
dtype: float64

Using axis=1

>>> df.skew(axis=1)
tiger   1.732051
zebra  -1.732051
cow     0.000000
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
...                   index=['tiger', 'zebra', 'cow'])
>>> df.skew(numeric_only=True)
a   0.0
dtype: float64

sparkle.FeatureDataFrame.sort(self: FeatureDataFrame) → None: Sorts the DataFrame by Multi-Index for readability.

sparkle.FeatureDataFrame.sort_index(self, *, axis: Axis = 0, level: IndexLabel | None = None, ascending: bool | Sequence[bool] = True, inplace: bool = False, kind: SortKind = 'quicksort', na_position: NaPosition = 'last', sort_remaining: bool = True, ignore_index: bool = False, key: IndexKeyFunc | None = None) → DataFrame | None

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns.
levelint or level name or list of ints or list of level names: If not None, sort on values in specified index level(s).
ascendingbool or list-like of bools, default True: Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one.
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’: Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
na_position{‘first’, ‘last’}, default ‘last’: Puts NaNs at the beginning if first; last puts NaNs at the end. Not implemented for MultiIndex.
sort_remainingbool, default True: If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.
ignore_indexbool, default False: If True, the resulting axis will be labeled 0, 1, …, n - 1.
keycallable, optional: If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.

Returns

DataFrame or None: The original DataFrame sorted by the labels or None if inplace=True.

Examples

>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
...                   columns=['A'])
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

>>> df.sort_index(ascending=False)
     A
3
5
1
 2
  4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4

sparkle.FeatureDataFrame.sort_values(self, by: IndexLabel, *, axis: Axis = 0, ascending: bool | list[bool] | tuple[bool, ...] = True, inplace: bool = False, kind: SortKind = 'quicksort', na_position: str = 'last', ignore_index: bool = False, key: ValueKeyFunc | None = None) → DataFrame | None

Sort by the values along either axis.

Parameters

bystr or list of str

Name or list of names to sort by.

if axis is 0 or ‘index’ then by may contain index levels and/or column labels.
if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.

axis“{0 or ‘index’, 1 or ‘columns’}”, default 0

Axis to be sorted.

ascendingbool or list of bool, default True

Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

inplacebool, default False

If True, perform operation in-place.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’

Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.

na_position{‘first’, ‘last’}, default ‘last’

Puts NaNs at the beginning if first; last puts NaNs at the end.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

keycallable, optional

Apply the key function to the values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently.

Returns

DataFrame or None: DataFrame with sorted values or None if inplace=True.

Examples

>>> df = pd.DataFrame({
...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Sort by col1

>>> df.sort_values(by=['col1'])
  col1  col2  col3 col4
  A     2     0    a
  A     1     1    B
  B     9     9    c
  C     4     3    F
  D     7     2    e
NaN     8     4    D

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
  col1  col2  col3 col4
  A     1     1    B
  A     2     0    a
  B     9     9    c
  C     4     3    F
  D     7     2    e
NaN     8     4    D

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
  col1  col2  col3 col4
  D     7     2    e
  C     4     3    F
  B     9     9    c
  A     2     0    a
  A     1     1    B
NaN     8     4    D

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
  col1  col2  col3 col4
NaN     8     4    D
  D     7     2    e
  C     4     3    F
  B     9     9    c
  A     2     0    a
  A     1     1    B

Sorting with a key function

>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
   col1  col2  col3 col4
  A     2     0    a
  A     1     1    B
  B     9     9    c
NaN     8     4    D
  D     7     2    e
  C     4     3    F

Natural sort with the key argument, using the natsort <https://github.com/SethMMorton/natsort> package.

>>> df = pd.DataFrame({
...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
...    "value": [10, 20, 30, 40, 50]
... })
>>> df
    time  value
0    0hr     10
1  128hr     20
2   72hr     30
3   48hr     40
4   96hr     50
>>> from natsort import index_natsorted
>>> df.sort_values(
...     by="time",
...     key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
    time  value
0    0hr     10
3   48hr     40
2   72hr     30
4   96hr     50
1  128hr     20

sparkle.FeatureDataFrame.squeeze(self, axis: int | Literal['index', 'columns', 'rows'] | None = None)

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don’t know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters

axis{0 or ‘index’, 1 or ‘columns’, None}, default None: A specific axis to squeeze. By default, all length-1 axes are squeezed. For Series this parameter is unused and defaults to None.

Returns

DataFrame, Series, or scalar: The projection after squeezing axis or all the axes.

Examples

>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0    2
dtype: int64

>>> even_primes.squeeze()
2

Squeezing objects with more than one value in every axis does nothing:

>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1    3
2    5
3    7
dtype: int64

>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

>>> df_a = df[['a']]
>>> df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

>>> df_a.squeeze('columns')
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

>>> df_0a.squeeze('rows')
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_0a.squeeze()
1

sparkle.FeatureDataFrame.stack(self, level: IndexLabel = -1, dropna: bool | lib.NoDefault = <no_default>, sort: bool | lib.NoDefault = <no_default>, future_stack: bool = False)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

if the columns have a single level, the output is a Series;

if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Parameters

levelint, str, list, default -1: Level(s) to stack from the column axis onto the index axis, defined as one index or label, or a list of indices or labels.
dropnabool, default True: Whether to drop rows in the resulting Frame/Series with missing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.
sortbool, default True: Whether to sort the levels of the resulting MultiIndex.
future_stackbool, default False: Whether to use the new implementation that will replace the current implementation in pandas 3.0. When True, dropna and sort have no impact on the result and must remain unspecified. See pandas 2.1.0 Release notes for more details.

Returns

DataFrame or Series: Stacked dataframe or series.

Notes

The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).

Reference the user guide for more examples.

Examples

Single level columns

>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
...                                     index=['cat', 'dog'],
...                                     columns=['weight', 'height'])

Stacking a dataframe with a single level column axis returns a Series:

>>> df_single_level_cols
     weight height
cat       0      1
dog       2      3
>>> df_single_level_cols.stack(future_stack=True)
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64

Multi level columns: simple case

>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol1)

Stacking a dataframe with a multi-level column axis:

>>> df_multi_level_cols1
     weight
         kg    pounds
cat       1        2
dog       2        4
>>> df_multi_level_cols1.stack(future_stack=True)
            weight
cat kg           1
    pounds       2
dog kg           2
    pounds       4

Missing values

>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:

>>> df_multi_level_cols2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0
>>> df_multi_level_cols2.stack(future_stack=True)
        weight  height
cat kg     1.0     NaN
    m      NaN     2.0
dog kg     3.0     NaN
    m      NaN     4.0

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0, future_stack=True)
             kg    m
cat weight  1.0  NaN
    height  NaN  2.0
dog weight  3.0  NaN
    height  NaN  4.0
>>> df_multi_level_cols2.stack([0, 1], future_stack=True)
cat  weight  kg    1.0
     height  m     2.0
dog  weight  kg    3.0
     height  m     4.0
dtype: float64

sparkle.FeatureDataFrame.std(self, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters

axis{index (0), columns (1)}: For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.

Returns

Series or DataFrame (if level specified)

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

Examples

>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
...                    'age': [21, 25, 62, 43],
...                    'height': [1.61, 1.87, 1.49, 2.01]}
...                   ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

The standard deviation of the columns can be found as follows:

>>> df.std()
age       18.786076
height     0.237417
dtype: float64

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.std(ddof=0)
age       16.269219
height     0.205609
dtype: float64

sparkle.FeatureDataFrame.sub(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.subtract(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.sum(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs)

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.sum()
14

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
0.0

This can be controlled with the min_count parameter. For example, if you’d like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([], dtype="float64").sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()
0.0

>>> pd.Series([np.nan]).sum(min_count=1)
nan

sparkle.FeatureDataFrame.swapaxes(self, axis1: int | Literal['index', 'columns', 'rows'], axis2: int | Literal['index', 'columns', 'rows'], copy: bool | None = None) → None: Interchange axes and swap values axes appropriately.

Deprecated since version 2.1.0: swapaxes is deprecated and will be removed. Please use transpose instead.

Returns

same as input

Examples

Please see examples for DataFrame.transpose().

sparkle.FeatureDataFrame.swaplevel(self, i: Axis = -2, j: Axis = -1, axis: Axis = 0) → DataFrame

Swap levels i and j in a MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters

i, jint or str: Levels of the indices to be swapped. Can pass level name as string.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to swap levels on. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

Returns

DataFrame: DataFrame with levels swapped in MultiIndex.

Examples

>>> df = pd.DataFrame(
...     {"Grade": ["A", "B", "A", "C"]},
...     index=[
...         ["Final exam", "Final exam", "Coursework", "Coursework"],
...         ["History", "Geography", "History", "Geography"],
...         ["January", "February", "March", "April"],
...     ],
... )
>>> df
                                    Grade
Final exam  History     January      A
            Geography   February     B
Coursework  History     March        A
            Geography   April        C

In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.

>>> df.swaplevel()
                                    Grade
Final exam  January     History         A
            February    Geography       B
Coursework  March       History         A
            April       Geography       C

By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.

>>> df.swaplevel(0)
                                    Grade
January     History     Final exam      A
February    Geography   Final exam      B
March       History     Coursework      A
April       Geography   Coursework      C

We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.

>>> df.swaplevel(0, 1)
                                    Grade
History     Final exam  January         A
Geography   Final exam  February        B
History     Coursework  March           A
Geography   Coursework  April           C

sparkle.FeatureDataFrame.tail(self, n: int = 5) → None

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first |n| rows, equivalent to df[|n|:].

If n is larger than the number of rows, this function returns all rows.

Parameters

nint, default 5: Number of rows to select.

Returns

type of caller: The last n rows of the caller object.

Examples

>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the last 5 lines

>>> df.tail()
   animal
monkey
parrot
 shark
 whale
 zebra

Viewing the last n lines (three in this case)

>>> df.tail(3)
  animal
6  shark
7  whale
8  zebra

For negative values of n

>>> df.tail(-3)
   animal
  lion
monkey
parrot
 shark
 whale
 zebra

sparkle.FeatureDataFrame.take(self, indices, axis: int | Literal['index', 'columns', 'rows'] = 0, **kwargs) → None

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

Parameters

indicesarray-like: An array of ints indicating which positions to take.
axis{0 or ‘index’, 1 or ‘columns’, None}, default 0: The axis on which to select elements. 0 means that we are selecting rows, 1 means that we are selecting columns. For Series this parameter is unused and defaults to 0.
**kwargs: For compatibility with numpy.take(). Has no effect on the output.

Returns

same type as caller: An array-like containing the elements taken from the object.

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=['name', 'class', 'max_speed'],
...                   index=[0, 2, 3, 1])
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That’s because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
     name   class  max_speed
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5

sparkle.FeatureDataFrame.to_clipboard(self, *, excel: bool = True, sep: str | None = None, **kwargs) → None

Copy object to the system clipboard.

Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.

Parameters

excelbool, default True

Produce output in a csv format for easy pasting into excel.

True, use the provided separator for csv pasting.
False, write a string representation of the object to the clipboard.

sepstr, default '\t'

Field delimiter.

**kwargs

These parameters will be passed to DataFrame.to_csv.

Notes

Requirements for your platform.

Linux : xclip, or xsel (with PyQt4 modules)

Windows : none

macOS : none

This method uses the processes developed for the package pyperclip. A solution to render any output string format is given in the examples.

Examples

Copy the contents of a DataFrame to the clipboard.

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])

>>> df.to_clipboard(sep=',')  
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6

We can omit the index by passing the keyword index and setting it to false.

>>> df.to_clipboard(sep=',', index=False)  
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6

Using the original pyperclip package for any string output format.

import pyperclip
html = df.style.to_html()
pyperclip.copy(html)

sparkle.FeatureDataFrame.to_csv(self, path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None, *, sep: str = ',', na_rep: str = '', float_format: str | Callable | None = None, columns: Sequence[Hashable] | None = None, header: bool_t | list[str] = True, index: bool_t = True, index_label: IndexLabel | None = None, mode: str = 'w', encoding: str | None = None, compression: CompressionOptions = 'infer', quoting: int | None = None, quotechar: str = '"', lineterminator: str | None = None, chunksize: int | None = None, date_format: str | None = None, doublequote: bool_t = True, escapechar: str | None = None, decimal: str = '.', errors: OpenFileErrors = 'strict', storage_options: StorageOptions | None = None) → str | None

Write object to a comma-separated values (csv) file.

Parameters

path_or_bufstr, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with newline=’’, disabling universal newlines. If a binary file object is passed, mode might need to contain a ‘b’.

sepstr, default ‘,’

String of length 1. Field delimiter for the output file.

na_repstr, default ‘’

Missing data representation.

float_formatstr, Callable, default None

Format string for floating point numbers. If a Callable is given, it takes precedence over other numeric formatting parameters, like decimal.

columnssequence, optional

Columns to write.

headerbool or list of str, default True

Write out the column names. If a list of strings is given it is assumed to be aliases for the column names.

indexbool, default True

Write row names (index).

index_labelstr or sequence, or False, default None

Column label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.

mode{‘w’, ‘x’, ‘a’}, default ‘w’

Forwarded to either open(mode=) or fsspec.open(mode=) to control the file opening. Typical values include:

‘w’, truncate the file first.
‘x’, exclusive creation, failing if the file already exists.
‘a’, append to the end of file if it exists.

encodingstr, optional

A string representing the encoding to use in the output file, defaults to ‘utf-8’. encoding is not supported if path_or_buf is a non-binary file object.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

May be a dict with key ‘method’ as compression mode and other entries as additional compression options if compression mode is ‘zip’.

Passing compression options as keys in dict is supported for compression modes ‘gzip’, ‘bz2’, ‘zstd’, and ‘zip’.

quotingoptional constant from csv module

Defaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.

quotecharstr, default ‘"’

String of length 1. Character used to quote fields.

lineterminatorstr, optional

The newline character or character sequence to use in the output file. Defaults to os.linesep, which depends on the OS in which this method is called (’\n’ for linux, ‘\r\n’ for Windows, i.e.).

Changed in version 1.5.0: Previously was line_terminator, changed for consistency with read_csv and the standard library ‘csv’ module.

chunksizeint or None

Rows to write at a time.

date_formatstr, default None

Format string for datetime objects.

doublequotebool, default True

Control quoting of quotechar inside a field.

escapecharstr, default None

String of length 1. Character used to escape sep and quotechar when appropriate.

decimalstr, default ‘.’

Character recognized as decimal separator. E.g. use ‘,’ for European data.

errorsstr, default ‘strict’

Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Returns

None or str: If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.

Examples

Create ‘out.csv’ containing ‘df’ without indices

>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv('out.csv', index=False)  

Create ‘out.zip’ containing ‘out.csv’

>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  

To write a csv file to a new folder or nested folder you will first need to create it using either Pathlib or os:

>>> from pathlib import Path  
>>> filepath = Path('folder/subfolder/out.csv')  
>>> filepath.parent.mkdir(parents=True, exist_ok=True)  
>>> df.to_csv(filepath)  

>>> import os  
>>> os.makedirs('folder/subfolder', exist_ok=True)  
>>> df.to_csv('folder/subfolder/out.csv')  

sparkle.FeatureDataFrame.to_dict(self, orient: Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index'] = 'dict', *, into: type[MutableMappingT] | MutableMappingT = <class 'dict'>, index: bool = True) → MutableMappingT | list[MutableMappingT]

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Parameters

orientstr {‘dict’, ‘list’, ‘series’, ‘split’, ‘tight’, ‘records’, ‘index’}

Determines the type of the values of the dictionary.

‘dict’ (default) : dict like {column -> {index -> value}}
‘list’ : dict like {column -> [values]}
‘series’ : dict like {column -> Series(values)}
‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
‘tight’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values], ‘index_names’ -> [index.names], ‘column_names’ -> [column.names]}
‘records’ : list like [{column -> value}, … , {column -> value}]
‘index’ : dict like {index -> {column -> value}}

Added in version 1.4.0: ‘tight’ as an allowed value for the orient argument

intoclass, default dict

The collections.abc.MutableMapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.

indexbool, default True

Whether to include the index item (and index_names item if orient is ‘tight’) in the returned dictionary. Can only be False when orient is ‘split’ or ‘tight’.

Added in version 2.0.0.

Returns

dict, list or collections.abc.MutableMapping: Return a collections.abc.MutableMapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

Examples

>>> df = pd.DataFrame({'col1': [1, 2],
...                    'col2': [0.5, 0.75]},
...                   index=['row1', 'row2'])
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}

>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}

>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]

>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

>>> df.to_dict('tight')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]

sparkle.FeatureDataFrame.to_excel(self, excel_writer: FilePath | WriteExcelBuffer | ExcelWriter, *, sheet_name: str = 'Sheet1', na_rep: str = '', float_format: str | None = None, columns: Sequence[Hashable] | None = None, header: Sequence[Hashable] | bool_t = True, index: bool_t = True, index_label: IndexLabel | None = None, startrow: int = 0, startcol: int = 0, engine: Literal['openpyxl', 'xlsxwriter'] | None = None, merge_cells: bool_t = True, inf_rep: str = 'inf', freeze_panes: tuple[int, int] | None = None, storage_options: StorageOptions | None = None, engine_kwargs: dict[str, Any] | None = None) → None

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters

excel_writerpath-like, file-like, or ExcelWriter object: File path or existing ExcelWriter.
sheet_namestr, default ‘Sheet1’: Name of sheet which will contain DataFrame.
na_repstr, default ‘’: Missing data representation.
float_formatstr, optional: Format string for floating point numbers. For example float_format="%.2f" will format 0.1234 to 0.12.
columnssequence or list of str, optional: Columns to write.
headerbool or list of str, default True: Write out the column names. If a list of string is given it is assumed to be aliases for the column names.
indexbool, default True: Write row names (index).
index_labelstr or sequence, optional: Column label for index column(s) if desired. If not specified, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
startrowint, default 0: Upper left cell row to dump data frame.
startcolint, default 0: Upper left cell column to dump data frame.
enginestr, optional: Write engine to use, ‘openpyxl’ or ‘xlsxwriter’. You can also set this via the options io.excel.xlsx.writer or io.excel.xlsm.writer.
merge_cellsbool, default True: Write MultiIndex and Hierarchical Rows as merged cells.
inf_repstr, default ‘inf’: Representation for infinity (there is no native representation for infinity in Excel).
freeze_panestuple of int (length 2), optional: Specifies the one-based bottommost row and rightmost column that is to be frozen.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Added in version 1.2.0.
engine_kwargsdict, optional: Arbitrary keyword arguments passed to excel engine.

Notes

For compatibility with to_csv(), to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.

Examples

Create, write to and save a workbook:

>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
...                    index=['row 1', 'row 2'],
...                    columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx")  

To specify the sheet name:

>>> df1.to_excel("output.xlsx",
...              sheet_name='Sheet_name_1')  

If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object:

>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer:  
...     df1.to_excel(writer, sheet_name='Sheet_name_1')
...     df2.to_excel(writer, sheet_name='Sheet_name_2')

ExcelWriter can also be used to append to an existing Excel file:

>>> with pd.ExcelWriter('output.xlsx',
...                     mode='a') as writer:  
...     df1.to_excel(writer, sheet_name='Sheet_name_3')

To set the library that is used to write the Excel file, you can pass the engine keyword (the default engine is automatically chosen depending on the file extension):

>>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  

sparkle.FeatureDataFrame.to_feather(self, path: FilePath | WriteBuffer[bytes], **kwargs) → None

Write a DataFrame to the binary Feather format.

Parameters

pathstr, path object, file-like object: String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. If a string or a path, it will be used as Root Directory path when writing a partitioned dataset.
**kwargs :: Additional keywords passed to pyarrow.feather.write_feather(). This includes the compression, compression_level, chunksize and version keywords.

Notes

This function writes the dataframe as a feather file. Requires a default index. For saving the DataFrame with your custom index use a method that supports custom indices e.g. to_parquet.

Examples

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]])
>>> df.to_feather("file.feather")  

sparkle.FeatureDataFrame.to_gbq(self, destination_table: str, *, project_id: str | None = None, chunksize: int | None = None, reauth: bool = False, if_exists: ToGbqIfexist = 'fail', auth_local_webserver: bool = True, table_schema: list[dict[str, str]] | None = None, location: str | None = None, progress_bar: bool = True, credentials=None) → None

Write a DataFrame to a Google BigQuery table.

Deprecated since version 2.2.0: Please use pandas_gbq.to_gbq instead.

This function requires the pandas-gbq package.

See the How to authenticate with Google BigQuery guide for authentication instructions.

Parameters

destination_tablestr

Name of table to be written, in the form dataset.tablename.

project_idstr, optional

Google BigQuery Account project ID. Optional when available from the environment.

chunksizeint, optional

Number of rows to be inserted in each chunk from the dataframe. Set to None to load the whole dataframe at once.

reauthbool, default False

Force Google BigQuery to re-authenticate the user. This is useful if multiple accounts are used.

if_existsstr, default ‘fail’

Behavior when the destination table exists. Value can be one of:

'fail': If table exists raise pandas_gbq.gbq.TableCreationError.
'replace': If table exists, drop it, recreate it, and insert data.
'append': If table exists, insert data. Create if does not exist.

auth_local_webserverbool, default True

Use the `local webserver flow`_ instead of the `console flow`_ when getting user credentials.

New in version 0.2.0 of pandas-gbq.

Changed in version 1.5.0: Default value is changed to True. Google has deprecated the auth_local_webserver = False “out of band” (copy-paste) flow.

table_schemalist of dicts, optional

List of BigQuery table fields to which according DataFrame columns conform to, e.g. [{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field.

New in version 0.3.1 of pandas-gbq.

locationstr, optional

Location where the load job should run. See the BigQuery locations documentation for a list of available locations. The location must match that of the target dataset.

New in version 0.5.0 of pandas-gbq.

progress_barbool, default True

Use the library tqdm to show the progress bar for the upload, chunk by chunk.

New in version 0.5.0 of pandas-gbq.

credentialsgoogle.auth.credentials.Credentials, optional

Credentials for accessing Google APIs. Use this parameter to override default credentials, such as to use Compute Engine google.auth.compute_engine.Credentials or Service Account google.oauth2.service_account.Credentials directly.

New in version 0.8.0 of pandas-gbq.

Examples

Example taken from Google BigQuery documentation

>>> project_id = "my-project"
>>> table_id = 'my_dataset.my_table'
>>> df = pd.DataFrame({
...                   "my_string": ["a", "b", "c"],
...                   "my_int64": [1, 2, 3],
...                   "my_float64": [4.0, 5.0, 6.0],
...                   "my_bool1": [True, False, True],
...                   "my_bool2": [False, True, False],
...                   "my_dates": pd.date_range("now", periods=3),
...                   }
...                   )

>>> df.to_gbq(table_id, project_id=project_id)  

sparkle.FeatureDataFrame.to_hdf(self, path_or_buf: FilePath | HDFStore, *, key: str, mode: Literal['a', 'w', 'r+'] = 'a', complevel: int | None = None, complib: Literal['zlib', 'lzo', 'bzip2', 'blosc'] | None = None, append: bool_t = False, format: Literal['fixed', 'table'] | None = None, index: bool_t = True, min_itemsize: int | dict[str, int] | None = None, nan_rep=None, dropna: bool_t | None = None, data_columns: Literal[True] | list[str] | None = None, errors: OpenFileErrors = 'strict', encoding: str = 'UTF-8') → None

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

Warning

One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.

For more information see the user guide.

Parameters

path_or_bufstr or pandas.HDFStore

File path or HDFStore object.

keystr

Identifier for the group in the store.

mode{‘a’, ‘w’, ‘r+’}, default ‘a’

Mode to open file:

‘w’: write, a new file is created (an existing file with the same name would be deleted).
‘a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created.
‘r+’: similar to ‘a’, but the file must already exist.

complevel{0-9}, default None

Specifies a compression level for data. A value of 0 or None disables compression.

complib{‘zlib’, ‘lzo’, ‘bzip2’, ‘blosc’}, default ‘zlib’

Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError.

appendbool, default False

For Table formats, append the input data to the existing.

format{‘fixed’, ‘table’, None}, default ‘fixed’

Possible values:

‘fixed’: Fixed format. Fast writing/reading. Not-appendable, nor searchable.
‘table’: Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”.

indexbool, default True

Write DataFrame index as a column.

min_itemsizedict or int, optional

Map column names to minimum string sizes for columns.

nan_repAny, optional

How to represent null values as str. Not allowed with append=True.

dropnabool, default False, optional

Remove missing values.

data_columnslist of columns or True, optional

List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See Query via data columns. for more information. Applicable only to format=’table’.

errorsstr, default ‘strict’

Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.

encoding : str, default “UTF-8”

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
...                   index=['a', 'b', 'c'])  
>>> df.to_hdf('data.h5', key='df', mode='w')  

We can add another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])  
>>> s.to_hdf('data.h5', key='s')  

Reading from HDF file:

>>> pd.read_hdf('data.h5', 'df')  
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')  
0    1
1    2
2    3
3    4
dtype: int64

Render a DataFrame as an HTML table.

Parameters

bufstr, Path or StringIO-like, optional, default None

Buffer to write to. If None, the output is returned as a string.

columnsarray-like, optional, default None

The subset of columns to write. Writes all columns by default.

col_spacestr or int, list or dict of int or str, optional

The minimum width of each column in CSS length units. An int is assumed to be px units..

headerbool, optional

Whether to print column labels, default True.

indexbool, optional, default True

Whether to print index (row) labels.

na_repstr, optional, default ‘NaN’

String representation of NaN to use.

formatterslist, tuple or dict of one-param. functions, optional

Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

float_formatone-parameter function, optional, default None

Formatter function to apply to columns’ elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.

sparsifybool, optional, default True

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_namesbool, optional, default True

Prints the names of the indexes.

justifystr, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are

left
right
center
justify
justify-all
start
end
inherit
match-parent
initial
unset.

max_rowsint, optional

Maximum number of rows to display in the console.

max_colsint, optional

Maximum number of columns to display in the console.

show_dimensionsbool, default False

Display DataFrame dimensions (number of rows by number of columns).

decimalstr, default ‘.’

Character recognized as decimal separator, e.g. ‘,’ in Europe.

bold_rowsbool, default True

Make the row labels bold in the output.

classesstr or list or tuple, default None

CSS class(es) to apply to the resulting html table.

escapebool, default True

Convert the characters <, >, and & to HTML-safe sequences.

notebook{True, False}, default False

Whether the generated HTML is for IPython Notebook.

borderint

A border=border attribute is included in the opening <table> tag. Default pd.options.display.html.border.

table_idstr, optional

A css id is included in the opening <table> tag if specified.

render_linksbool, default False

Convert URLs to HTML links.

encodingstr, default “utf-8”

Set character encoding.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

Examples

>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
>>> html_string = '''<table border="1" class="dataframe">
...   <thead>
...     <tr style="text-align: right;">
...       <th></th>
...       <th>col1</th>
...       <th>col2</th>
...     </tr>
...   </thead>
...   <tbody>
...     <tr>
...       <th>0</th>
...       <td>1</td>
...       <td>4</td>
...     </tr>
...     <tr>
...       <th>1</th>
...       <td>2</td>
...       <td>3</td>
...     </tr>
...   </tbody>
... </table>'''
>>> assert html_string == df.to_html()

sparkle.FeatureDataFrame.to_json(self, path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None, *, orient: Literal['split', 'records', 'index', 'table', 'columns', 'values'] | None = None, date_format: str | None = None, double_precision: int = 10, force_ascii: bool_t = True, date_unit: TimeUnit = 'ms', default_handler: Callable[[Any], JSONSerializable] | None = None, lines: bool_t = False, compression: CompressionOptions = 'infer', index: bool_t | None = None, indent: int | None = None, storage_options: StorageOptions | None = None, mode: Literal['a', 'w'] = 'w') → str | None

Convert the object to a JSON string.

Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters

path_or_bufstr, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.

orientstr

Indication of expected JSON string format.

Series:
- default is ‘index’
- allowed values are: {‘split’, ‘records’, ‘index’, ‘table’}.
DataFrame:
- default is ‘columns’
- allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.
The format of the JSON string:
- ‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
- ‘records’ : list like [{column -> value}, … , {column -> value}]
- ‘index’ : dict like {index -> {column -> value}}
- ‘columns’ : dict like {column -> {index -> value}}
- ‘values’ : just the values array
- ‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}}
Describing the data, where data component is like orient='records'.

date_format{None, ‘epoch’, ‘iso’}

Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.

double_precisionint, default 10

The number of decimal places to use when encoding floating point values. The possible maximal value is 15. Passing double_precision greater than 15 will raise a ValueError.

force_asciibool, default True

Force encoded string to be ASCII.

date_unitstr, default ‘ms’ (milliseconds)

The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.

default_handlercallable, default None

Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.

linesbool, default False

If ‘orient’ is ‘records’ write out line-delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list-like.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

indexbool or None, default None

The index is only used when ‘orient’ is ‘split’, ‘index’, ‘column’, or ‘table’. Of these, ‘index’ and ‘column’ do not support index=False.

indentint, optional

Length of whitespace used to indent each record.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

modestr, default ‘w’ (writing)

Specify the IO mode for output when supplying a path_or_buf. Accepted args are ‘w’ (writing) and ‘a’ (append) only. mode=’a’ is only supported when lines is True and orient is ‘records’.

Returns

None or str: If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

Notes

The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release.

orient='table' contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema.

Examples

>>> from json import loads, dumps
>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )

>>> result = df.to_json(orient="split")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> result = df.to_json(orient="records")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> result = df.to_json(orient="index")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "row 1": {
        "col 1": "a",
        "col 2": "b"
    },
    "row 2": {
        "col 1": "c",
        "col 2": "d"
    }
}

Encoding/decoding a Dataframe using 'columns' formatted JSON:

>>> result = df.to_json(orient="columns")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "col 1": {
        "row 1": "a",
        "row 2": "c"
    },
    "col 2": {
        "row 1": "b",
        "row 2": "d"
    }
}

Encoding/decoding a Dataframe using 'values' formatted JSON:

>>> result = df.to_json(orient="values")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
[
    [
        "a",
        "b"
    ],
    [
        "c",
        "d"
    ]
]

Encoding with Table Schema:

>>> result = df.to_json(orient="table")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "schema": {
        "fields": [
            {
                "name": "index",
                "type": "string"
            },
            {
                "name": "col 1",
                "type": "string"
            },
            {
                "name": "col 2",
                "type": "string"
            }
        ],
        "primaryKey": [
            "index"
        ],
        "pandas_version": "1.4.0"
    },
    "data": [
        {
            "index": "row 1",
            "col 1": "a",
            "col 2": "b"
        },
        {
            "index": "row 2",
            "col 1": "c",
            "col 2": "d"
        }
    ]
}

Render object to a LaTeX tabular, longtable, or nested table.

Requires \usepackage{{booktabs}}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{{table.tex}}.

Changed in version 2.0.0: Refactored to use the Styler implementation via jinja2 templating.

Parameters

bufstr, Path or StringIO-like, optional, default None: Buffer to write to. If None, the output is returned as a string.
columnslist of label, optional: The subset of columns to write. Writes all columns by default.
headerbool or list of str, default True: Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
indexbool, default True: Write row names (index).
na_repstr, default ‘NaN’: Missing data representation.
formatterslist of functions or dict of {{str: function}}, optional: Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns.
float_formatone-parameter function or str, optional, default None: Formatter for floating point numbers. For example float_format="%.2f" and float_format="{{:0.2f}}".format will both result in 0.1234 being formatted as 0.12.
sparsifybool, optional: Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module.
index_namesbool, default True: Prints the names of the indexes.
bold_rowsbool, default False: Make the row labels bold in the output.
column_formatstr, optional: The columns format as specified in LaTeX table format e.g. ‘rcl’ for 3 columns. By default, ‘l’ will be used for all columns except columns of numbers, which default to ‘r’.
longtablebool, optional: Use a longtable environment instead of tabular. Requires adding a usepackage{{longtable}} to your LaTeX preamble. By default, the value will be read from the pandas config module, and set to True if the option styler.latex.environment is “longtable”.

Changed in version 2.0.0: The pandas option affecting this argument has changed.
escapebool, optional: By default, the value will be read from the pandas config module and set to True if the option styler.format.escape is “latex”. When set to False prevents from escaping latex special characters in column names.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to False.
encodingstr, optional: A string representing the encoding to use in the output file, defaults to ‘utf-8’.
decimalstr, default ‘.’: Character recognized as decimal separator, e.g. ‘,’ in Europe.
multicolumnbool, default True: Use multicolumn to enhance MultiIndex columns. The default will be read from the config module, and is set as the option styler.sparse.columns.

Changed in version 2.0.0: The pandas option affecting this argument has changed.
multicolumn_formatstr, default ‘r’: The alignment for multicolumns, similar to column_format The default will be read from the config module, and is set as the option styler.latex.multicol_align.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to “r”.
multirowbool, default True: Use multirow to enhance MultiIndex rows. Requires adding a usepackage{{multirow}} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module, and is set as the option styler.sparse.index.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to True.
captionstr or tuple, optional: Tuple (full_caption, short_caption), which results in \caption[short_caption]{{full_caption}}; if a single string is passed, no short caption will be set.
labelstr, optional: The LaTeX label to be placed inside \label{{}} in the output. This is used with \ref{{}} in the main .tex file.
positionstr, optional: The LaTeX positional argument for tables, to be placed after \begin{{}} in the output.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

Notes

As of v2.0.0 this method has changed to use the Styler implementation as part of Styler.to_latex() via jinja2 templating. This means that jinja2 is a requirement, and needs to be installed, for this method to function. It is advised that users switch to using Styler, since that implementation is more frequently updated and contains much more flexibility with the output.

Examples

Convert a general DataFrame to LaTeX with formatting:

>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
...                        age=[26, 45],
...                        height=[181.23, 177.65]))
>>> print(df.to_latex(index=False,
...                   formatters={"name": str.upper},
...                   float_format="{:.1f}".format,
... ))  
\begin{tabular}{lrr}
\toprule
name & age & height \\
\midrule
RAPHAEL & 26 & 181.2 \\
DONATELLO & 45 & 177.7 \\
\bottomrule
\end{tabular}

sparkle.FeatureDataFrame.to_markdown(self, buf: FilePath | WriteBuffer[str] | None = None, *, mode: str = 'wt', index: bool = True, storage_options: StorageOptions | None = None, **kwargs) → str | None

Print DataFrame in Markdown-friendly format.

Parameters

bufstr, Path or StringIO-like, optional, default None: Buffer to write to. If None, the output is returned as a string.
modestr, optional: Mode in which file is opened, “wt” by default.
indexbool, optional, default True: Add index (row) labels.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
**kwargs: These parameters will be passed to tabulate.

Returns

str: DataFrame in Markdown-friendly format.

Notes

Requires the tabulate package.

Examples

>>> df = pd.DataFrame(
...     data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
... )
>>> print(df.to_markdown())
|    | animal_1   | animal_2   |
|---:|:-----------|:-----------|
|  0 | elk        | dog        |
|  1 | pig        | quetzal    |

Output markdown with a tabulate option.

>>> print(df.to_markdown(tablefmt="grid"))
+----+------------+------------+
|    | animal_1   | animal_2   |
+====+============+============+
|  0 | elk        | dog        |
+----+------------+------------+
|  1 | pig        | quetzal    |
+----+------------+------------+

sparkle.FeatureDataFrame.to_numpy(self, dtype: npt.DTypeLike | None = None, copy: bool = False, na_value: object = <no_default>) → np.ndarray

Convert the DataFrame to a NumPy array.

By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are float16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.

Parameters

dtypestr or numpy.dtype, optional: The dtype to pass to numpy.asarray().
copybool, default False: Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.
na_valueAny, optional: The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

Returns

numpy.ndarray

Examples

>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
       [2, 4]])

With heterogeneous data, the lowest common type will have to be used.

>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
       [2. , 4.5]])

For a mix of numeric and non-numeric types, the output array will have object dtype.

>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
       [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)

Write a DataFrame to the ORC format.

Added in version 1.5.0.

Parameters

pathstr, file-like object or None, default None: If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned.
engine{‘pyarrow’}, default ‘pyarrow’: ORC library to use.
indexbool, optional: If True, include the dataframe’s index(es) in the file output. If False, they will not be written to the file. If None, similar to infer the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.
engine_kwargsdict[str, Any] or None, default None: Additional keyword arguments passed to pyarrow.orc.write_table().

Returns

bytes if no path argument is provided else None

Raises

NotImplementedError: Dtype of one or more columns is category, unsigned integers, interval, period or sparse.
ValueError: engine is not pyarrow.

Notes

Before using this function you should read the user guide about ORC and install optional dependencies.
This function requires pyarrow library.
For supported dtypes please refer to supported ORC features in Arrow.
Currently timezones in datetime columns are not preserved when a dataframe is converted into ORC files.

Examples

>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
>>> df.to_orc('df.orc')  
>>> pd.read_orc('df.orc')  
   col1  col2
0     1     4
1     2     3

If you want to get a buffer to the orc content you can write it to io.BytesIO

>>> import io
>>> b = io.BytesIO(df.to_orc())  
>>> b.seek(0)  
0
>>> content = b.read()  

Write a DataFrame to the binary parquet format.

This function writes the dataframe as a parquet file. You can choose different parquet backends, and have the option of compression. See the user guide for more details.

Parameters

pathstr, path object, file-like object, or None, default None: String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. If None, the result is returned as bytes. If a string or path, it will be used as Root Directory path when writing a partitioned dataset.
engine{‘auto’, ‘pyarrow’, ‘fastparquet’}, default ‘auto’: Parquet library to use. If ‘auto’, then the option io.parquet.engine is used. The default io.parquet.engine behavior is to try ‘pyarrow’, falling back to ‘fastparquet’ if ‘pyarrow’ is unavailable.
compressionstr or None, default ‘snappy’: Name of the compression to use. Use None for no compression. Supported options: ‘snappy’, ‘gzip’, ‘brotli’, ‘lz4’, ‘zstd’.
indexbool, default None: If True, include the dataframe’s index(es) in the file output. If False, they will not be written to the file. If None, similar to True the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.
partition_colslist, optional, default None: Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
**kwargs: Additional arguments passed to the parquet library. See pandas io for more details.

Returns

bytes if no path argument is provided else None

Notes

This function requires either the fastparquet or pyarrow library.

Examples

>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
...               compression='gzip')  
>>> pd.read_parquet('df.parquet.gzip')  
   col1  col2
0     1     3
1     2     4

If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don’t use partition_cols, which creates multiple files.

>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()

sparkle.FeatureDataFrame.to_period(self, freq: Frequency | None = None, axis: Axis = 0, copy: bool | None = None) → DataFrame

Convert DataFrame from DatetimeIndex to PeriodIndex.

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).

Parameters

freqstr, default: Frequency of the PeriodIndex.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to convert (the index by default).
copybool, default True: If False then underlying input data is not copied.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: The DataFrame has a PeriodIndex.

Examples

>>> idx = pd.to_datetime(
...     [
...         "2001-03-31 00:00:00",
...         "2002-05-31 00:00:00",
...         "2003-08-31 00:00:00",
...     ]
... )

>>> idx
DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
dtype='datetime64[ns]', freq=None)

>>> idx.to_period("M")
PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')

For the yearly frequency

>>> idx.to_period("Y")
PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')

sparkle.FeatureDataFrame.to_pickle(self, path: FilePath | WriteBuffer[bytes], *, compression: CompressionOptions = 'infer', protocol: int = 5, storage_options: StorageOptions | None = None) → None

Pickle (serialize) object to file.

Parameters

pathstr, path object, or file-like object: String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. File path where the pickled object will be stored.
compressionstr or dict, default ‘infer’: For on-the-fly compression of the output data. If ‘infer’ and ‘path’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.
protocolint: Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.

[1]
https://docs.python.org/3/library/pickle.html.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Examples

>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})  
>>> original_df  
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> original_df.to_pickle("./dummy.pkl")  

>>> unpickled_df = pd.read_pickle("./dummy.pkl")  
>>> unpickled_df  
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9

sparkle.FeatureDataFrame.to_records(self, index: bool = True, column_dtypes=None, index_dtypes=None) → recarray

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters

indexbool, default True

Include index in resulting record array, stored in ‘index’ field or using the index label, if set.

column_dtypesstr, type, dict, default None

If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types.

index_dtypesstr, type, dict, default None

If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.

This mapping is applied only if index=True.

Returns

numpy.rec.recarray: NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

Examples

>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
...                   index=['a', 'b'])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to ‘index’. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])

>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.

Parameters

namestr

Name of SQL table.

consqlalchemy.engine.(Engine or Connection) or sqlite3.Connection

Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable. See here. If passing a sqlalchemy.engine.Connection which is already in a transaction, the transaction will not be committed. If passing a sqlite3.Connection, it will not be possible to roll back the record insertion.

schemastr, optional

Specify the schema (if database flavor supports this). If None, use default schema.

if_exists{‘fail’, ‘replace’, ‘append’}, default ‘fail’

How to behave if the table already exists.

fail: Raise a ValueError.
replace: Drop the table before inserting new values.
append: Insert new values to the existing table.

indexbool, default True

Write DataFrame index as a column. Uses index_label as the column name in the table. Creates a table index for this column.

index_labelstr or sequence, default None

Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.

chunksizeint, optional

Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once.

dtypedict or scalar, optional

Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.

method{None, ‘multi’, callable}, optional

Controls the SQL insertion clause used:

None : Uses standard SQL INSERT clause (one per row).
‘multi’: Pass multiple values in a single INSERT clause.
callable with signature (pd_table, conn, keys, data_iter).

Details and a sample callable implementation can be found in the section insert method.

Returns

None or int

Number of rows affected by to_sql. None is returned if the callable passed into method does not return an integer number of rows.

The number of returned rows affected is the sum of the rowcount attribute of sqlite3.Cursor or SQLAlchemy connectable which may not reflect the exact number of written rows as stipulated in the sqlite3 or SQLAlchemy.

Added in version 1.4.0.

Raises

ValueError: When the table already exists and if_exists is ‘fail’ (the default).

Notes

Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

Not all datastores support method="multi". Oracle, for example, does not support multi-value insert.

References

Examples

Create an in-memory SQLite database.

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)

Create a table from scratch with 3 rows.

>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
     name
0  User 1
1  User 2
2  User 3

>>> df.to_sql(name='users', con=engine)
3
>>> from sqlalchemy import text
>>> with engine.connect() as conn:
...    conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

An sqlalchemy.engine.Connection can also be passed to con:

>>> with engine.begin() as connection:
...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
...     df1.to_sql(name='users', con=connection, if_exists='append')
2

This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql(name='users', con=engine, if_exists='append')
2
>>> with engine.connect() as conn:
...    conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
 (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
 (1, 'User 7')]

Overwrite the table with just df2.

>>> df2.to_sql(name='users', con=engine, if_exists='replace',
...            index_label='id')
2
>>> with engine.connect() as conn:
...    conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 6'), (1, 'User 7')]

Use method to define a callable insertion method to do nothing if there’s a primary key conflict on a table in a PostgreSQL database.

>>> from sqlalchemy.dialects.postgresql import insert
>>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
...     # "a" is the primary key in "conflict_table"
...     data = [dict(zip(keys, row)) for row in data_iter]
...     stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
...     result = conn.execute(stmt)
...     return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing)  
0

For MySQL, a callable to update columns b and c if there’s a conflict on a primary key.

>>> from sqlalchemy.dialects.mysql import insert
>>> def insert_on_conflict_update(table, conn, keys, data_iter):
...     # update columns "b" and "c" on primary key conflict
...     data = [dict(zip(keys, row)) for row in data_iter]
...     stmt = (
...         insert(table.table)
...         .values(data)
...     )
...     stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
...     result = conn.execute(stmt)
...     return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update)  
2

Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
     A
0  1.0
1  NaN
2  2.0

>>> from sqlalchemy.types import Integer
>>> df.to_sql(name='integers', con=engine, index=False,
...           dtype={"A": Integer()})
3

>>> with engine.connect() as conn:
...   conn.execute(text("SELECT * FROM integers")).fetchall()
[(1,), (None,), (2,)]

sparkle.FeatureDataFrame.to_stata(self, path: FilePath | WriteBuffer[bytes], *, convert_dates: dict[Hashable, str] | None = None, write_index: bool = True, byteorder: ToStataByteorder | None = None, time_stamp: datetime.datetime | None = None, data_label: str | None = None, variable_labels: dict[Hashable, str] | None = None, version: int | None = 114, convert_strl: Sequence[Hashable] | None = None, compression: CompressionOptions = 'infer', storage_options: StorageOptions | None = None, value_labels: dict[Hashable, dict[float, str]] | None = None) → None

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. “dta” files contain a Stata dataset.

Parameters

pathstr, path object, or buffer

String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function.

convert_datesdict

Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are ‘tc’, ‘td’, ‘tm’, ‘tw’, ‘th’, ‘tq’, ‘ty’. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to ‘tc’. Raises NotImplementedError if a datetime column has timezone information.

write_indexbool

Write the index to Stata dataset.

byteorderstr

Can be “>”, “<”, “little”, or “big”. default is sys.byteorder.

time_stampdatetime

A datetime to use as file creation date. Default is the current time.

data_labelstr, optional

A label for the data set. Must be 80 characters or smaller.

variable_labelsdict

Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller.

version{114, 117, 118, 119, None}, default 114

Version to use in the output dta file. Set to None to let pandas decide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.

Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.

convert_strllist, optional

List of column names to convert to string columns to Stata StrL format. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

value_labelsdict of dicts

Dictionary containing columns as keys and dictionaries of column value to labels as values. Labels for a single variable must be 32,000 characters or smaller.

Added in version 1.4.0.

Raises

NotImplementedError

If datetimes contain timezone information
Column dtype is not representable in Stata

ValueError

Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime
Column listed in convert_dates is not in DataFrame
Categorical label contains more than 32,000 characters

Examples

>>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',
...                               'parrot'],
...                    'speed': [350, 18, 361, 15]})
>>> df.to_stata('animals.dta')  

Render a DataFrame to a console-friendly tabular output.

Parameters

bufstr, Path or StringIO-like, optional, default None

Buffer to write to. If None, the output is returned as a string.

columnsarray-like, optional, default None

The subset of columns to write. Writes all columns by default.

col_spaceint, list or dict of int, optional

The minimum width of each column. If a list of ints is given every integers corresponds with one column. If a dict is given, the key references the column, while the value defines the space to use..

headerbool or list of str, optional

Write out the column names. If a list of columns is given, it is assumed to be aliases for the column names.

indexbool, optional, default True

Whether to print index (row) labels.

na_repstr, optional, default ‘NaN’

String representation of NaN to use.

formatterslist, tuple or dict of one-param. functions, optional

Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

float_formatone-parameter function, optional, default None

Formatter function to apply to columns’ elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.

sparsifybool, optional, default True

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_namesbool, optional, default True

Prints the names of the indexes.

justifystr, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are

left
right
center
justify
justify-all
start
end
inherit
match-parent
initial
unset.

max_rowsint, optional

Maximum number of rows to display in the console.

max_colsint, optional

Maximum number of columns to display in the console.

show_dimensionsbool, default False

Display DataFrame dimensions (number of rows by number of columns).

decimalstr, default ‘.’

Character recognized as decimal separator, e.g. ‘,’ in Europe.

line_widthint, optional

Width to wrap a line in characters.

min_rowsint, optional

The number of rows to display in the console in a truncated repr (when number of rows is above max_rows).

max_colwidthint, optional

Max width to truncate each column in characters. By default, no limit.

encodingstr, default “utf-8”

Set character encoding.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

Examples

>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
   col1  col2
0     1     4
1     2     5
2     3     6

sparkle.FeatureDataFrame.to_timestamp(self, freq: Frequency | None = None, how: ToTimestampHow = 'start', axis: Axis = 0, copy: bool | None = None) → DataFrame

Cast to DatetimeIndex of timestamps, at beginning of period.

Parameters

freqstr, default frequency of PeriodIndex: Desired frequency.
how{‘s’, ‘e’, ‘start’, ‘end’}: Convention for converting period to timestamp; start of period vs. end.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to convert (the index by default).
copybool, default True: If False then underlying input data is not copied.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: The DataFrame has a DatetimeIndex.

Examples

>>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d, index=idx)
>>> df1
      col1   col2
2023     1      3
2024     2      4

The resulting timestamps will be at the beginning of the year in this case

>>> df1 = df1.to_timestamp()
>>> df1
            col1   col2
2023-01-01     1      3
2024-01-01     2      4
>>> df1.index
DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)

Using freq which is the offset that the Timestamps will have

>>> df2 = pd.DataFrame(data=d, index=idx)
>>> df2 = df2.to_timestamp(freq='M')
>>> df2
            col1   col2
2023-01-31     1      3
2024-01-31     2      4
>>> df2.index
DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)

sparkle.FeatureDataFrame.to_xarray(self)

Return an xarray object from the pandas object.

Returns

xarray.DataArray or xarray.Dataset: Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.

Notes

See the xarray docs

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
...                    ('parrot', 'bird', 24.0, 2),
...                    ('lion', 'mammal', 80.5, 4),
...                    ('monkey', 'mammal', np.nan, 4)],
...                   columns=['name', 'class', 'max_speed',
...                            'num_legs'])
>>> df
     name   class  max_speed  num_legs
0  falcon    bird      389.0         2
1  parrot    bird       24.0         2
2    lion  mammal       80.5         4
3  monkey  mammal        NaN         4

>>> df.to_xarray()  
<xarray.Dataset>
Dimensions:    (index: 4)
Coordinates:
  * index      (index) int64 32B 0 1 2 3
Data variables:
    name       (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
    class      (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
    max_speed  (index) float64 32B 389.0 24.0 80.5 nan
    num_legs   (index) int64 32B 2 2 4 4

>>> df['max_speed'].to_xarray()  
<xarray.DataArray 'max_speed' (index: 4)>
array([389. ,  24. ,  80.5,   nan])
Coordinates:
  * index    (index) int64 0 1 2 3

>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
...                         '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
...                               'animal': ['falcon', 'parrot',
...                                          'falcon', 'parrot'],
...                               'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])

>>> df_multiindex
                   speed
date       animal
2018-01-01 falcon    350
           parrot     18
2018-01-02 falcon    361
           parrot     15

>>> df_multiindex.to_xarray()  
<xarray.Dataset>
Dimensions:  (date: 2, animal: 2)
Coordinates:
  * date     (date) datetime64[ns] 2018-01-01 2018-01-02
  * animal   (animal) object 'falcon' 'parrot'
Data variables:
    speed    (date, animal) int64 350 18 361 15

Render a DataFrame to an XML document.

Added in version 1.3.0.

Parameters

path_or_bufferstr, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.

indexbool, default True

Whether to include index in XML document.

root_namestr, default ‘data’

The name of root element in XML document.

row_namestr, default ‘row’

The name of row element in XML document.

na_repstr, optional

Missing data representation.

attr_colslist-like, optional

List of columns to write as attributes in row element. Hierarchical columns will be flattened with underscore delimiting the different levels.

elem_colslist-like, optional

List of columns to write as children in row element. By default, all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels.

namespacesdict, optional

All namespaces to be defined in root element. Keys of dict should be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example,

namespaces = {"": "https://example.com"}

prefixstr, optional

Namespace prefix to be used for every element and/or attribute in document. This should be one of the keys in namespaces dict.

encodingstr, default ‘utf-8’

Encoding of the resulting document.

xml_declarationbool, default True

Whether to include the XML declaration at start of document.

pretty_printbool, default True

Whether output should be pretty printed with indentation and line breaks.

parser{‘lxml’,’etree’}, default ‘lxml’

Parser module to use for building of tree. Only ‘lxml’ and ‘etree’ are supported. With ‘lxml’, the ability to use XSLT stylesheet is supported.

stylesheetstr, path object or file-like object, optional

A URL, file-like object, or a raw string containing an XSLT script used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requires lxml to be installed. Only XSLT 1.0 scripts and not later versions is currently supported.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buffer’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Returns

None or str: If io is None, returns the resulting XML format as a string. Otherwise returns None.

Examples

>>> df = pd.DataFrame({'shape': ['square', 'circle', 'triangle'],
...                    'degrees': [360, 360, 180],
...                    'sides': [4, np.nan, 3]})

>>> df.to_xml()  
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row>
    <index>0</index>
    <shape>square</shape>
    <degrees>360</degrees>
    <sides>4.0</sides>
  </row>
  <row>
    <index>1</index>
    <shape>circle</shape>
    <degrees>360</degrees>
    <sides/>
  </row>
  <row>
    <index>2</index>
    <shape>triangle</shape>
    <degrees>180</degrees>
    <sides>3.0</sides>
  </row>
</data>

>>> df.to_xml(attr_cols=[
...           'index', 'shape', 'degrees', 'sides'
...           ])  
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row index="0" shape="square" degrees="360" sides="4.0"/>
  <row index="1" shape="circle" degrees="360"/>
  <row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>

>>> df.to_xml(namespaces={"doc": "https://example.com"},
...           prefix="doc")  
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
  <doc:row>
    <doc:index>0</doc:index>
    <doc:shape>square</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides>4.0</doc:sides>
  </doc:row>
  <doc:row>
    <doc:index>1</doc:index>
    <doc:shape>circle</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides/>
  </doc:row>
  <doc:row>
    <doc:index>2</doc:index>
    <doc:shape>triangle</doc:shape>
    <doc:degrees>180</doc:degrees>
    <doc:sides>3.0</doc:sides>
  </doc:row>
</doc:data>

sparkle.FeatureDataFrame.transform(self, func: AggFuncType, axis: Axis = 0, *args, **kwargs) → DataFrame

Call func on self producing a DataFrame with the same axis shape as self.

Parameters

funcfunction, str, list-like or dict-like

Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.

Accepted combinations are:

function
string function name
list-like of functions and/or function names, e.g. [np.exp, 'sqrt']
dict-like of axis labels -> functions, function names or list-like of such.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns

DataFrame: A DataFrame that must have the same length as self.

Raises

ValueError : If the returned DataFrame has a different length than self.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

Examples

>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})
>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056

You can call transform on a GroupBy object:

>>> df = pd.DataFrame({
...     "Date": [
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df
         Date  Data
0  2015-05-08     5
1  2015-05-07     8
2  2015-05-06     6
3  2015-05-05     1
4  2015-05-08    50
5  2015-05-07   100
6  2015-05-06    60
7  2015-05-05   120
>>> df.groupby('Date')['Data'].transform('sum')
0     55
1    108
2     66
3    121
4     55
5    108
6     66
7    121
Name: Data, dtype: int64

>>> df = pd.DataFrame({
...     "c": [1, 1, 1, 2, 2, 2, 2],
...     "type": ["m", "n", "o", "m", "m", "n", "n"]
... })
>>> df
   c type
0  1    m
1  1    n
2  1    o
3  2    m
4  2    m
5  2    n
6  2    n
>>> df['size'] = df.groupby('c')['type'].transform(len)
>>> df
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4

sparkle.FeatureDataFrame.transpose(self, *args, copy: bool = False) → DataFrame

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property T is an accessor to the method transpose().

Parameters

*argstuple, optional

Accepted for compatibility with NumPy.

copybool, default False

Whether to copy the data after transposing, even for DataFrames with a single dtype.

Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: The transposed DataFrame.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4

>>> df1_transposed = df1.T  # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    int64
col2    int64
dtype: object
>>> df1_transposed.dtypes
0    int64
1    int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {'name': ['Alice', 'Bob'],
...       'score': [9.5, 8],
...       'employed': [False, True],
...       'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
    name  score  employed  kids
0  Alice    9.5     False     0
1    Bob    8.0      True     0

>>> df2_transposed = df2.T  # or df2.transpose()
>>> df2_transposed
              0     1
name      Alice   Bob
score       9.5   8.0
employed  False  True
kids          0     0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name         object
score       float64
employed       bool
kids          int64
dtype: object
>>> df2_transposed.dtypes
0    object
1    object
dtype: object

sparkle.FeatureDataFrame.truediv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.FeatureDataFrame.truncate(self, before=None, after=None, axis: int | Literal['index', 'columns', 'rows'] | None = None, copy: bool | None = None) → None

Truncate a Series or DataFrame before and after some index value.

This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.

Parameters

beforedate, str, int: Truncate all rows before this index value.
afterdate, str, int: Truncate all rows after this index value.
axis{0 or ‘index’, 1 or ‘columns’}, optional: Axis to truncate. Truncates the index (rows) by default. For Series this parameter is unused and defaults to 0.
copybool, default is True,: Return a copy of the truncated section.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

type of caller: The truncated Series or DataFrame.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples

>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
...                    'B': ['f', 'g', 'h', 'i', 'j'],
...                    'C': ['k', 'l', 'm', 'n', 'o']},
...                   index=[1, 2, 3, 4, 5])
>>> df
   A  B  C
1  a  f  k
2  b  g  l
3  c  h  m
4  d  i  n
5  e  j  o

>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n

The columns of a DataFrame can be truncated.

>>> df.truncate(before="A", after="B", axis="columns")
   A  B
a  f
b  g
c  h
d  i
e  j

For Series, only rows can be truncated.

>>> df['A'].truncate(before=2, after=4)
2    b
3    c
4    d
Name: A, dtype: object

The index values in truncate can be datetimes or string dates.

>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
                     A
2016-01-31 23:59:56  1
2016-01-31 23:59:57  1
2016-01-31 23:59:58  1
2016-01-31 23:59:59  1
2016-02-01 00:00:00  1

>>> df.truncate(before=pd.Timestamp('2016-01-05'),
...             after=pd.Timestamp('2016-01-10')).tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

>>> df.truncate('2016-01-05', '2016-01-10').tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Note that truncate assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

>>> df.loc['2016-01-05':'2016-01-10', :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1

sparkle.FeatureDataFrame.tz_convert(self, tz, axis: int | Literal['index', 'columns', 'rows'] = 0, level=None, copy: bool | None = None) → None

Convert tz-aware axis to target time zone.

Parameters

tzstr or tzinfo object or None: Target time zone. Passing None will convert to UTC and remove the timezone information.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to convert
levelint, str, default None: If axis is a MultiIndex, convert a specific level. Otherwise must be None.
copybool, default True: Also make a copy of the underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

Series/DataFrame: Object with time zone converted axis.

Raises

TypeError: If the axis is tz-naive.

Examples

Change to another time zone:

>>> s = pd.Series(
...     [1],
...     index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),
... )
>>> s.tz_convert('Asia/Shanghai')
2018-09-15 07:30:00+08:00    1
dtype: int64

Pass None to convert to UTC and get a tz-naive index:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_convert(None)
2018-09-14 23:30:00    1
dtype: int64

sparkle.FeatureDataFrame.tz_localize(self, tz, axis: Axis = 0, level=None, copy: bool_t | None = None, ambiguous: TimeAmbiguous = 'raise', nonexistent: TimeNonexistent = 'raise') → Self

Localize tz-naive index of a Series or DataFrame to target time zone.

This operation localizes the Index. To localize the values in a timezone-naive Series, use Series.dt.tz_localize().

Parameters

tzstr or tzinfo or None

Time zone to localize. Passing None will remove the time zone information and preserve local time.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to localize

levelint, str, default None

If axis ia a MultiIndex, localize a specific level. Otherwise must be None.

copybool, default True

Also make a copy of the underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’

When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.

‘infer’ will attempt to infer fall dst-transition hours based on order
bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
‘NaT’ will return NaT where there are ambiguous times
‘raise’ will raise an AmbiguousTimeError if there are ambiguous times.

nonexistentstr, default ‘raise’

A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are:

‘shift_forward’ will shift the nonexistent time forward to the closest existing time
‘shift_backward’ will shift the nonexistent time backward to the closest existing time
‘NaT’ will return NaT where there are nonexistent times
timedelta objects will shift nonexistent times by the timedelta
‘raise’ will raise an NonExistentTimeError if there are nonexistent times.

Returns

Series/DataFrame: Same type as the input.

Raises

TypeError: If the TimeSeries is tz-aware and tz is not None.

Examples

Localize local times:

>>> s = pd.Series(
...     [1],
...     index=pd.DatetimeIndex(['2018-09-15 01:30:00']),
... )
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00    1
dtype: int64

Pass None to convert to tz-naive index and preserve local time:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_localize(None)
2018-09-15 01:30:00    1
dtype: int64

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.Series(range(7),
...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 03:00:00',
...                                       '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00    0
2018-10-28 02:00:00+02:00    1
2018-10-28 02:30:00+02:00    2
2018-10-28 02:00:00+01:00    3
2018-10-28 02:30:00+01:00    4
2018-10-28 03:00:00+01:00    5
2018-10-28 03:30:00+01:00    6
dtype: int64

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.Series(range(3),
...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
...                                       '2018-10-28 02:36:00',
...                                       '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00    0
2018-10-28 02:36:00+02:00    1
2018-10-28 03:46:00+01:00    2
dtype: int64

If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or ‘shift_forward’ or ‘shift_backward’.

>>> s = pd.Series(range(2),
...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
...                                       '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00    0
2015-03-29 03:30:00+02:00              1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
2015-03-29 03:30:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64

sparkle.FeatureDataFrame.unstack(self, level: IndexLabel = -1, fill_value=None, sort: bool = True)

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Parameters

levelint, str, or list of these, default -1 (last level): Level(s) of index to unstack, can pass level name.
fill_valueint, str or dict: Replace NaN with this value if the unstack produces missing values.
sortbool, default True: Sort the level(s) in the resulting MultiIndex columns.

Returns

Series or DataFrame

Notes

Reference the user guide for more examples.

Examples

>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
...                                    ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64

>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0

>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0

>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64

sparkle.FeatureDataFrame.update(self, other, join: UpdateJoin = 'left', overwrite: bool = True, filter_func=None, errors: IgnoreRaise = 'ignore') → None

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters

otherDataFrame, or object coercible into a DataFrame

Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.

join{‘left’}, default ‘left’

Only left join is implemented, keeping the index and columns of the original object.

overwritebool, default True

How to handle non-NA values for overlapping keys:

True: overwrite original DataFrame’s values with values from other.
False: only update values that are NA in the original DataFrame.

filter_funccallable(1d-array) -> bool 1d-array, optional

Can choose to replace values other than NA. Return True for values that should be updated.

errors{‘raise’, ‘ignore’}, default ‘ignore’

If ‘raise’, will raise a ValueError if the DataFrame and other both contain non-NA data in the same place.

Returns

None: This method directly changes calling object.

Raises

ValueError

When errors=’raise’ and there’s overlapping non-NA data.
When errors is not either ‘ignore’ or ‘raise’

NotImplementedError

If join != ‘left’

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame’s length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  y
2  c  f

For Series, its name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e', 'f'], name='B')
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400., 500., 600.]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0

sparkle.FeatureDataFrame.value_counts(self, subset: IndexLabel | None = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True) → Series

Return a Series containing the frequency of each distinct row in the Dataframe.

Parameters

subsetlabel or list of labels, optional: Columns to use when counting unique combinations.
normalizebool, default False: Return proportions rather than frequencies.
sortbool, default True: Sort by frequencies when True. Sort by DataFrame column values when False.
ascendingbool, default False: Sort in ascending order.
dropnabool, default True: Don’t include counts of rows that contain NA values.

Added in version 1.3.0.

Returns

Series

Notes

The returned Series will have a MultiIndex with one level per input column but an Index (non-multi) for a single label. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
...                    'num_wings': [2, 0, 0, 0]},
...                   index=['falcon', 'dog', 'cat', 'ant'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0

>>> df.value_counts()
num_legs  num_wings
4         0            2
2         2            1
6         0            1
Name: count, dtype: int64

>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
Name: count, dtype: int64

>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
Name: count, dtype: int64

>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
2         2            0.25
6         0            0.25
Name: proportion, dtype: float64

With dropna set to False we can also count rows with NA values.

>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
>>> df
  first_name middle_name
0       John       Smith
1       Anne        <NA>
2       John        <NA>
3       Beth      Louise

>>> df.value_counts()
first_name  middle_name
Beth        Louise         1
John        Smith          1
Name: count, dtype: int64

>>> df.value_counts(dropna=False)
first_name  middle_name
Anne        NaN            1
Beth        Louise         1
John        Smith          1
            NaN            1
Name: count, dtype: int64

>>> df.value_counts("first_name")
first_name
John    2
Anne    1
Beth    1
Name: count, dtype: int64

sparkle.FeatureDataFrame.var(self, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs)

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters

axis{index (0), columns (1)}: For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.var with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.

Returns

Series or DataFrame (if level specified)

Examples

>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
...                    'age': [21, 25, 62, 43],
...                    'height': [1.61, 1.87, 1.49, 2.01]}
...                   ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

>>> df.var()
age       352.916667
height      0.056367
dtype: float64

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.var(ddof=0)
age       264.687500
height      0.042275
dtype: float64

sparkle.FeatureDataFrame.where(self, cond, other=nan, *, inplace: bool_t = False, axis: Axis | None = None, level: Level | None = None) → Self | None

Replace values where the condition is False.

Parameters

condbool Series/DataFrame, array-like, or callable: Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
otherscalar, Series/DataFrame, or callable: Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).
inplacebool, default False: Whether to perform the operation in place on the data.
axisint, default None: Alignment axis if needed. For Series this parameter is unused and defaults to 0.
levelint, default None: Alignment level if needed.

Returns

Same type as caller or None if inplace=True.

Notes

The where method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is True the element is used; otherwise the corresponding element from the DataFrame other is used. If the axis of other does not align with axis of cond Series/DataFrame, the misaligned index positions will be filled with False.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the where documentation in indexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)
0    99
1     1
2    99
3    99
4    99
dtype: int64

>>> s.where(s > 1, 10)
  10
  10
  2
  3
  4
dtype: int64
>>> s.mask(s > 1, 10)
   0
   1
  10
  10
  10
dtype: int64

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

sparkle.FeatureDataFrame.xs(self, key: Hashable | Sequence[Hashable], axis: int | Literal['index', 'columns', 'rows'] = 0, level: Hashable | Sequence[Hashable] | None = None, drop_level: bool = True) → None

Return cross-section from the Series/DataFrame.

This method takes a key argument to select data at a particular level of a MultiIndex.

Parameters

keylabel or tuple of label: Label contained in the index, or partially in a MultiIndex.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Axis to retrieve cross-section on.
levelobject, defaults to first n levels (n=1 or len(key)): In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position.
drop_levelbool, default True: If False, returns object with same levels as self.

Returns

Series or DataFrame: Cross-section from the original Series or DataFrame corresponding to the selected index levels.

Notes

xs can not be used to set values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see MultiIndex Slicers.

Examples

>>> d = {'num_legs': [4, 4, 2, 2],
...      'num_wings': [0, 0, 2, 2],
...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
...      'animal': ['cat', 'dog', 'bat', 'penguin'],
...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
                           num_legs  num_wings
class  animal  locomotion
mammal cat     walks              4          0
       dog     walks              4          0
       bat     flies              2          2
bird   penguin walks              2          2

Get values at specified index

>>> df.xs('mammal')
                   num_legs  num_wings
animal locomotion
cat    walks              4          0
dog    walks              4          0
bat    flies              2          2

Get values at several indexes

>>> df.xs(('mammal', 'dog', 'walks'))
num_legs     4
num_wings    0
Name: (mammal, dog, walks), dtype: int64

Get values at specified index and level

>>> df.xs('cat', level=1)
                   num_legs  num_wings
class  locomotion
mammal walks              4          0

Get values at several indexes and levels

>>> df.xs(('bird', 'walks'),
...       level=[0, 'locomotion'])
         num_legs  num_wings
animal
penguin         2          2

Get values at specified column and axis

>>> df.xs('num_wings', axis=1)
class   animal   locomotion
mammal  cat      walks         0
        dog      walks         0
        bat      flies         2
bird    penguin  walks         2
Name: num_wings, dtype: int64

FeatureGroup

FeatureSubgroup

FeatureType

FileInstanceSet

Object representation of a set of single-file instances.

sparkle.FileInstanceSet.__init__(self: FileInstanceSet, target: Path) → None

Initialise an InstanceSet, where each instance is a file in the directory.

Args:

target: Path to the instances directory. If multiple files are found,: they are assumed to have the same number of instances per file.

sparkle.FileInstanceSet.__repr__(self: InstanceSet) → str: Get detailed representation of an Instance Set.

sparkle.FileInstanceSet.__str__(self: InstanceSet) → str: Get the string representation of an Instance Set.

sparkle.FileInstanceSet.get_path_by_name(self: InstanceSet, name: str) → Path | list[Path]: Retrieves an instance paths by its name. Returns None upon failure.

InstanceSet

Instance_Set

IterableFileInstanceSet

Object representation of files containing multiple instances.

sparkle.IterableFileInstanceSet.__determine_size__(file: Path) → int: Determine the number of instances in a file.

sparkle.IterableFileInstanceSet.__init__(self: IterableFileInstanceSet, target: Path) → None

Initialise an InstanceSet from a single file.

Args:

target: Path to the instances directory. If multiple files are found,: they are assumed to have the same number of instances.

sparkle.IterableFileInstanceSet.__repr__(self: InstanceSet) → str: Get detailed representation of an Instance Set.

sparkle.IterableFileInstanceSet.__str__(self: InstanceSet) → str: Get the string representation of an Instance Set.

sparkle.IterableFileInstanceSet.get_path_by_name(self: InstanceSet, name: str) → Path | list[Path]: Retrieves an instance paths by its name. Returns None upon failure.

MultiFileInstanceSet

Object representation of a set of multi-file instances.

sparkle.MultiFileInstanceSet.__init__(self: MultiFileInstanceSet, target: Path) → None

Initialise an Instances object from a directory.

Args:: target: Path to the instances directory. Will read from instances.csv.

sparkle.MultiFileInstanceSet.__repr__(self: InstanceSet) → str: Get detailed representation of an Instance Set.

sparkle.MultiFileInstanceSet.__str__(self: InstanceSet) → str: Get the string representation of an Instance Set.

sparkle.MultiFileInstanceSet.get_path_by_name(self: InstanceSet, name: str) → Path | list[Path]: Retrieves an instance paths by its name. Returns None upon failure.

Option

PCSConverter

Path

PerformanceDataFrame

Class to manage performance data and common operations on them.

sparkle.PerformanceDataFrame.__add__(self, other)

Get Addition of DataFrame and other, column-wise.

Equivalent to DataFrame.add(other).

Parameters

otherscalar, sequence, Series, dict or DataFrame: Object to be added to the DataFrame.

Returns

DataFrame: The result of adding other to DataFrame.

Examples

>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
...                   index=['elk', 'moose'])
>>> df
       height  weight
elk       1.5     500
moose     2.6     800

Adding a scalar affects all rows and columns.

>>> df[['height', 'weight']] + 1.5
       height  weight
elk       3.0   501.5
moose     4.1   801.5

Each element of a list is added to a column of the DataFrame, in order.

>>> df[['height', 'weight']] + [0.5, 1.5]
       height  weight
elk       2.0   501.5
moose     3.1   801.5

Keys of a dictionary are aligned to the DataFrame, based on column names; each value in the dictionary is added to the corresponding column.

>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
       height  weight
elk       2.0   501.5
moose     3.1   801.5

When other is a Series, the index of other is aligned with the columns of the DataFrame.

>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
>>> df[['height', 'weight']] + s1
       height  weight
elk       3.0   500.5
moose     4.1   800.5

Even when the index of other is the same as the index of the DataFrame, the Series will not be reoriented. If index-wise alignment is desired, DataFrame.add() should be used with axis=’index’.

>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
>>> df[['height', 'weight']] + s2
       elk  height  moose  weight
elk    NaN     NaN    NaN     NaN
moose  NaN     NaN    NaN     NaN

>>> df[['height', 'weight']].add(s2, axis='index')
       height  weight
elk       2.0   500.5
moose     4.1   801.5

When other is a DataFrame, both columns names and the index are aligned.

>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
...                      index=['elk', 'moose', 'deer'])
>>> df[['height', 'weight']] + other
       height  weight
deer      NaN     NaN
elk       1.7     NaN
moose     3.0     NaN

sparkle.PerformanceDataFrame.__arrow_c_stream__(self, requested_schema=None)

Export the pandas DataFrame as an Arrow C stream PyCapsule.

This relies on pyarrow to convert the pandas DataFrame to the Arrow format (and follows the default behaviour of pyarrow.Table.from_pandas in its handling of the index, i.e. store the index as a column except for RangeIndex). This conversion is not necessarily zero-copy.

Parameters

requested_schemaPyCapsule, default None: The schema to which the dataframe should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema.

Returns

PyCapsule

sparkle.PerformanceDataFrame.__contains__(self, key) → bool: True if the key is in the info axis

sparkle.PerformanceDataFrame.__dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True) → DataFrameXchg

Return the dataframe interchange object implementing the interchange protocol.

Parameters

nan_as_nullbool, default False: nan_as_null is DEPRECATED and has no effect. Please avoid using it; it will be removed in a future release.
allow_copybool, default True: Whether to allow memory copying when exporting. If set to False it would cause non-zero-copy exports to fail.

Returns

DataFrame interchange object: The object which consuming library can use to ingress the dataframe.

Notes

Details on the interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html

Examples

>>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
>>> interchange_object = df_not_necessarily_pandas.__dataframe__()
>>> interchange_object.column_names()
Index(['A', 'B'], dtype='object')
>>> df_pandas = (pd.api.interchange.from_dataframe
...              (interchange_object.select_columns_by_name(['A'])))
>>> df_pandas
     A
0    1
1    2

These methods (column_names, select_columns_by_name) should work for any dataframe library which implements the interchange protocol.

sparkle.PerformanceDataFrame.__dataframe_consortium_standard__(self, *, api_version: str | None = None) → Any

Provide entry point to the Consortium DataFrame Standard API.

This is developed and maintained outside of pandas. Please report any issues to https://github.com/data-apis/dataframe-api-compat.

sparkle.PerformanceDataFrame.__deepcopy__(self, memo=None) → None

Parameters

memo, default None: Standard signature. Unused

sparkle.PerformanceDataFrame.__delitem__(self, key) → None: Delete item

sparkle.PerformanceDataFrame.__dir__(self) → list[str]: Provide method name lookup and completion.

Notes

Only provide ‘public’ methods.

sparkle.PerformanceDataFrame.__eq__(self, other): Return self==value.

sparkle.PerformanceDataFrame.__finalize__(self, other, method: str | None = None, **kwargs) → None

Propagate metadata from other to self.

Parameters

otherthe object from which to get the attributes that we are going: to propagate
methodstr, optional: A passed method name providing context on where __finalize__ was called.

Warning

The value passed as method are not currently considered stable across pandas releases.

sparkle.PerformanceDataFrame.__ge__(self, other): Return self>=value.

sparkle.PerformanceDataFrame.__getattr__(self, name: str): After regular attribute access, try looking up the name This allows simpler access to columns for interactive use.

sparkle.PerformanceDataFrame.__getstate__(self) → dict[str, Any]: Helper for pickle.

sparkle.PerformanceDataFrame.__gt__(self, other): Return self>value.

sparkle.PerformanceDataFrame.__init__(self: PerformanceDataFrame, csv_filepath: Path, solvers: list[str] = None, configurations: dict[str, dict[str, dict]] = None, objectives: list[str | SparkleObjective] = None, instances: list[str] = None, n_runs: int = 1) → None

Initialise a PerformanceDataFrame.

Consists of:

Columns representing the Solvers
Rows representing the result by multi-index in order of:
- Objective (Static, given in constructor or read from file)
- Instance
- Runs (Static, given in constructor or read from file)

Args:

csv_filepath: If path exists, load from Path.: Otherwise create new and save to this path.

solvers: List of solver names to be added into the Dataframe configurations: The configuration keys per solver to add, structured as

configurations[solver][config_key] = {“parameter”: “value”, ..}

objectives: List of SparkleObjectives or objective names. By default None,: then the objectives will be derived from Sparkle Settings if possible.

instances: List of instance names to be added into the Dataframe n_runs: The number of runs to consider per Solver/Objective/Instance comb.

sparkle.PerformanceDataFrame.__iter__(self) → Iterator

Iterate over info axis.

Returns

iterator: Info axis as iterator.

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> for x in df:
...     print(x)
A
B

sparkle.PerformanceDataFrame.__le__(self, other): Return self<=value.

sparkle.PerformanceDataFrame.__len__(self) → int: Returns length of info axis, but here we use the index.

sparkle.PerformanceDataFrame.__lt__(self, other): Return self<value.

sparkle.PerformanceDataFrame.__matmul__(self, other: AnyArrayLike | DataFrame) → DataFrame | Series: Matrix multiplication using binary @ operator.

sparkle.PerformanceDataFrame.__ne__(self, other): Return self!=value.

sparkle.PerformanceDataFrame.__or__(self, other): Return self|value.

sparkle.PerformanceDataFrame.__repr__(self) → str: Return a string representation for a particular DataFrame.

sparkle.PerformanceDataFrame.__rmatmul__(self, other) → DataFrame: Matrix multiplication using binary @ operator.

sparkle.PerformanceDataFrame.__ror__(self, other): Return value|self.

sparkle.PerformanceDataFrame.__setattr__(self, name: str, value) → None: After regular attribute access, try setting the name This allows simpler access to columns for interactive use.

sparkle.PerformanceDataFrame.__sizeof__(self) → int: Generates the total memory usage for an object that returns either a value or Series of values

sparkle.PerformanceDataFrame._align_for_op(self, other, axis: AxisInt, flex: bool | None = False, level: Level | None = None): Convert rhs to meet lhs dims if input is list, tuple or np.ndarray.

Parameters

left : DataFrame right : Any axis : int flex : bool or None, default False

Whether this is a flex op, in which case we reindex. None indicates not to check for alignment.

level : int or level name, default None

Returns

left : DataFrame right : Any

sparkle.PerformanceDataFrame._arith_method_with_reindex(self, right: DataFrame, op) → DataFrame: For DataFrame-with-DataFrame operations that require reindexing, operate only on shared columns, then reindex.

Parameters

right : DataFrame op : binary operator

Returns

DataFrame

sparkle.PerformanceDataFrame._as_manager(self, typ: str, copy: bool = True) → None

Private helper function to create a DataFrame with specific manager.

Parameters

typ : {“block”, “array”} copy : bool, default True

Only controls whether the conversion from Block->ArrayManager copies the 1D arrays (to ensure proper/contiguous memory layout).

Returns

DataFrame: New DataFrame using specified manager type. Is not guaranteed to be a copy or not.

sparkle.PerformanceDataFrame._box_col_values(self, values: SingleDataManager, loc: int) → Series: Provide boxed values for a column.

sparkle.PerformanceDataFrame._check_is_chained_assignment_possible(self) → bool

Check if we are a view, have a cacher, and are of mixed type. If so, then force a setitem_copy check.

Should be called just near setting a value

Will return a boolean if it we are a view and are cached, but a single-dtype meaning that the cacher should be updated following setting.

sparkle.PerformanceDataFrame._check_label_or_level_ambiguity(self, key: Hashable, axis: int | Literal['index', 'columns', 'rows'] = 0) → None

Check whether key is ambiguous.

By ambiguous, we mean that it matches both a level of the input axis and a label of the other axis.

Parameters

keyHashable: Label or level name.
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns).

Raises

ValueError: key is ambiguous

sparkle.PerformanceDataFrame._check_setitem_copy(self, t: str = 'setting', force: bool = False): Parameters

t : str, the type of setting error force : bool, default False

If True, then force showing an error.

validate if we are doing a setitem on a chained copy.

It is technically possible to figure out that we are setting on a copy even WITH a multi-dtyped pandas object. In other words, some blocks may be views while other are not. Currently _is_view will ALWAYS return False for multi-blocks to avoid having to handle this case.

df = DataFrame(np.arange(0,9), columns=[‘count’]) df[‘group’] = ‘b’

# This technically need not raise SettingWithCopy if both are view # (which is not generally guaranteed but is usually True. However, # this is in general not a good practice and we recommend using .loc. df.iloc[0:5][‘group’] = ‘a’

sparkle.PerformanceDataFrame._consolidate(self): Compute NDFrame with “consolidated” internals (data of each dtype grouped together in a single ndarray).

Returns

consolidated : same type as caller

sparkle.PerformanceDataFrame._consolidate_inplace(self) → None: Consolidate data in place and return None

sparkle.PerformanceDataFrame._construct_axes_dict(self, axes: Sequence[Axis] | None = None, **kwargs): Return an axes dictionary for myself.

sparkle.PerformanceDataFrame._construct_result(self, result) → DataFrame: Wrap the result of an arithmetic, comparison, or logical operation.

Parameters

result : DataFrame

Returns

DataFrame

sparkle.PerformanceDataFrame._create_data_for_split_and_tight_to_dict(self, are_all_object_dtype_cols: bool, object_dtype_indices: list[int]) → list: Simple helper method to create data for to to_dict(orient="split") and to_dict(orient="tight") to create the main output data

sparkle.PerformanceDataFrame._dir_additions(self) → set[str]: add the string-like attributes from the info_axis. If info_axis is a MultiIndex, its first level values are used.

sparkle.PerformanceDataFrame._dir_deletions(self) → set[str]: Delete unwanted __dir__ for this object.

sparkle.PerformanceDataFrame._dispatch_frame_op(self, right, func: Callable, axis: AxisInt | None = None) → DataFrame: Evaluate the frame operation func(left, right) by evaluating column-by-column, dispatching to the Series implementation.

Parameters

right : scalar, Series, or DataFrame func : arithmetic or comparison operator axis : {None, 0, 1}

Returns

DataFrame

Notes

Caller is responsible for setting np.errstate where relevant.

sparkle.PerformanceDataFrame._drop_axis(self, labels, axis, level=None, errors: Literal['ignore', 'raise'] = 'raise', only_slice: bool = False) → None

Drop labels from specified axis. Used in the drop method internally.

Parameters

labels : single label or list-like axis : int or axis name level : int or level name, default None

For MultiIndex

errors{‘ignore’, ‘raise’}, default ‘raise’: If ‘ignore’, suppress error and existing labels are dropped.
only_slicebool, default False: Whether indexing along columns should be view-only.

sparkle.PerformanceDataFrame._drop_labels_or_levels(self, keys, axis: int = 0)

Drop labels and/or levels for the given axis.

For each key in keys:

(axis=0): If key matches a column label then drop the column. Otherwise if key matches an index level then drop the level.
(axis=1): If key matches an index label then drop the row. Otherwise if key matches a column level then drop the level.

Parameters

keysstr or list of str: labels or levels to drop
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

dropped: DataFrame

Raises

ValueError: if any keys match neither a label nor a level

sparkle.PerformanceDataFrame._ensure_valid_index(self, value) → None: Ensure that if we don’t have an index, that we can create one from the passed value.

sparkle.PerformanceDataFrame._find_valid_index(self, *, how: str) → Hashable | None

Retrieves the index of the first valid value.

Parameters

how{‘first’, ‘last’}: Use this parameter to change between the first or last valid index.

Returns

idx_first_valid : type of index

sparkle.PerformanceDataFrame._get_agg_axis(self, axis_num: int) → Index: Let’s be explicit about this.

sparkle.PerformanceDataFrame._get_cleaned_column_resolvers(self) → dict[Hashable, Series]

Return the special character free column resolvers of a dataframe.

Column names with special characters are ‘cleaned up’ so that they can be referred to by backtick quoting. Used in DataFrame.eval().

sparkle.PerformanceDataFrame._get_column_array(self, i: int) → ArrayLike

Get the values of the i’th column (ndarray or ExtensionArray, as stored in the Block)

Warning! The returned array is a view but doesn’t handle Copy-on-Write, so this should be used with caution (for read-only purposes).

sparkle.PerformanceDataFrame._get_item_cache(self, item: Hashable) → Series: Return the cached item, item represents a label indexer.

sparkle.PerformanceDataFrame._get_label_or_level_values(self, key: Hashable, axis: int = 0) → ExtensionArray | ndarray

Return a 1-D array of values associated with key, a label or level from the given axis.

Retrieval logic:

(axis=0): Return column values if key matches a column label. Otherwise return index level values if key matches an index level.
(axis=1): Return row values if key matches an index label. Otherwise return column level values if ‘key’ matches a column level

Parameters

keyHashable: Label or level name.
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

np.ndarray or ExtensionArray

Raises

KeyError: if key matches neither a label nor a level
ValueError: if key matches multiple labels

sparkle.PerformanceDataFrame._get_value(self, index, col, takeable: bool = False) → Scalar: Quickly retrieve single value at passed column and index.

Parameters

index : row label col : column label takeable : interpret the index/col as indexers, default False

Returns

scalar

Notes

Assumes that both self.index._index_as_unique and self.columns._index_as_unique; Caller is responsible for checking.

sparkle.PerformanceDataFrame._getitem_nocopy(self, key: list): Behaves like __getitem__, but returns a view in cases where __getitem__ would make a copy.

sparkle.PerformanceDataFrame._getitem_slice(self, key: slice) → None: __getitem__ for the case where the key is a slice object.

sparkle.PerformanceDataFrame._gotitem(self, key: IndexLabel, ndim: int, subset: DataFrame | Series | None = None) → DataFrame | Series

Sub-classes to define. Return a sliced object.

Parameters

key : string / list of selections ndim : {1, 2}

requested ndim of result

subsetobject, default None: subset to act on

sparkle.PerformanceDataFrame._info_repr(self) → bool: True if the repr should show the info view.

sparkle.PerformanceDataFrame._inplace_method(self, other, op) → None: Wrap arithmetic method to operate inplace.

sparkle.PerformanceDataFrame._is_label_or_level_reference(self, key: Hashable, axis: int = 0) → bool

Test whether a key is a label or level reference for a given axis.

To be considered either a label or a level reference, key must be a string that:

(axis=0): Matches a column label or an index level

(axis=1): Matches an index label or a column level

Parameters

keyHashable: Potential label or level name
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

bool

sparkle.PerformanceDataFrame._is_label_reference(self, key: Hashable, axis: int | Literal['index', 'columns', 'rows'] = 0) → bool

Test whether a key is a label reference for a given axis.

To be considered a label reference, key must be a string that:

(axis=0): Matches a column label
(axis=1): Matches an index label

Parameters

keyHashable: Potential label name, i.e. Index entry.
axisint, default 0: Axis perpendicular to the axis that labels are associated with (0 means search for column labels, 1 means search for index labels)

Returns

is_label: bool

sparkle.PerformanceDataFrame._is_level_reference(self, key: Hashable, axis: int | Literal['index', 'columns', 'rows'] = 0) → bool

Test whether a key is a level reference for a given axis.

To be considered a level reference, key must be a string that:

(axis=0): Matches the name of an index level and does NOT match a column label.
(axis=1): Matches the name of a column level and does NOT match an index label.

Parameters

keyHashable: Potential level name for the given axis
axisint, default 0: Axis that levels are associated with (0 for index, 1 for columns)

Returns

is_level : bool

sparkle.PerformanceDataFrame._iter_column_arrays(self) → Iterator[ArrayLike]

Iterate over the arrays of all columns in order. This returns the values as stored in the Block (ndarray or ExtensionArray).

Warning! The returned array is a view but doesn’t handle Copy-on-Write, so this should be used with caution (for read-only purposes).

sparkle.PerformanceDataFrame._ixs(self, i: int, axis: AxisInt = 0) → Series: Parameters

i : int axis : int

Returns

Series

sparkle.PerformanceDataFrame._maybe_align_series_as_frame(self, series: Series, axis: AxisInt): If the Series operand is not EA-dtype, we can broadcast to 2D and operate blockwise.

sparkle.PerformanceDataFrame._maybe_cache_changed(self, item, value: Series, inplace: bool) → None: The object has called back to us saying maybe it has changed.

sparkle.PerformanceDataFrame._maybe_update_cacher(self, clear: bool = False, verify_is_copy: bool = True, inplace: bool = False) → None

See if we need to update our parent cacher if clear, then clear our cache.

Parameters

clearbool, default False: Clear the item cache.
verify_is_copybool, default True: Provide is_copy checks.

sparkle.PerformanceDataFrame._needs_reindex_multi(self, axes, method, level: Hashable | None) → bool: Check if we do need a multi reindex.

sparkle.PerformanceDataFrame._protect_consolidate(self, f): Consolidate _mgr – if the blocks have changed, then clear the cache

sparkle.PerformanceDataFrame._reduce_axis1(self, name: str, func, skipna: bool) → Series

Special case for _reduce to try to avoid a potentially-expensive transpose.

Apply the reduction block-wise along axis=1 and then reduce the resulting 1D arrays.

sparkle.PerformanceDataFrame._reindex_axes(self, axes, level: Level | None, limit: int | None, tolerance, method, fill_value: Scalar | None, copy: bool_t | None) → Self: Perform the reindex for all the axes.

sparkle.PerformanceDataFrame._reindex_multi(self, axes: dict[str, Index], copy: bool, fill_value) → DataFrame: We are guaranteed non-Nones in the axes.

sparkle.PerformanceDataFrame._reindex_with_indexers(self, reindexers, fill_value=None, copy: bool | None = False, allow_dups: bool = False) → None: allow_dups indicates an internal call here

sparkle.PerformanceDataFrame._replace_columnwise(self, mapping: dict[Hashable, tuple[Any, Any]], inplace: bool, regex)

Dispatch to Series.replace column-wise.

Parameters

mappingdict: of the form {col: (target, value)}

inplace : bool regex : bool or same types as to_replace in DataFrame.replace

Returns

DataFrame or None

sparkle.PerformanceDataFrame._repr_data_resource_(self): Not a real Jupyter special repr method, but we use the same naming convention.

sparkle.PerformanceDataFrame._repr_fits_horizontal_(self) → bool: Check if full repr fits in horizontal boundaries imposed by the display options width and max_columns.

sparkle.PerformanceDataFrame._repr_fits_vertical_(self) → bool: Check length against max_rows.

sparkle.PerformanceDataFrame._repr_html_(self) → str | None

Return a html representation for a particular DataFrame.

Mainly for IPython notebook.

sparkle.PerformanceDataFrame._repr_latex_(self): Returns a LaTeX representation for a particular object. Mainly for use with nbconvert (jupyter notebook conversion to pdf).

sparkle.PerformanceDataFrame._reset_cache(self, key: str | None = None) → None: Reset cached properties. If key is passed, only clears that key.

sparkle.PerformanceDataFrame._reset_cacher(self) → None: Reset the cacher.

sparkle.PerformanceDataFrame._sanitize_column(self, value) → tuple[ArrayLike, BlockValuesRefs | None]: Ensures new columns (which go into the BlockManager as new blocks) are always copied (or a reference is being tracked to them under CoW) and converted into an array.

Parameters

value : scalar, Series, or array-like

Returns

tuple of numpy.ndarray or ExtensionArray and optional BlockValuesRefs

sparkle.PerformanceDataFrame._set_axis(self, axis: AxisInt, labels: AnyArrayLike | list) → None: This is called from the cython code when we set the index attribute directly, e.g. series.index = [1, 2, 3].

sparkle.PerformanceDataFrame._set_axis_name(self, name, axis: int | Literal['index', 'columns', 'rows'] = 0, inplace: bool = False, copy: bool | None = True)

Set the name(s) of the axis.

Parameters

namestr or list of str: Name(s) to set.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to set the label. The value 0 or ‘index’ specifies index, and the value 1 or ‘columns’ specifies columns.
inplacebool, default False: If True, do operation inplace and return None.
copy:: Whether to make a copy of the result.

Returns

Series, DataFrame, or None: The same type as the caller or None if inplace is True.

Examples

>>> df = pd.DataFrame({"num_legs": [4, 4, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs
dog            4
cat            4
monkey         2
>>> df._set_axis_name("animal")
        num_legs
animal
dog            4
cat            4
monkey         2
>>> df.index = pd.MultiIndex.from_product(
...                [["mammal"], ['dog', 'cat', 'monkey']])
>>> df._set_axis_name(["type", "name"])
               num_legs
type   name
mammal dog        4
       cat        4
       monkey     2

sparkle.PerformanceDataFrame._set_item(self, key, value) → None

Add series to DataFrame in specified column.

If series is a numpy-array (not a Series/TimeSeries), it must be the same length as the DataFrames index or an error will be thrown.

Series/TimeSeries will be conformed to the DataFrames index to ensure homogeneity.

sparkle.PerformanceDataFrame._set_value(self, index: IndexLabel, col, value: Scalar, takeable: bool = False) → None

Put single value at passed column and index.

Parameters

indexLabel: row label
colLabel: column label

value : scalar takeable : bool, default False

Sets whether or not index/col interpreted as indexers

sparkle.PerformanceDataFrame._should_reindex_frame_op(self, right, op, axis: int, fill_value, level) → bool: Check if this is an operation between DataFrames that will need to reindex.

sparkle.PerformanceDataFrame._slice(self, slobj: slice, axis: int = 0) → None

Construct a slice of this container.

Slicing with this method is always positional.

sparkle.PerformanceDataFrame._take_with_is_copy(self, indices, axis: int | Literal['index', 'columns', 'rows'] = 0) → None

Internal version of the take method that sets the _is_copy attribute to keep track of the parent dataframe (using in indexing for the SettingWithCopyWarning).

For Series this does the same as the public take (it never sets _is_copy).

See the docstring of take for full explanation of the parameters.

sparkle.PerformanceDataFrame._to_dict_of_blocks(self)

Return a dict of dtype -> Constructor Types that each is a homogeneous dtype.

Internal ONLY - only works for BlockManager

Render object to a LaTeX tabular, longtable, or nested table.

Uses the Styler implementation with the following, ordered, method chaining:

Parameters

bufstr, Path or StringIO-like, optional, default None: Buffer to write to. If None, the output is returned as a string.
hidedict, list of dict: Keyword args to pass to the method call of Styler.hide. If a list will call the method numerous times.
relabel_indexdict, list of dict: Keyword args to pass to the method of Styler.relabel_index. If a list will call the method numerous times.
formatdict, list of dict: Keyword args to pass to the method call of Styler.format. If a list will call the method numerous times.
format_indexdict, list of dict: Keyword args to pass to the method call of Styler.format_index. If a list will call the method numerous times.
render_kwargsdict: Keyword args to pass to the method call of Styler.to_latex.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

sparkle.PerformanceDataFrame._update_inplace(self, result, verify_is_copy: bool = True) → None: Replace self internals with result.

Parameters

result : same type as self verify_is_copy : bool, default True

Provide is_copy checks.

sparkle.PerformanceDataFrame._where(self, cond, other=<no_default>, inplace: bool = False, axis: int | ~typing.Literal['index', 'columns', 'rows'] | None = None, level=None, warn: bool = True): Equivalent to public method where, except that other is not applied as a function even if callable. Used in __setitem__.

sparkle.PerformanceDataFrame.abs(self) → None

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns

abs: Series/DataFrame containing the absolute value of each element.

Notes

For complex inputs, 1.2 + 1j, the absolute value is \(\sqrt{ a^2 + b^2 }\).

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0   1 days
dtype: timedelta64[ns]

Select rows with data closest to certain value using argsort (from StackOverflow).

>>> df = pd.DataFrame({
...     'a': [4, 5, 6, 7],
...     'b': [10, 20, 30, 40],
...     'c': [100, 50, -30, -50]
... })
>>> df
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50

sparkle.PerformanceDataFrame.add(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Addition of dataframe and other, element-wise (binary operator add).

Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, radd.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.add_configuration(self: PerformanceDataFrame, solver: str, configuration_id: str | list[str], configuration: dict[str, Any] | list[dict[str, Any]] = None) → None

Add new configurations for a solver to the dataframe.

If the key already exists, update the value.

Args:: solver: The name of the solver to be added. configuration_id: The name of the configuration to be added. configuration: The configuration to be added.

sparkle.PerformanceDataFrame.add_instance(self: PerformanceDataFrame, instance_name: str, initial_values: Any | list[Any] = None) → None

Add and instance to the DataFrame.

Args:: instance_name: The name of the instance to be added. initial_values: The values assigned for each index of the new instance.

If list, must match the column dimension (Value, Seed, Configuration).

sparkle.PerformanceDataFrame.add_objective(self: PerformanceDataFrame, objective_name: str, initial_value: float = None) → None: Add an objective to the DataFrame.

sparkle.PerformanceDataFrame.add_prefix(self, prefix: str, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters

prefixstr: The string to add before each label.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Axis to add prefix on

Added in version 2.0.0.

Returns

Series or DataFrame: New Series or DataFrame with updated labels.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_prefix('item_')
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_prefix('col_')
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6

sparkle.PerformanceDataFrame.add_runs(self: PerformanceDataFrame, num_extra_runs: int, instance_names: list[str] = None, initial_values: Any | list[Any] = None) → None

Add runs to the DataFrame.

Args:

num_extra_runs: The number of runs to be added. instance_names: The instances for which runs are to be added.

By default None, which means runs are added to all instances.

initial_values: The initial value for each objective of each new run.: If a list, needs to have a value for Value, Seed and Configuration.

sparkle.PerformanceDataFrame.add_solver(self: PerformanceDataFrame, solver_name: str, configurations: list[str, dict] = None, initial_value: float | list[str | float] = None) → None

Add a new solver to the dataframe. Initializes value to None by default.

Args:: solver_name: The name of the solver to be added. configurations: A list of configuration keys for the solver. initial_value: The value assigned for each index of the new solver.

If not None, must match the index dimension (n_obj * n_inst * n_runs).

sparkle.PerformanceDataFrame.add_suffix(self, suffix: str, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters

suffixstr: The string to add after each label.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Axis to add suffix on

Added in version 2.0.0.

Returns

Series or DataFrame: New Series or DataFrame with updated labels.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_suffix('_item')
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_suffix('_col')
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6

sparkle.PerformanceDataFrame.agg(self, func=None, axis: Axis = 0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters

funcfunction, str, list or dict

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:

function
string function name
list of functions and/or function names, e.g. [np.sum, 'mean']
dict of axis labels -> functions, function names or list of such.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns

scalar, Series or DataFrame

The return can be:

scalar : when Series.agg is called with single function

Series : when DataFrame.agg is called with a single function

DataFrame : when DataFrame.agg is called with several functions

Notes

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d) as opposed to numpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples

>>> df = pd.DataFrame([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64

sparkle.PerformanceDataFrame.aggregate(self, func=None, axis: Axis = 0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters

funcfunction, str, list or dict

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:

function
string function name
list of functions and/or function names, e.g. [np.sum, 'mean']
dict of axis labels -> functions, function names or list of such.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns

scalar, Series or DataFrame

The return can be:

scalar : when Series.agg is called with single function

Series : when DataFrame.agg is called with a single function

DataFrame : when DataFrame.agg is called with several functions

Notes

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d) as opposed to numpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples

>>> df = pd.DataFrame([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters

other : DataFrame or Series join : {‘outer’, ‘inner’, ‘left’, ‘right’}, default ‘outer’

Type of alignment to be performed.

left: use only keys from left frame, preserve key order.

right: use only keys from right frame, preserve key order.

outer: use union of keys from both frames, sort keys lexicographically.

inner: use intersection of keys from both frames, preserve the order of the left keys.

axisallowed axis of the other object, default None

Align on index (0), columns (1), or both (None).

levelint or level name, default None

Broadcast across a level, matching Index values on the passed MultiIndex level.

copybool, default True

Always returns new objects. If copy=False and no reindexing is required then original objects are returned.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

fill_valuescalar, default np.nan

Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

method{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None

Method to use for filling holes in reindexed Series:

pad / ffill: propagate last valid observation forward to next valid.
backfill / bfill: use NEXT valid observation to fill gap.

Deprecated since version 2.1.

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

Deprecated since version 2.1.

fill_axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame, default 0

Filling axis, method and limit.

Deprecated since version 2.1.

broadcast_axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame, default None

Broadcast values along this axis, if aligning two objects of different dimensions.

Deprecated since version 2.1.

Returns

tuple of (Series/DataFrame, type of other): Aligned objects.

Examples

>>> df = pd.DataFrame(
...     [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
... )
>>> other = pd.DataFrame(
...     [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
...     columns=["A", "B", "C", "D"],
...     index=[2, 3, 4],
... )
>>> df
   D  B  E  A
1  1  2  3  4
2  6  7  8  9
>>> other
    A    B    C    D
2   10   20   30   40
3   60   70   80   90
4  600  700  800  900

Align on columns:

>>> left, right = df.align(other, join="outer", axis=1)
>>> left
   A  B   C  D  E
1  4  2 NaN  1  3
2  9  7 NaN  6  8
>>> right
    A    B    C    D   E
2   10   20   30   40 NaN
3   60   70   80   90 NaN
4  600  700  800  900 NaN

We can also align on the index:

>>> left, right = df.align(other, join="outer", axis=0)
>>> left
    D    B    E    A
1  1.0  2.0  3.0  4.0
2  6.0  7.0  8.0  9.0
3  NaN  NaN  NaN  NaN
4  NaN  NaN  NaN  NaN
>>> right
    A      B      C      D
1    NaN    NaN    NaN    NaN
2   10.0   20.0   30.0   40.0
3   60.0   70.0   80.0   90.0
4  600.0  700.0  800.0  900.0

Finally, the default axis=None will align on both index and columns:

>>> left, right = df.align(other, join="outer", axis=None)
>>> left
     A    B   C    D    E
1  4.0  2.0 NaN  1.0  3.0
2  9.0  7.0 NaN  6.0  8.0
3  NaN  NaN NaN  NaN  NaN
4  NaN  NaN NaN  NaN  NaN
>>> right
       A      B      C      D   E
1    NaN    NaN    NaN    NaN NaN
2   10.0   20.0   30.0   40.0 NaN
3   60.0   70.0   80.0   90.0 NaN
4  600.0  700.0  800.0  900.0 NaN

sparkle.PerformanceDataFrame.all(self, axis: Axis | None = 0, bool_only: bool = False, skipna: bool = True, **kwargs) → Series | bool

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.

bool_onlybool, default False

Include only boolean columns. Not implemented for Series.

skipnabool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

**kwargsany, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: If level is specified, then, DataFrame is returned; otherwise, Series is returned.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if values in each column all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if values in each row all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False

sparkle.PerformanceDataFrame.any(self, *, axis: Axis | None = 0, bool_only: bool = False, skipna: bool = True, **kwargs) → Series | bool

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.

bool_onlybool, default False

Include only boolean columns. Not implemented for Series.

skipnabool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

**kwargsany, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: If level is specified, then, DataFrame is returned; otherwise, Series is returned.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
   A  B  C
0  1  0  0
1  2  2  0

>>> df.any()
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2

>>> df.any(axis='columns')
0    True
1    True
dtype: bool

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0

>>> df.any(axis='columns')
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)

sparkle.PerformanceDataFrame.apply(self, func: AggFuncType, axis: Axis = 0, raw: bool = False, result_type: Literal['expand', 'reduce', 'broadcast'] | None = None, args=(), by_row: Literal[False, 'compat'] = 'compat', engine: Literal['python', 'numba'] = 'python', engine_kwargs: dict[str, bool] | None = None, **kwargs)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

Parameters

funcfunction

Function to apply to each column or row.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Axis along which the function is applied:

0 or ‘index’: apply function to each column.
1 or ‘columns’: apply function to each row.

rawbool, default False

Determines if row or column is passed as a Series or ndarray object:

False : passes each row or column as a Series to the function.
True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.

result_type{‘expand’, ‘reduce’, ‘broadcast’, None}, default None

These only act when axis=1 (columns):

‘expand’ : list-like results will be turned into columns.
‘reduce’ : returns a Series if possible rather than expanding list-like results. This is the opposite of ‘expand’.
‘broadcast’ : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.

The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.

argstuple

Positional arguments to pass to func in addition to the array/series.

by_rowFalse or “compat”, default “compat”

Only has an effect when func is a listlike or dictlike of funcs and the func isn’t a string. If “compat”, will if possible first translate the func into pandas methods (e.g. Series().apply(np.sum) will be translated to Series().sum()). If that doesn’t work, will try call to apply again with by_row=True and if that fails, will call apply again with by_row=False (backward compatible). If False, the funcs will be passed the whole Series at once.

Added in version 2.1.0.

engine{‘python’, ‘numba’}, default ‘python’

Choose between the python (default) engine or the numba engine in apply.

The numba engine will attempt to JIT compile the passed function, which may result in speedups for large DataFrames. It also supports the following engine_kwargs :

nopython (compile the function in nopython mode)
nogil (release the GIL inside the JIT compiled function)
parallel (try to apply the function in parallel over the DataFrame)

Note: Due to limitations within numba/how pandas interfaces with numba, you should only use this if raw=True

Note: The numba compiler only supports a subset of valid Python/numpy operations.

Please read more about the supported python features and supported numpy features in numba to learn what you can or cannot use in the passed function.

Added in version 2.2.0.

engine_kwargsdict

Pass keyword arguments to the engine. This is currently only used by the numba engine, see the documentation for the engine argument for more information.

**kwargs

Additional keyword arguments to pass as keywords arguments to func.

Returns

Series or DataFrame: Result of applying func along the given axis of the DataFrame.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

Examples

>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
   A  B
0  4  9
1  4  9
2  4  9

Using a numpy universal function (in this case the same as np.sqrt(df)):

>>> df.apply(np.sqrt)
     A    B
0  2.0  3.0
1  2.0  3.0
2  2.0  3.0

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A    12
B    27
dtype: int64

>>> df.apply(np.sum, axis=1)
0    13
1    13
2    13
dtype: int64

Returning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
1
1  2
1  2
1  2

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2

Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.

>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
   A  B
0  1  2
1  1  2
2  1  2

sparkle.PerformanceDataFrame.applymap(self, func: PythonFuncType, na_action: NaAction | None = None, **kwargs) → DataFrame

Apply a function to a Dataframe elementwise.

Deprecated since version 2.1.0: DataFrame.applymap has been deprecated. Use DataFrame.map instead.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters

funccallable: Python function, returns a single value from a single value.
na_action{None, ‘ignore’}, default None: If ‘ignore’, propagate NaN values, without passing them to func.
**kwargs: Additional keyword arguments to pass as keywords arguments to func.

Returns

DataFrame: Transformed DataFrame.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.map(lambda x: len(str(x)))
1
3  4
5  5

sparkle.PerformanceDataFrame.asfreq(self, freq: Frequency, method: FillnaOptions | None = None, how: Literal['start', 'end'] | None = None, normalize: bool_t = False, fill_value: Hashable | None = None) → Self

Convert time series to specified frequency.

Returns the original data conformed to a new index with the specified frequency.

If the index of this Series/DataFrame is a PeriodIndex, the new index is the result of transforming the original index with PeriodIndex.asfreq (so the original index will map one-to-one to the new index).

Otherwise, the new index will be equivalent to pd.date_range(start, end, freq=freq) where start and end are, respectively, the first and last entries in the original index (see pandas.date_range()). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see the method parameter below).

The resample() method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.

Parameters

freqDateOffset or str

Frequency DateOffset or string.

method{‘backfill’/’bfill’, ‘pad’/’ffill’}, default None

Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):

‘pad’ / ‘ffill’: propagate last valid observation forward to next valid
‘backfill’ / ‘bfill’: use NEXT valid observation to fill.

how{‘start’, ‘end’}, default end

For PeriodIndex only (see PeriodIndex.asfreq).

normalizebool, default False

Whether to reset output index to midnight.

fill_valuescalar, optional

Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).

Returns

Series/DataFrame: Series/DataFrame object reindexed to the specified frequency.

Notes

To learn more about the frequency strings, please see this link.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='min')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s': series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30s')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30s', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30s', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0

sparkle.PerformanceDataFrame.asof(self, where, subset=None)

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters

wheredate or array-like of dates: Date(s) before which the last row(s) are returned.
subsetstr or array-like of str, default None: For DataFrame, if not None, only use these columns to check for NaNs.

Returns

scalar, Series, or DataFrame

The return can be:

scalar : when self is a Series and where is a scalar

Series: when self is a Series and where is an array-like, or when self is a DataFrame and where is a scalar

DataFrame : when self is a DataFrame and where is an array-like

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64

>>> s.asof(20)
2.0

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
2.0

Take all columns into consideration

>>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
...                    'b': [None, None, None, None, 500]},
...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
...                                           '2018-02-27 09:02:00',
...                                           '2018-02-27 09:03:00',
...                                           '2018-02-27 09:04:00',
...                                           '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']),
...         subset=['a'])
                        a   b
2018-02-27 09:03:30  30.0 NaN
2018-02-27 09:04:30  40.0 NaN

sparkle.PerformanceDataFrame.assign(self, **kwargs) → DataFrame

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters

**kwargsdict of {str: callable or Series}: The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

Returns

DataFrame: A new DataFrame with the new columns in addition to all the existing columns.

Notes

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Examples

>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15

sparkle.PerformanceDataFrame.astype(self, dtype, copy: bool | None = None, errors: Literal['ignore', 'raise'] = 'raise') → None

Cast a pandas object to a specified dtype dtype.

Parameters

dtypestr, data type, Series or Mapping of column name -> data type

Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.

copybool, default True

Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

errors{‘raise’, ‘ignore’}, default ‘raise’

Control raising of exceptions on invalid data for provided dtype.

raise : allow exceptions to be raised
ignore : suppress exceptions. On error return original object.

Returns

same type as caller

Notes

Changed in version 2.0.0: Using astype to convert from timezone-naive dtype to timezone-aware dtype will raise an exception. Use Series.dt.tz_localize() instead.

Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype('int32').dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({'col1': 'int32'}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype('int64')
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')
0    1
1    2
dtype: category
Categories (2, int32): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
...     categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[ns]

sparkle.PerformanceDataFrame.at_time(self, time, asof: bool = False, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Select values at particular time of day (e.g., 9:30AM).

Parameters

timedatetime.time or str: The values to select.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: For Series this parameter is unused and defaults to 0.

Returns

Series or DataFrame

Raises

TypeError: If the index is not a DatetimeIndex

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='12h')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-09 12:00:00  2
2018-04-10 00:00:00  3
2018-04-10 12:00:00  4

>>> ts.at_time('12:00')
                     A
2018-04-09 12:00:00  2
2018-04-10 12:00:00  4

Fill NA/NaN values by using the next valid observation to fill the gap.

Deprecated since version 2.0: Series/DataFrame.backfill is deprecated. Use Series/DataFrame.bfill instead.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

Please see examples for DataFrame.bfill() or Series.bfill().

sparkle.PerformanceDataFrame.best_configuration(self: PerformanceDataFrame, solver: str, objective: SparkleObjective = None, instances: list[str] = None) → tuple[str, float]

Return the best configuration for the given objective over the instances.

Args:: solver: The solver for which we determine the best configuration objective: The objective for which we calculate the best configuration instances: The instances which should be selected for the evaluation
Returns:: The best configuration id and its aggregated performance.

sparkle.PerformanceDataFrame.best_instance_performance(self: PerformanceDataFrame, objective: str | SparkleObjective = None, instances: list[str] = None, run_id: int = None, exclude_solvers: list[str, str] = None) → Series

Return the best performance for each instance in the portfolio.

Args:

objective: The objective for which we calculate the best performance instances: The instances which should be selected for the evaluation run_id: The run for which we calculate the best performance. If None,

we consider all runs.

exclude_solvers: List of (solver, config_id) to exclude in the calculation.

Returns:

The best performance for each instance in the portfolio.

sparkle.PerformanceDataFrame.best_performance(self: PerformanceDataFrame, exclude_solvers: list[str, str] = [], instances: list[str] = None, objective: str | SparkleObjective = None) → float

Return the overall best performance of the portfolio.

Args:

exclude_solvers: List of (solver, config_id) to exclude in the calculation.: Defaults to none.
instances: The instances which should be selected for the evaluation: If None, use all instances.

objective: The objective for which we calculate the best performance

Returns:

The aggregated best performance of the portfolio over all instances.

sparkle.PerformanceDataFrame.between_time(self, start_time, end_time, inclusive: Literal['left', 'right', 'both', 'neither'] = 'both', axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Select values between particular times of the day (e.g., 9:00-9:30 AM).

By setting start_time to be later than end_time, you can get the times that are not between the two times.

Parameters

start_timedatetime.time or str: Initial time as a time filter limit.
end_timedatetime.time or str: End time as a time filter limit.
inclusive{“both”, “neither”, “left”, “right”}, default “both”: Include boundaries; whether to set each bound as closed or open.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Determine range time on index or columns value. For Series this parameter is unused and defaults to 0.

Returns

Series or DataFrame: Data from the original object filtered to the specified dates range.

Raises

TypeError: If the index is not a DatetimeIndex

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4

>>> ts.between_time('0:15', '0:45')
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are not between two times by setting start_time later than end_time:

>>> ts.between_time('0:45', '0:15')
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4

Fill NA/NaN values by using the next valid observation to fill the gap.

Parameters

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

limit_area{None, ‘inside’, ‘outside’}, default None

If limit is specified, consecutive NaNs will be filled with this restriction.

None: No fill restriction.
‘inside’: Only fill NaNs surrounded by valid values (interpolate).
‘outside’: Only fill NaNs outside valid values (extrapolate).

Added in version 2.2.0.

downcastdict, default is None

A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Deprecated since version 2.2.0.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

For Series:

>>> s = pd.Series([1, None, None, 2])
>>> s.bfill()
0    1.0
1    2.0
2    2.0
3    2.0
dtype: float64
>>> s.bfill(limit=1)
0    1.0
1    NaN
2    2.0
3    2.0
dtype: float64

With DataFrame:

>>> df = pd.DataFrame({'A': [1, None, None, 4], 'B': [None, 5, None, 7]})
>>> df
      A     B
0   1.0   NaN
1   NaN   5.0
2   NaN   NaN
3   4.0   7.0
>>> df.bfill()
      A     B
0   1.0   5.0
1   4.0   5.0
2   4.0   7.0
3   4.0   7.0
>>> df.bfill(limit=1)
      A     B
0   1.0   5.0
1   NaN   5.0
2   4.0   7.0
3   4.0   7.0

sparkle.PerformanceDataFrame.bool(self) → bool

Return the bool of a single element Series or DataFrame.

Deprecated since version 2.1.0: bool is deprecated and will be removed in future version of pandas. For Series use pandas.Series.item.

This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).

Returns

bool: The value in the Series or DataFrame.

Examples

The method will only work for single element objects with a boolean value:

>>> pd.Series([True]).bool()  
True
>>> pd.Series([False]).bool()  
False

>>> pd.DataFrame({'col': [True]}).bool()  
True
>>> pd.DataFrame({'col': [False]}).bool()  
False

This is an alternative method and will only work for single element objects with a boolean value:

>>> pd.Series([True]).item()  
True
>>> pd.Series([False]).item()  
False

sparkle.PerformanceDataFrame.boxplot(self: DataFrame, column=None, by=None, ax=None, fontsize: int | None = None, rot: int = 0, grid: bool = True, figsize: tuple[float, float] | None = None, layout=None, return_type=None, backend=None, **kwargs)

Make a box plot from DataFrame columns.

Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns. A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. By default, they extend no more than 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box, ending at the farthest data point within that interval. Outliers are plotted as separate dots.

For further details see Wikipedia’s entry for boxplot.

Parameters

columnstr or list of str, optional

Column name or list of names, or vector. Can be any valid input to pandas.DataFrame.groupby().

bystr or array-like, optional

Column in the DataFrame to pandas.DataFrame.groupby(). One box-plot will be done per value of columns in by.

axobject of class matplotlib.axes.Axes, optional

The matplotlib axes to be used by boxplot.

fontsizefloat or str

Tick label font size in points or as a string (e.g., large).

rotfloat, default 0

The rotation angle of labels (in degrees) with respect to the screen coordinate system.

gridbool, default True

Setting this to True will show the grid.

figsizeA tuple (width, height) in inches

The size of the figure to create in matplotlib.

layouttuple (rows, columns), optional

For example, (3, 5) will display the subplots using 3 rows and 5 columns, starting from the top-left.

return_type{‘axes’, ‘dict’, ‘both’} or None, default ‘axes’

The kind of object to return. The default is axes.

‘axes’ returns the matplotlib axes the boxplot is drawn on.
‘dict’ returns a dictionary whose values are the matplotlib Lines of the boxplot.
‘both’ returns a namedtuple with the axes and dict.
when grouping with by, a Series mapping columns to return_type is returned.

If return_type is None, a NumPy array of axes with the same shape as layout is returned.

backendstr, default None

Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.

**kwargs

All other plotting keyword arguments to be passed to matplotlib.pyplot.boxplot().

Returns

result: See Notes.

Notes

The return type depends on the return_type parameter:

‘axes’ : object of class matplotlib.axes.Axes
‘dict’ : dict of matplotlib.lines.Line2D objects
‘both’ : a namedtuple with structure (ax, lines)

For data grouped with by, return a Series of the above or a numpy array:

Series
array (for return_type = None)

Use return_type='dict' when you want to tweak the appearance of the lines after plotting. In this case a dict containing the Lines making up the boxes, caps, fliers, medians, and whiskers is returned.

Examples

Boxplots can be created for every column in the dataframe by df.boxplot() or indicating the columns to be used:

Boxplots of variables distributions grouped by the values of a third variable can be created using the option by. For instance:

A list of strings (i.e. ['X', 'Y']) can be passed to boxplot in order to group the data by combination of the variables in the x-axis:

The layout of boxplot can be adjusted giving a tuple to layout:

Additional formatting can be done to the boxplot, like suppressing the grid (grid=False), rotating the labels in the x-axis (i.e. rot=45) or changing the fontsize (i.e. fontsize=15):

The parameter return_type can be used to select the type of element returned by boxplot. When return_type='axes' is selected, the matplotlib axes on which the boxplot is drawn are returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes')
>>> type(boxplot)
<class 'matplotlib.axes._axes.Axes'>

When grouping with by, a Series mapping columns to return_type is returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
...                      return_type='axes')
>>> type(boxplot)
<class 'pandas.core.series.Series'>

If return_type is None, a NumPy array of axes with the same shape as layout is returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
...                      return_type=None)
>>> type(boxplot)
<class 'numpy.ndarray'>

sparkle.PerformanceDataFrame.clean_csv(self: PerformanceDataFrame) → None: Set all values in Performance Data to None.

sparkle.PerformanceDataFrame.clip(self, lower=None, upper=None, *, axis: Axis | None = None, inplace: bool_t = False, **kwargs) → Self | None

Trim values at input threshold(s).

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters

lowerfloat or array-like, default None: Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
upperfloat or array-like, default None: Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
axis{{0 or ‘index’, 1 or ‘columns’, None}}, default None: Align object with lower and upper along the given axis. For Series this parameter is unused and defaults to None.
inplacebool, default False: Whether to perform the operation in place on the data.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns

Series or DataFrame or None: Same type as calling object with the values outside the clip boundaries replaced or None if inplace=True.

Examples

>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
   col_0  col_1
0      9     -2
1     -3     -7
2      0      6
3     -1      8
4      5     -5

Clips per column using lower and upper thresholds:

>>> df.clip(-4, 6)
   col_0  col_1
    6     -2
   -3     -4
    0      6
   -1      6
    5     -4

Clips using specific lower and upper thresholds per column:

>>> df.clip([-2, -1], [4, 5])
    col_0  col_1
    4     -1
   -2     -1
    0      5
   -1      5
    4     -1

Clips using specific lower and upper thresholds per column element:

>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0    2
1   -4
2   -1
3    6
4    3
dtype: int64

>>> df.clip(t, t + 4, axis=0)
   col_0  col_1
    6      2
   -3     -4
    0      3
    6      8
    5      3

Clips using specific lower threshold per column element, with missing values:

>>> t = pd.Series([2, -4, np.nan, 6, 3])
>>> t
0    2.0
1   -4.0
2    NaN
3    6.0
4    3.0
dtype: float64

>>> df.clip(t, axis=0)
col_0  col_1
    9      2
   -3     -4
    0      6
    6      8
    5      3

sparkle.PerformanceDataFrame.clone(self: PerformanceDataFrame, csv_filepath: Path = None) → PerformanceDataFrame

Create a copy of this object.

Args:

csv_filepath: The new filepath to use for saving the object to.: If None, will not be saved. Warning: If the original path is used, it could lead to dataloss!

sparkle.PerformanceDataFrame.combine(self, other: DataFrame, func: Callable[[Series, Series], Series | Hashable], fill_value=None, overwrite: bool = True) → DataFrame

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters

otherDataFrame: The DataFrame to merge column-wise.
funcfunction: Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
fill_valuescalar value, default None: The value to fill NaNs with prior to passing any column to the merge func.
overwritebool, default True: If True, columns in self that do not exist in other will be overwritten with NaNs.

Returns

DataFrame: Combination of the provided DataFrames.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
   A    B
0  0 -5.0
1  0  4.0

However, if the same element in both dataframes is None, that None is preserved

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
    A    B
0  0 -5.0
1  0  3.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0

>>> df1.combine(df2, take_smaller, overwrite=False)
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
   A    B   C
0  0.0  NaN NaN
1  0.0  3.0 NaN
2  NaN  3.0 NaN

>>> df2.combine(df1, take_smaller, overwrite=False)
     A    B   C
0  0.0  NaN NaN
1  0.0  3.0 1.0
2  NaN  3.0 1.0

sparkle.PerformanceDataFrame.combine_first(self, other: DataFrame) → DataFrame

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the ‘first’ dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).

Parameters

otherDataFrame: Provided DataFrame to use to fill null values.

Returns

DataFrame: The result of combining the provided DataFrame with the other object.

Examples

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0

sparkle.PerformanceDataFrame.compare(self, other: DataFrame, align_axis: Axis = 1, keep_shape: bool = False, keep_equal: bool = False, result_names: Suffixes = ('self', 'other')) → DataFrame

Compare to another DataFrame and show the differences.

Parameters

otherDataFrame

Object to compare with.

align_axis{0 or ‘index’, 1 or ‘columns’}, default 1

Determine which axis to align the comparison on.

0, or ‘index’Resulting differences are stacked vertically
with rows drawn alternately from self and other.
1, or ‘columns’Resulting differences are aligned horizontally
with columns drawn alternately from self and other.

keep_shapebool, default False

If true, all rows and columns are kept. Otherwise, only the ones with different values are kept.

keep_equalbool, default False

If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.

result_namestuple, default (‘self’, ‘other’)

Set the dataframes names in the comparison.

Added in version 1.5.0.

Returns

DataFrame

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level.

Raises

ValueError: When the two DataFrames don’t have identical labels or shape.

Notes

Matching NaNs will not appear as a difference.

Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames

Examples

>>> df = pd.DataFrame(
...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0

>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Align the differences on columns

>>> df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Assign result_names

>>> df.compare(df2, result_names=("left", "right"))
  col1       col3
  left right left right
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0

sparkle.PerformanceDataFrame.configuration_performance(self: PerformanceDataFrame, solver: str, configuration: str | list[str] = None, objective: str | SparkleObjective = None, instances: list[str] = None, per_instance: bool = False) → tuple[str, float]

Return the (best) configuration performance for objective over the instances.

Args:: solver: The solver for which we determine evaluate the configuration configuration: The configuration (id) to evaluate objective: The objective for which we calculate find the best value instances: The instances which should be selected for the evaluation per_instance: Whether to return the performance per instance,

or aggregated.
Returns:: The (best) configuration id and its aggregated performance.

sparkle.PerformanceDataFrame.convert_dtypes(self, infer_objects: bool = True, convert_string: bool = True, convert_integer: bool = True, convert_boolean: bool = True, convert_floating: bool = True, dtype_backend: Literal['pyarrow', 'numpy_nullable'] = 'numpy_nullable') → None

Convert columns to the best possible dtypes using dtypes supporting pd.NA.

Parameters

infer_objectsbool, default True

Whether object dtypes should be converted to the best possible types.

convert_stringbool, default True

Whether object dtypes should be converted to StringDtype().

convert_integerbool, default True

Whether, if possible, conversion can be done to integer extension types.

convert_booleanbool, defaults True

Whether object dtypes should be converted to BooleanDtypes().

convert_floatingbool, defaults True

Whether, if possible, conversion can be done to floating extension types. If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultant DataFrame (still experimental). Behaviour is as follows:

"numpy_nullable": returns nullable-dtype-backed DataFrame (default).
"pyarrow": returns pyarrow-backed nullable ArrowDtype DataFrame.

Added in version 2.0.

Returns

Series or DataFrame: Copy of input object with new dtype.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_floating, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples

>>> df = pd.DataFrame(
...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0

>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10   <NA>
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0

>>> dfn.dtypes
a             Int32
b    string[python]
c           boolean
d    string[python]
e             Int64
f           Float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: object

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string

sparkle.PerformanceDataFrame.copy(self, deep: bool | None = True) → None

Make a copy of this object’s indices and data.

When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below).

When deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa).

Note

The deep=False behaviour as described above will change in pandas 3.0. Copy-on-Write will be enabled by default, which means that the “shallow” copy is that is returned with deep=False will still avoid making an eager copy, but changes to the data of the original will no longer be reflected in the shallow copy (or vice versa). Instead, it makes use of a lazy (deferred) copy mechanism that will copy the data only when any changes to the original or shallow copy is made.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Parameters

deepbool, default True: Make a deep copy, including a copy of the data and the indices. With deep=False neither the indices nor the data are copied.

Returns

Series or DataFrame: Object type matches caller.

Notes

When deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below).

While Index objects are copied when deep=True, the underlying numpy array is not copied for performance reasons. Since Index is immutable, the underlying data can be safely shared and a copy is not needed.

Since pandas is not thread safe, see the gotchas when copying in a threading environment.

When copy_on_write in pandas config is set to True, the copy_on_write config takes effect even when deep=False. This means that any changes to the copied data would make a new copy of the data upon write (and vice versa). Changes made to either the original or copied variable would not be reflected in the counterpart. See Copy_on_Write for more information.

Examples

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: int64

>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: int64

Shallow copy versus default (deep) copy:

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)

Shallow copy shares data and index with original.

>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True

Deep copy has own copy of data and index.

>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False

Updates to the data shared by shallow copy and original is reflected in both (NOTE: this will no longer be true for pandas >= 3.0); deep copy remains unchanged.

>>> s.iloc[0] = 3
>>> shallow.iloc[1] = 4
>>> s
a    3
b    4
dtype: int64
>>> shallow
a    3
b    4
dtype: int64
>>> deep
a    1
b    2
dtype: int64

Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy.

>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0    [10, 2]
1     [3, 4]
dtype: object
>>> deep
0    [10, 2]
1     [3, 4]
dtype: object

Copy-on-Write is set to true, the shallow copy is not modified when the original data is changed:

>>> with pd.option_context("mode.copy_on_write", True):
...     s = pd.Series([1, 2], index=["a", "b"])
...     copy = s.copy(deep=False)
...     s.iloc[0] = 100
...     s
a    100
b      2
dtype: int64
>>> copy
a    1
b    2
dtype: int64

sparkle.PerformanceDataFrame.corr(self, method: CorrelationMethod = 'pearson', min_periods: int = 1, numeric_only: bool = False) → DataFrame

Compute pairwise correlation of columns, excluding NA/null values.

Parameters

method{‘pearson’, ‘kendall’, ‘spearman’} or callable

Method of correlation:

pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

min_periodsint, optional

Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

numeric_onlybool, default False

Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns

DataFrame: Correlation matrix.

Notes

Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

Examples

>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0

>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
...                   columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)
      dogs  cats
dogs   1.0   NaN
cats   NaN   1.0

sparkle.PerformanceDataFrame.corrwith(self, other: DataFrame | Series, axis: Axis = 0, drop: bool = False, method: CorrelationMethod = 'pearson', numeric_only: bool = False) → Series

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Parameters

otherDataFrame, Series

Object with which to compute correlations.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to use. 0 or ‘index’ to compute row-wise, 1 or ‘columns’ for column-wise.

dropbool, default False

Drop missing indices from result.

method{‘pearson’, ‘kendall’, ‘spearman’} or callable

Method of correlation:

pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
callable: callable with input two 1d ndarrays
and returning a float.

numeric_onlybool, default False

Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns

Series: Pairwise correlations.

Examples

>>> index = ["a", "b", "c", "d", "e"]
>>> columns = ["one", "two", "three", "four"]
>>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
>>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
>>> df1.corrwith(df2)
one      1.0
two      1.0
three    1.0
four     1.0
dtype: float64

>>> df2.corrwith(df1, axis=1)
a    1.0
b    1.0
c    1.0
d    1.0
e    NaN
dtype: float64

sparkle.PerformanceDataFrame.count(self, axis: Axis = 0, numeric_only: bool = False)

Count non-NA cells for each column or row.

The values None, NaN, NaT, pandas.NA are considered NA.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
numeric_onlybool, default False: Include only float, int or boolean data.

Returns

Series: For each column/row the number of non-NA/null entries.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis='columns')
  3
  2
  3
  3
  3
dtype: int64

sparkle.PerformanceDataFrame.cov(self, min_periods: int | None = None, ddof: int | None = 1, numeric_only: bool = False) → DataFrame

Compute pairwise covariance of columns, excluding NA/null values.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters

min_periodsint, optional: Minimum number of observations required per pair of columns to have a valid result.
ddofint, default 1: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. This argument is applicable only when no nan is in the dataframe.
numeric_onlybool, default False: Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns

DataFrame: The covariance matrix of the series of the DataFrame.

Notes

Returns the covariance matrix of the DataFrame’s time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices for more details.

Examples

>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
...                   columns=['dogs', 'cats'])
>>> df.cov()
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202

sparkle.PerformanceDataFrame.cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative maximum of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
  2.0
  NaN
  5.0
  5.0
  5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0

sparkle.PerformanceDataFrame.cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative minimum of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
  2.0
  NaN
  2.0
 -1.0
 -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

sparkle.PerformanceDataFrame.cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative product of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
   2.0
   NaN
  10.0
 -10.0
  -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0

sparkle.PerformanceDataFrame.cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs)

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Series or DataFrame: Return cumulative sum of Series or DataFrame.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
  2.0
  NaN
  7.0
  6.0
  6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0

sparkle.PerformanceDataFrame.describe(self, percentiles=None, include=None, exclude=None) → None

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters

percentileslist-like of numbers, optional

The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.

include‘all’, list-like of dtypes or None (default), optional

A white list of data types to include in the result. Ignored for Series. Here are the options:

‘all’ : All columns of the input will be included in the output.
A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
None (default) : The result will include all numeric columns.

excludelist-like of dtypes or None (default), optional,

A black list of data types to omit from the result. Ignored for Series. Here are the options:

A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(exclude=['O'])). To exclude pandas categorical columns, use 'category'
None (default) : The result will exclude nothing.

Returns

Series or DataFrame: Summary statistics of the Series or Dataframe provided.

Notes

For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...     np.datetime64("2000-01-01"),
...     np.datetime64("2010-01-01"),
...     np.datetime64("2010-01-01")
... ])
>>> s.describe()
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                    })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')  
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])  
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])
       categorical
count            3
unique           3
top              d
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])  
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])  
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0

sparkle.PerformanceDataFrame.diff(self, periods: int = 1, axis: Axis = 0) → DataFrame

First discrete difference of element.

Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row).

Parameters

periodsint, default 1: Periods to shift for calculating difference, accepts negative values.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Take difference over rows (0) or columns (1).

Returns

DataFrame: First differences of the Series.

Notes

For boolean dtypes, this uses operator.xor() rather than operator.sub(). The result is calculated according to current dtype in DataFrame, however dtype of the result is always float64.

Examples

Difference with previous row

>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
...                    'b': [1, 1, 2, 3, 5, 8],
...                    'c': [1, 4, 9, 16, 25, 36]})
>>> df
   a  b   c
0  1  1   1
1  2  1   4
2  3  2   9
3  4  3  16
4  5  5  25
5  6  8  36

>>> df.diff()
     a    b     c
NaN  NaN   NaN
1.0  0.0   3.0
1.0  1.0   5.0
1.0  1.0   7.0
1.0  2.0   9.0
1.0  3.0  11.0

Difference with previous column

>>> df.diff(axis=1)
    a  b   c
NaN  0   0
NaN -1   3
NaN -1   7
NaN -1  13
NaN  0  20
NaN  2  28

Difference with 3rd previous row

>>> df.diff(periods=3)
     a    b     c
NaN  NaN   NaN
NaN  NaN   NaN
NaN  NaN   NaN
3.0  2.0  15.0
3.0  4.0  21.0
3.0  6.0  27.0

Difference with following row

>>> df.diff(periods=-1)
     a    b     c
-1.0  0.0  -3.0
-1.0 -1.0  -5.0
-1.0 -1.0  -7.0
-1.0 -2.0  -9.0
-1.0 -3.0 -11.0
NaN  NaN   NaN

Overflow in input dtype

>>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
>>> df.diff()
       a
0    NaN
1  255.0

sparkle.PerformanceDataFrame.div(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.divide(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.dot(self, other: AnyArrayLike | DataFrame) → DataFrame | Series

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using self @ other.

Parameters

otherSeries, DataFrame or array-like: The other object to compute the matrix product with.

Returns

Series or DataFrame: If other is a Series, return the matrix product between self and other as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0    -4
1     5
dtype: int64

Here we multiply a DataFrame with another DataFrame.

>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
    0   1
0   1   4
1   2   2

Note that the dot method give the same result as @

>>> df @ other
 1
 1   4
 2   2

The dot method works also if other is an np.array.

>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
    0   1
0   1   4
1   2   2

Note how shuffling of the objects does not change the result.

>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0    -4
1     5
dtype: int64

sparkle.PerformanceDataFrame.drop(self, labels: IndexLabel | None = None, *, axis: Axis = 0, index: IndexLabel | None = None, columns: IndexLabel | None = None, level: Level | None = None, inplace: bool = False, errors: IgnoreRaise = 'raise') → DataFrame | None

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by directly specifying index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide for more information about the now unused levels.

Parameters

labelssingle label or list-like: Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
indexsingle label or list-like: Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).
columnssingle label or list-like: Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).
levelint or level name, optional: For MultiIndex, level from which the labels will be removed.
inplacebool, default False: If False, return a copy. Otherwise, do operation in place and return None.
errors{‘ignore’, ‘raise’}, default ‘raise’: If ‘ignore’, suppress error and only existing labels are dropped.

Returns

DataFrame or None: Returns DataFrame or None DataFrame with the specified index or column labels removed or None if inplace=True.

Raises

KeyError: If any of the labels is not found in the selected axis.

Examples

>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2

Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination 'falcon' and 'weight', which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight'))
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        length  0.3     0.2

>>> df.drop(index='cow', columns='small')
                big
llama   speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3

>>> df.drop(index='length', level=1)
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8

sparkle.PerformanceDataFrame.drop_duplicates(self, subset: Hashable | Sequence[Hashable] | None = None, *, keep: DropKeep = 'first', inplace: bool = False, ignore_index: bool = False) → DataFrame | None

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters

subsetcolumn label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the columns.

keep{‘first’, ‘last’, False}, default ‘first’

Determines which duplicates (if any) to keep.

‘first’ : Drop duplicates except for the first occurrence.
‘last’ : Drop duplicates except for the last occurrence.
False : Drop all duplicates.

inplacebool, default False

Whether to modify the DataFrame rather than creating a new one.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

Returns

DataFrame or None: DataFrame with duplicates removed or None if inplace=True.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0

sparkle.PerformanceDataFrame.droplevel(self, level: Hashable | Sequence[Hashable], axis: int | Literal['index', 'columns', 'rows'] = 0) → None

Return Series/DataFrame with requested index / column level(s) removed.

Parameters

levelint, str, or list-like

If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Axis along which the level(s) is removed:

0 or ‘index’: remove level(s) in column.
1 or ‘columns’: remove level(s) in row.

For Series this parameter is unused and defaults to 0.

Returns

Series/DataFrame: Series/DataFrame with requested index / column level(s) removed.

Examples

>>> df = pd.DataFrame([
...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])

>>> df.columns = pd.MultiIndex.from_tuples([
...     ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])

>>> df
level_1   c   d
level_2   e   f
a b
1 2      3   4
5 6      7   8
9 10    11  12

>>> df.droplevel('a')
level_1   c   d
level_2   e   f
b
2        3   4
6        7   8
10      11  12

>>> df.droplevel('level_2', axis=1)
level_1   c   d
a b
1 2      3   4
5 6      7   8
9 10    11  12

sparkle.PerformanceDataFrame.dropna(self, *, axis: Axis = 0, how: AnyAll | lib.NoDefault = <no_default>, thresh: int | lib.NoDefault = <no_default>, subset: IndexLabel | None = None, inplace: bool = False, ignore_index: bool = False) → DataFrame | None

Remove missing values.

See the User Guide for more on which values are considered missing, and how to work with missing data.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Determine if rows or columns which contain missing values are removed.

0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.

Only a single axis is allowed.

how{‘any’, ‘all’}, default ‘any’

Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

‘any’ : If any NA values are present, drop that row or column.
‘all’ : If all values are NA, drop that row or column.

threshint, optional

Require that many non-NA values. Cannot be combined with how.

subsetcolumn label or sequence of labels, optional

Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.

inplacebool, default False

Whether to modify the DataFrame rather than creating a new one.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

Added in version 2.0.0.

Returns

DataFrame or None: DataFrame with NA entries dropped from it or None if inplace=True.

Examples

>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

sparkle.PerformanceDataFrame.duplicated(self, subset: Hashable | Sequence[Hashable] | None = None, keep: DropKeep = 'first') → Series

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters

subsetcolumn label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the columns.

keep{‘first’, ‘last’, False}, default ‘first’

Determines which duplicates (if any) to mark.

first : Mark duplicates as True except for the first occurrence.
last : Mark duplicates as True except for the last occurrence.
False : Mark all duplicates as True.

Returns

Series: Boolean series for each duplicated rows.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, for each set of duplicated values, the first occurrence is set on False and all others on True.

>>> df.duplicated()
  False
   True
  False
  False
  False
dtype: bool

By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True.

>>> df.duplicated(keep='last')
   True
  False
  False
  False
  False
dtype: bool

By setting keep on False, all duplicates are True.

>>> df.duplicated(keep=False)
   True
   True
  False
  False
  False
dtype: bool

To find duplicates on specific column(s), use subset.

>>> df.duplicated(subset=['brand'])
  False
   True
  False
   True
   True
dtype: bool

sparkle.PerformanceDataFrame.eq(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Equal to of dataframe and other, element-wise (binary operator eq).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.PerformanceDataFrame.equals(self, other: object) → bool

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns and index must be of the same dtype.

Parameters

otherSeries or DataFrame: The other Series or DataFrame to be compared with the first.

Returns

bool: True if all elements are the same in both objects, False otherwise.

Examples

>>> df = pd.DataFrame({1: [10], 2: [20]})
>>> df
    1   2
0  10  20

DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.

>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.

>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
      1     2
0  10.0  20.0
>>> df.equals(different_data_type)
False

sparkle.PerformanceDataFrame.eval(self, expr: str, *, inplace: bool = False, **kwargs) → Any | None

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters

exprstr: The expression string to evaluate.
inplacebool, default False: If the expression contains an assignment, whether to perform the operation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
**kwargs: See the documentation for eval() for complete details on the keyword arguments accepted by query().

Returns

ndarray, scalar, pandas object, or None: The result of the evaluation or None if inplace=True.

Notes

For more details see the API documentation for eval(). For detailed examples see enhancing performance with eval.

Examples

>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')
   A   B   C
1  10  11
2   8  10
3   6   9
4   4   8
5   2   7
>>> df
   A   B
1  10
2   8
3   6
4   4
5   2

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3

Provide exponentially weighted (EW) calculations.

Exactly one of com, span, halflife, or alpha must be provided if times is not provided. If times is provided, halflife and one of com, span or alpha may be provided.

Parameters

comfloat, optional

Specify decay in terms of center of mass

\(\alpha = 1 / (1 + com)\), for \(com \geq 0\).

spanfloat, optional

Specify decay in terms of span

\(\alpha = 2 / (span + 1)\), for \(span \geq 1\).

halflifefloat, str, timedelta, optional

Specify decay in terms of half-life

\(\alpha = 1 - \exp\left(-\ln(2) / halflife\right)\), for \(halflife > 0\).

If times is specified, a timedelta convertible unit over which an observation decays to half its value. Only applicable to mean(), and halflife value will not apply to the other functions.

alphafloat, optional

Specify smoothing factor \(\alpha\) directly

\(0 < \alpha \leq 1\).

min_periodsint, default 0

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

adjustbool, default True

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average).

When adjust=True (default), the EW function is calculated using weights \(w_i = (1 - \alpha)^i\). For example, the EW moving average of the series [\(x_0, x_1, ..., x_t\)] would be:

\[y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}\]

When adjust=False, the exponentially weighted function is calculated recursively:

\[\begin{split}\begin{split} y_0 &= x_0\\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}\end{split}\]

ignore_nabool, default False

Ignore missing values when calculating weights.

When ignore_na=False (default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-\alpha)^2\) and \(1\) if adjust=True, and \((1-\alpha)^2\) and \(\alpha\) if adjust=False.
When ignore_na=True, weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-\alpha\) and \(1\) if adjust=True, and \(1-\alpha\) and \(\alpha\) if adjust=False.

axis{0, 1}, default 0

If 0 or 'index', calculate across the rows.

If 1 or 'columns', calculate across the columns.

For Series this parameter is unused and defaults to 0.

times : np.ndarray, Series, default None

Only applicable to mean().

Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype.

If 1-D array like, a sequence with the same shape as the observations.

methodstr {‘single’, ‘table’}, default ‘single’

Added in version 1.4.0.

Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Only applicable to mean()

Returns

pandas.api.typing.ExponentialMovingWindow

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

>>> df.ewm(com=0.5).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213
>>> df.ewm(alpha=2 / 3).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213

adjust

>>> df.ewm(com=0.5, adjust=True).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213
>>> df.ewm(com=0.5, adjust=False).mean()
          B
0.000000
0.666667
1.555556
1.555556
3.650794

ignore_na

>>> df.ewm(com=0.5, ignore_na=True).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.225000
>>> df.ewm(com=0.5, ignore_na=False).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213

times

Exponentially weighted mean with weights calculated with a timedelta halflife relative to times.

>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
          B
0  0.000000
1  0.585786
2  1.523889
3  1.523889
4  3.233686

sparkle.PerformanceDataFrame.expanding(self, min_periods: int = 1, axis: int | ~typing.Literal['index', 'columns', 'rows'] | ~typing.Literal[_NoDefault.no_default] = <no_default>, method: ~typing.Literal['single', 'table'] = 'single') → Expanding

Provide expanding window calculations.

Parameters

min_periodsint, default 1

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

axisint or str, default 0

If 0 or 'index', roll across the rows.

If 1 or 'columns', roll across the columns.

For Series this parameter is unused and defaults to 0.

methodstr {‘single’, ‘table’}, default ‘single’

Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Added in version 1.3.0.

Returns

pandas.api.typing.Expanding

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

min_periods

Expanding sum with 1 vs 3 observations needed to calculate a value.

>>> df.expanding(1).sum()
     B
0.0
1.0
3.0
3.0
7.0
>>> df.expanding(3).sum()
     B
NaN
NaN
3.0
3.0
7.0

sparkle.PerformanceDataFrame.explode(self, column: IndexLabel, ignore_index: bool = False) → DataFrame

Transform each element of a list-like to a row, replicating index values.

Parameters

columnIndexLabel: Column(s) to explode. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.

Added in version 1.3.0: Multi-column explode
ignore_indexbool, default False: If True, the resulting index will be labeled 0, 1, …, n - 1.

Returns

DataFrame: Exploded lists to rows of the subset columns; index will be duplicated for these rows.

Raises

ValueError :

If columns of the frame are not unique.
If specified columns to explode is empty list.
If specified columns to explode have not matching count of elements rowwise in the frame.

Notes

This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
...                    'B': 1,
...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode('A')
     A  B          C
  0  1  [a, b, c]
  1  1  [a, b, c]
  2  1  [a, b, c]
foo  1        NaN
NaN  1         []
  3  1     [d, e]
  4  1     [d, e]

Multi-column explode.

>>> df.explode(list('AC'))
     A  B    C
  0  1    a
  1  1    b
  2  1    c
foo  1  NaN
NaN  1  NaN
  3  1    d
  4  1    e

Fill NA/NaN values by propagating the last valid observation to next valid.

Parameters

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

limit_area{None, ‘inside’, ‘outside’}, default None

If limit is specified, consecutive NaNs will be filled with this restriction.

None: No fill restriction.
‘inside’: Only fill NaNs surrounded by valid values (interpolate).
‘outside’: Only fill NaNs outside valid values (extrapolate).

Added in version 2.2.0.

downcastdict, default is None

A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Deprecated since version 2.2.0.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, np.nan],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

>>> df.ffill()
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  3.0  4.0 NaN  1.0
3  3.0  3.0 NaN  4.0

>>> ser = pd.Series([1, np.nan, 2, 3])
>>> ser.ffill()
0   1.0
1   1.0
2   2.0
3   3.0
dtype: float64

Fill NA/NaN values using the specified method.

Parameters

valuescalar, dict, Series, or DataFrame

Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.

method{‘backfill’, ‘bfill’, ‘ffill’, None}, default None

Method to use for filling holes in reindexed Series:

ffill: propagate last valid observation forward to next valid.
backfill / bfill: use next valid observation to fill gap.

Deprecated since version 2.1.0: Use ffill or bfill instead.

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

downcastdict, default is None

A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Deprecated since version 2.2.0.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, np.nan],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

Replace all NaN elements with 0s.

>>> df.fillna(0)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  0.0
3  0.0  3.0  0.0  4.0

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  2.0  1.0
2  0.0  1.0  2.0  3.0
3  0.0  3.0  2.0  4.0

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  NaN  1.0
2  NaN  1.0  NaN  3.0
3  NaN  3.0  NaN  4.0

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  NaN
3  0.0  3.0  0.0  4.0

Note that column D is not affected since it is not present in df2.

sparkle.PerformanceDataFrame.filter(self, items=None, like: str | None = None, regex: str | None = None, axis: int | Literal['index', 'columns', 'rows'] | None = None) → None

Subset the dataframe rows or columns according to the specified index labels.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Parameters

itemslist-like: Keep labels from axis which are in items.
likestr: Keep labels from axis for which “like in label == True”.
regexstr (regular expression): Keep labels from axis for which re.search(regex, label) == True.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, ‘columns’ for DataFrame. For Series this parameter is unused and defaults to None.

Returns

same type as input object

Notes

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

Examples

>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])
>>> df
        one  two  three
mouse     1    2      3
rabbit    4    5      6

>>> # select columns by name
>>> df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6

>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
         one  three
mouse     1      3
rabbit    4      6

>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6

sparkle.PerformanceDataFrame.filter_objective(self: PerformanceDataFrame, objective: str | list[str]) → None: Filter the Dataframe to a subset of objectives.

sparkle.PerformanceDataFrame.first(self, offset) → None

Select initial periods of time series data based on a date offset.

Deprecated since version 2.1: first() is deprecated and will be removed in a future version. Please create a mask and filter using .loc instead.

For a DataFrame with a sorted DatetimeIndex, this function can select the first few rows based on a date offset.

Parameters

offsetstr, DateOffset or dateutil.relativedelta: The offset length of the data that will be selected. For instance, ‘1ME’ will display all the rows having their index within the first month.

Returns

Series or DataFrame: A subset of the caller.

Raises

TypeError: If the index is not a DatetimeIndex

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the first 3 days:

>>> ts.first('3D')
            A
2018-04-09  1
2018-04-11  2

Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.

sparkle.PerformanceDataFrame.first_valid_index(self) → Hashable | None

Return index for first non-NA value or None, if no non-NA value is found.

Returns

type of index

Examples

For Series:

>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2

>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If all elements in Series are NA/null, returns None.

>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If Series is empty, returns None.

For DataFrame:

>>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
>>> df
     A      B
0  NaN    NaN
1  NaN    3.0
2  2.0    4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2

>>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
>>> df
     A      B
0  None   None
1  None   None
2  None   None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If all elements in DataFrame are NA/null, returns None.

>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If DataFrame is empty, returns None.

sparkle.PerformanceDataFrame.floordiv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Integer division of dataframe and other, element-wise (binary operator floordiv).

Equivalent to dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.ge(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.PerformanceDataFrame.get(self, key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

Parameters

key : object

Returns

same type as items contained in object

Examples

>>> df = pd.DataFrame(
...     [
...         [24.3, 75.7, "high"],
...         [31, 87.8, "high"],
...         [22, 71.6, "medium"],
...         [35, 95, "medium"],
...     ],
...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
...     index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
... )

>>> df
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium

>>> df.get(["temp_celsius", "windspeed"])
            temp_celsius windspeed
2014-02-12          24.3      high
2014-02-13          31.0      high
2014-02-14          22.0    medium
2014-02-15          35.0    medium

>>> ser = df['windspeed']
>>> ser.get('2014-02-13')
'high'

If the key isn’t found, the default value will be used.

>>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
'default_value'

>>> ser.get('2014-02-10', '[unknown]')
'[unknown]'

sparkle.PerformanceDataFrame.get_configurations(self: PerformanceDataFrame, solver_name: str) → list[str]: Return the list of configuration keys for a solver.

sparkle.PerformanceDataFrame.get_full_configuration(self: PerformanceDataFrame, solver: str, configuration_id: str | list[str]) → dict | list[dict]: Return the actual configuration associated with the configuration key.

sparkle.PerformanceDataFrame.get_instance_num_runs(self: PerformanceDataFrame, instance: str) → int: Return the number of runs for an instance.

sparkle.PerformanceDataFrame.get_job_list(self: PerformanceDataFrame, rerun: bool = False) → list[tuple[str, str]]

Return a list of performance computation jobs there are to be done.

Get a list of tuple[instance, solver] to run from the performance data. If rerun is False (default), get only the tuples that don’t have a value, else (True) get all the tuples.

Args:: rerun: Boolean indicating if we want to rerun all jobs
Returns:: A tuple of (solver, config, instance, run) combinations

sparkle.PerformanceDataFrame.get_solver_ranking(self: PerformanceDataFrame, objective: str | SparkleObjective = None, instances: list[str] = None) → list[tuple[str, dict, float]]: Return a list with solvers ranked by average performance.

sparkle.PerformanceDataFrame.get_value(self: PerformanceDataFrame, solver: str | list[str] = None, instance: str | list[str] = None, configuration: str = None, objective: str = None, run: int = None, solver_fields: list[str] = ['Value']) → float | str | list[Any]: Index a value of the DataFrame and return it.

sparkle.PerformanceDataFrame.groupby(self, by=None, axis: Axis | lib.NoDefault = <no_default>, level: IndexLabel | None = None, as_index: bool = True, sort: bool = True, group_keys: bool = True, observed: bool | lib.NoDefault = <no_default>, dropna: bool = True) → DataFrameGroupBy

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters

bymapping, function, label, pd.Grouper or list of such: Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If a list or ndarray of length equal to the selected axis is passed (see the groupby user guide), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.

Deprecated since version 2.1.0: Will be removed and behave like axis=0 in a future version. For axis=1, do frame.T.groupby(...) instead.
levelint, level name, or sequence of such, default None: If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.
as_indexbool, default True: Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).
sortbool, default True: Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. If False, the groups will appear in the same order as they did in the original DataFrame. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).

Changed in version 2.0.0: Specifying sort=False with an ordered categorical grouper will no longer sort the values.
group_keysbool, default True: When calling apply and the by argument produces a like-indexed (i.e. a transform) result, add group keys to index to identify pieces. By default group keys are not included when the result’s index (and column) labels match the inputs, and are included otherwise.

Changed in version 1.5.0: Warns that group_keys will no longer be ignored when the result from apply is a like-indexed Series or DataFrame. Specify group_keys explicitly to include the group keys or not.

Changed in version 2.0.0: group_keys now defaults to True.
observedbool, default False: This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Deprecated since version 2.1.0: The default value will change to True in a future version of pandas.
dropnabool, default True: If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

Returns

pandas.api.typing.DataFrameGroupBy: Returns a groupby object that contains information about the groups.

Notes

See the user guide for more detailed usage and examples, including splitting an object into groups, iterating through groups, selecting a group, aggregation, and more.

Examples

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True.

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5

>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4

>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0

>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0

When using .apply(), use group_keys to include or exclude the group keys. The group_keys argument defaults to True (include).

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
          Max Speed
Animal
Falcon 0      380.0
       1      370.0
Parrot 2       24.0
       3       26.0

>>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
   Max Speed
0      380.0
1      370.0
2       24.0
3       26.0

sparkle.PerformanceDataFrame.gt(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than of dataframe and other, element-wise (binary operator gt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.PerformanceDataFrame.head(self, n: int = 5) → None

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last |n| rows, equivalent to df[:n].

If n is larger than the number of rows, this function returns all rows.

Parameters

nint, default 5: Number of rows to select.

Returns

same type as caller: The first n rows of the caller object.

Examples

>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the first 5 lines

>>> df.head()
      animal
alligator
      bee
   falcon
     lion
   monkey

Viewing the first n lines (three in this case)

>>> df.head(3)
      animal
0  alligator
1        bee
2     falcon

For negative values of n

>>> df.head(-3)
      animal
alligator
      bee
   falcon
     lion
   monkey
   parrot

sparkle.PerformanceDataFrame.hist(data: DataFrame, column: IndexLabel | None = None, by=None, grid: bool = True, xlabelsize: int | None = None, xrot: float | None = None, ylabelsize: int | None = None, yrot: float | None = None, ax=None, sharex: bool = False, sharey: bool = False, figsize: tuple[int, int] | None = None, layout: tuple[int, int] | None = None, bins: int | Sequence[int] = 10, backend: str | None = None, legend: bool = False, **kwargs)

Make a histogram of the DataFrame’s columns.

A `histogram`_ is a representation of the distribution of data. This function calls matplotlib.pyplot.hist(), on each series in the DataFrame, resulting in one histogram per column.

Parameters

dataDataFrame: The pandas object holding the data.
columnstr or sequence, optional: If passed, will be used to limit data to a subset of columns.
byobject, optional: If passed, then used to form histograms for separate groups.
gridbool, default True: Whether to show axis grid lines.
xlabelsizeint, default None: If specified changes the x-axis label size.
xrotfloat, default None: Rotation of x axis labels. For example, a value of 90 displays the x labels rotated 90 degrees clockwise.
ylabelsizeint, default None: If specified changes the y-axis label size.
yrotfloat, default None: Rotation of y axis labels. For example, a value of 90 displays the y labels rotated 90 degrees clockwise.
axMatplotlib axes object, default None: The axes to plot the histogram on.
sharexbool, default True if ax is None else False: In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in. Note that passing in both an ax and sharex=True will alter all x axis labels for all subplots in a figure.
shareybool, default False: In case subplots=True, share y axis and set some y axis labels to invisible.
figsizetuple, optional: The size in inches of the figure to create. Uses the value in matplotlib.rcParams by default.
layouttuple, optional: Tuple of (rows, columns) for the layout of the histograms.
binsint or sequence, default 10: Number of histogram bins to be used. If an integer is given, bins + 1 bin edges are calculated and returned. If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, bins is returned unmodified.
backendstr, default None: Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.
legendbool, default False: Whether to show the legend.
**kwargs: All other plotting keyword arguments to be passed to matplotlib.pyplot.hist().

Returns

matplotlib.AxesSubplot or numpy.ndarray of them

Examples

This example draws a histogram based on the length and width of some animals, displayed in three bins

sparkle.PerformanceDataFrame.idxmax(self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False) → Series

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

Added in version 1.5.0.

Returns

Series: Indexes of maxima along the specified axis.

Raises

ValueError

If the row/column is empty

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                     'co2_emissions': [37.2, 19.66, 1712]},
...                   index=['Pork', 'Wheat Products', 'Beef'])

>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object

sparkle.PerformanceDataFrame.idxmin(self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False) → Series

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

Added in version 1.5.0.

Returns

Series: Indexes of minima along the specified axis.

Raises

ValueError

If the row/column is empty

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                     'co2_emissions': [37.2, 19.66, 1712]},
...                   index=['Pork', 'Wheat Products', 'Beef'])

>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object

sparkle.PerformanceDataFrame.infer_objects(self, copy: bool | None = None) → None

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

Parameters

copybool, default True: Whether to make a copy for non-object or non-inferable columns or Series.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

same type as input object

Examples

>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3

>>> df.dtypes
A    object
dtype: object

>>> df.infer_objects().dtypes
A    int64
dtype: object

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.

Parameters

verbosebool, optional

Whether to print the full summary. By default, the setting in pandas.options.display.max_info_columns is followed.

bufwritable buffer, defaults to sys.stdout

Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output.

max_colsint, optional

When to switch from the verbose to the truncated output. If the DataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.

memory_usagebool, str, optional

Specifies whether total memory usage of the DataFrame elements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting.

True always show memory usage. False never shows memory usage. A value of ‘deep’ is equivalent to “True with deep introspection”. Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources. See the Frequently Asked Questions for more details.

show_countsbool, optional

Whether to show the non-null counts. By default, this is shown only if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.

Returns

None: This method prints a summary of a DataFrame and returns None.

Examples

>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
...                   "float_col": float_values})
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  
...     f.write(s)
260

The memory_usage parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 22.9+ MB

>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 165.9 MB

sparkle.PerformanceDataFrame.insert(self, loc: int, column: Hashable, value: Scalar | AnyArrayLike, allow_duplicates: bool | lib.NoDefault = <no_default>) → None

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

Parameters

locint: Insertion index. Must verify 0 <= loc <= len(columns).
columnstr, number, or hashable object: Label of the inserted column.
valueScalar, Series, or array-like: Content of the inserted column.
allow_duplicatesbool, optional, default lib.no_default: Allow duplicate column labels to be created.

Examples

>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
>>> df.insert(1, "newcol", [99, 99])
>>> df
   col1  newcol  col2
0     1      99     3
1     2      99     4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
   col1  col1  newcol  col2
0   100     1      99     3
1   100     2      99     4

Notice that pandas uses index alignment in case of value from type Series:

>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
   col0  col1  col1  newcol  col2
0   NaN   100     1      99     3
1   5.0   100     2      99     4

sparkle.PerformanceDataFrame.interpolate(self, method: InterpolateOptions = 'linear', *, axis: Axis = 0, limit: int | None = None, inplace: bool_t = False, limit_direction: Literal['forward', 'backward', 'both'] | None = None, limit_area: Literal['inside', 'outside'] | None = None, downcast: Literal['infer'] | None | lib.NoDefault = <no_default>, **kwargs) → Self | None

Fill NaN values using an interpolation method.

Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex.

Parameters

methodstr, default ‘linear’

Interpolation technique to use. One of:

‘linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
‘time’: Works on daily and higher resolution data to interpolate given length of interval.
‘index’, ‘values’: use the actual numerical values of the index.
‘pad’: Fill in NaNs using existing values.
‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d, whereas ‘spline’ is passed to scipy.interpolate.UnivariateSpline. These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5). Note that, slinear method in Pandas refers to the Scipy first order spline instead of Pandas first order spline.
‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’, ‘cubicspline’: Wrappers around the SciPy interpolation methods of similar names. See Notes.
‘from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives.

axis{{0 or ‘index’, 1 or ‘columns’, None}}, default None

Axis to interpolate along. For Series this parameter is unused and defaults to 0.

limitint, optional

Maximum number of consecutive NaNs to fill. Must be greater than 0.

inplacebool, default False

Update the data in place if possible.

limit_direction{{‘forward’, ‘backward’, ‘both’}}, Optional

Consecutive NaNs will be filled in this direction.

If limit is specified:

If ‘method’ is ‘pad’ or ‘ffill’, ‘limit_direction’ must be ‘forward’.
If ‘method’ is ‘backfill’ or ‘bfill’, ‘limit_direction’ must be ‘backwards’.

If ‘limit’ is not specified:

If ‘method’ is ‘backfill’ or ‘bfill’, the default is ‘backward’
else the default is ‘forward’

raises ValueError if limit_direction is ‘forward’ or ‘both’ and

method is ‘backfill’ or ‘bfill’.

raises ValueError if limit_direction is ‘backward’ or ‘both’ and

method is ‘pad’ or ‘ffill’.

limit_area{{None, ‘inside’, ‘outside’}}, default None

If limit is specified, consecutive NaNs will be filled with this restriction.

None: No fill restriction.
‘inside’: Only fill NaNs surrounded by valid values (interpolate).
‘outside’: Only fill NaNs outside valid values (extrapolate).

downcastoptional, ‘infer’ or None, defaults to None

Downcast dtypes if possible.

Deprecated since version 2.1.0.

**kwargsoptional

Keyword arguments to pass on to the interpolating function.

Returns

Series or DataFrame or None: Returns the same object type as the caller, interpolated at some or all NaN values or None if inplace=True.

Notes

The ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’ and ‘akima’ methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the SciPy documentation.

Examples

Filling in NaN in a Series via linear interpolation.

>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0    0.0
1    1.0
2    NaN
3    3.0
dtype: float64
>>> s.interpolate()
0    0.0
1    1.0
2    2.0
3    3.0
dtype: float64

Filling in NaN in a Series via polynomial interpolation or splines: Both ‘polynomial’ and ‘spline’ methods require that you also specify an order (int).

>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method='polynomial', order=2)
0    0.000000
1    2.000000
2    4.666667
3    8.000000
dtype: float64

Fill the DataFrame forward (that is, going down) along each column using linear interpolation.

Note how the last entry in column ‘a’ is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column ‘b’ remains NaN, because there is no entry before it to use for interpolation.

>>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
...                    (np.nan, 2.0, np.nan, np.nan),
...                    (2.0, 3.0, np.nan, 9.0),
...                    (np.nan, 4.0, -4.0, 16.0)],
...                   columns=list('abcd'))
>>> df
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  NaN  2.0  NaN   NaN
2  2.0  3.0  NaN   9.0
3  NaN  4.0 -4.0  16.0
>>> df.interpolate(method='linear', limit_direction='forward', axis=0)
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  1.0  2.0 -2.0   5.0
2  2.0  3.0 -3.0   9.0
3  2.0  4.0 -4.0  16.0

Using polynomial interpolation.

>>> df['d'].interpolate(method='polynomial', order=2)
0     1.0
1     4.0
2     9.0
3    16.0
Name: d, dtype: float64

sparkle.PerformanceDataFrame.is_missing(self: PerformanceDataFrame, solver: str, instance: str) → int: Checks if a solver/instance is missing values.

sparkle.PerformanceDataFrame.isetitem(self, loc, value) → None

Set the given value in the column with position loc.

This is a positional analogue to __setitem__.

Parameters

locint or sequence of ints: Index position for the column.
valuescalar or arraylike: Value(s) for the column.

Notes

frame.isetitem(loc, value) is an in-place method as it will modify the DataFrame in place (not returning a new object). In contrast to frame.iloc[:, i] = value which will try to update the existing values in place, frame.isetitem(loc, value) will not update the values of the column itself in place, it will instead insert a new array.

In cases where frame.columns is unique, this is equivalent to frame[frame.columns[i]] = value.

sparkle.PerformanceDataFrame.isin(self, values: Series | DataFrame | Sequence | Mapping) → DataFrame

Whether each element in the DataFrame is contained in values.

Parameters

valuesiterable, Series, DataFrame or dict: The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.

Returns

DataFrame: DataFrame of booleans showing whether each element in the DataFrame is contained in values.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
...                   index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

To check if values is not in the DataFrame, use the ~ operator:

>>> ~df.isin([0, 2])
        num_legs  num_wings
falcon     False      False
dog         True      False

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in other.

>>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
...                      index=['spider', 'falcon'])
>>> df.isin(other)
        num_legs  num_wings
falcon     False       True
dog        False      False

sparkle.PerformanceDataFrame.isna(self) → DataFrame

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

sparkle.PerformanceDataFrame.isnull(self) → DataFrame

DataFrame.isnull is an alias for DataFrame.isna.

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

sparkle.PerformanceDataFrame.items(self) → Iterable[tuple[Hashable, Series]]

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields

labelobject: The column names for the DataFrame being iterated over.
contentSeries: The column entries belonging to each label, as a Series.

Examples

>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64

sparkle.PerformanceDataFrame.iterrows(self) → Iterable[tuple[Hashable, Series]]

Iterate over DataFrame rows as (index, Series) pairs.

Yields

indexlabel or tuple of label: The index of the row. A tuple for a MultiIndex.
dataSeries: The data of the row as a Series.

Notes

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames).

To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.
You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

Examples

>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

sparkle.PerformanceDataFrame.itertuples(self, index: bool = True, name: str | None = 'Pandas') → Iterable[tuple[Any, ...]]

Iterate over DataFrame rows as namedtuples.

Parameters

indexbool, default True: If True, return the index as the first element of the tuple.
namestr or None, default “Pandas”: The name of the returned namedtuples or None to return regular tuples.

Returns

iterator: An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore.

Examples

>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
...                   index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):
...     print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name='Animal'):
...     print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)

sparkle.PerformanceDataFrame.join(self, other: DataFrame | Series | Iterable[DataFrame | Series], on: IndexLabel | None = None, how: MergeHow = 'left', lsuffix: str = '', rsuffix: str = '', sort: bool = False, validate: JoinValidate | None = None) → DataFrame

Join columns of another DataFrame.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters

otherDataFrame, Series, or a list containing any combination of them

Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.

onstr, list of str, or array-like, optional

Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.

how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘left’

How to handle the operation of the two objects.

left: use calling frame’s index (or column if on is specified)
right: use other’s index.
outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it lexicographically.
inner: form intersection of calling frame’s index (or column if on is specified) with other’s index, preserving the order of the calling’s one.
cross: creates the cartesian product from both frames, preserves the order of the left keys.

lsuffixstr, default ‘’

Suffix to use from left frame’s overlapping columns.

rsuffixstr, default ‘’

Suffix to use from right frame’s overlapping columns.

sortbool, default False

Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).

validatestr, optional

If specified, checks if join is of specified type.

“one_to_one” or “1:1”: check if join keys are unique in both left and right datasets.
“one_to_many” or “1:m”: check if join keys are unique in left dataset.
“many_to_one” or “m:1”: check if join keys are unique in right dataset.
“many_to_many” or “m:m”: allowed, but does not result in checks.

Added in version 1.5.0.

Returns

DataFrame: A dataframe containing columns from both the caller and other.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Examples

>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> df
  key   A
K0  A0
K1  A1
K2  A2
K3  A3
K4  A4
K5  A5

>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})

>>> other
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix='_caller', rsuffix='_other')
  key_caller   A key_other    B
       K0  A0        K0   B0
       K1  A1        K1   B1
       K2  A2        K2   B2
       K3  A3       NaN  NaN
       K4  A4       NaN  NaN
       K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index('key').join(other.set_index('key'))
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result.

>>> df.join(other.set_index('key'), on='key')
  key   A    B
K0  A0   B0
K1  A1   B1
K2  A2   B2
K3  A3  NaN
K4  A4  NaN
K5  A5  NaN

Using non-unique key values shows how they are matched.

>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> df
  key   A
K0  A0
K1  A1
K1  A2
K3  A3
K0  A4
K1  A5

>>> df.join(other.set_index('key'), on='key', validate='m:1')
  key   A    B
K0  A0   B0
K1  A1   B1
K1  A2   B1
K3  A3  NaN
K0  A4   B0
K1  A5   B1

sparkle.PerformanceDataFrame.keys(self) → Index

Get the ‘info axis’ (see Indexing for more).

This is index for Series, columns for DataFrame.

Returns

Index: Info axis.

Examples

>>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]},
...                  index=['a', 'b', 'c'])
>>> d
   A  B
a  1  0
b  2  4
c  3  8
>>> d.keys()
Index(['A', 'B'], dtype='object')

sparkle.PerformanceDataFrame.kurt(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
>>> s
cat    1
dog    2
dog    2
mouse  3
dtype: int64
>>> s.kurt()
1.5

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
...                   index=['cat', 'dog', 'dog', 'mouse'])
>>> df
       a   b
  cat  1   3
  dog  2   4
  dog  2   4
mouse  3   4
>>> df.kurt()
a   1.5
b   4.0
dtype: float64

With axis=None

>>> df.kurt(axis=None).round(6)
-0.988693

Using axis=1

>>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
...                   index=['cat', 'dog'])
>>> df.kurt(axis=1)
cat   -6.0
dog   -6.0
dtype: float64

sparkle.PerformanceDataFrame.kurtosis(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
>>> s
cat    1
dog    2
dog    2
mouse  3
dtype: int64
>>> s.kurt()
1.5

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
...                   index=['cat', 'dog', 'dog', 'mouse'])
>>> df
       a   b
  cat  1   3
  dog  2   4
  dog  2   4
mouse  3   4
>>> df.kurt()
a   1.5
b   4.0
dtype: float64

With axis=None

>>> df.kurt(axis=None).round(6)
-0.988693

Using axis=1

>>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
...                   index=['cat', 'dog'])
>>> df.kurt(axis=1)
cat   -6.0
dog   -6.0
dtype: float64

sparkle.PerformanceDataFrame.last(self, offset) → None

Select final periods of time series data based on a date offset.

Deprecated since version 2.1: last() is deprecated and will be removed in a future version. Please create a mask and filter using .loc instead.

For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.

Parameters

offsetstr, DateOffset, dateutil.relativedelta: The offset length of the data that will be selected. For instance, ‘3D’ will display all the rows having their index within the last 3 days.

Returns

Series or DataFrame: A subset of the caller.

Raises

TypeError: If the index is not a DatetimeIndex

Notes

Deprecated since version 2.1.0: Please create a mask and filter using .loc instead

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the last 3 days:

>>> ts.last('3D')  
            A
2018-04-13  3
2018-04-15  4

Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.

sparkle.PerformanceDataFrame.last_valid_index(self) → Hashable | None

Return index for last non-NA value or None, if no non-NA value is found.

Returns

type of index

Examples

For Series:

>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2

>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If all elements in Series are NA/null, returns None.

>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If Series is empty, returns None.

For DataFrame:

>>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
>>> df
     A      B
0  NaN    NaN
1  NaN    3.0
2  2.0    4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2

>>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
>>> df
     A      B
0  None   None
1  None   None
2  None   None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If all elements in DataFrame are NA/null, returns None.

>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If DataFrame is empty, returns None.

sparkle.PerformanceDataFrame.le(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Less than or equal to of dataframe and other, element-wise (binary operator le).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.PerformanceDataFrame.lt(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Less than of dataframe and other, element-wise (binary operator lt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.PerformanceDataFrame.map(self, func: PythonFuncType, na_action: str | None = None, **kwargs) → DataFrame

Apply a function to a Dataframe elementwise.

Added in version 2.1.0: DataFrame.applymap was deprecated and renamed to DataFrame.map.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters

funccallable: Python function, returns a single value from a single value.
na_action{None, ‘ignore’}, default None: If ‘ignore’, propagate NaN values, without passing them to func.
**kwargs: Additional keyword arguments to pass as keywords arguments to func.

Returns

DataFrame: Transformed DataFrame.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.map(lambda x: len(str(x)))
1
3  4
5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
     0  1
0  NaN  4
1  5.0  5

It is also possible to use map with functions that are not lambda functions:

>>> df.map(round, ndigits=1)
  1
1.0  2.1
3.4  4.6

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.map(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it’s better to avoid map in that case.

>>> df ** 2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

sparkle.PerformanceDataFrame.marginal_contribution(self: PerformanceDataFrame, objective: str | SparkleObjective = None, instances: list[str] = None, sort: bool = False) → list[float]

Return the marginal contribution of the solver configuration on the instances.

Args:: objective: The objective for which we calculate the marginal contribution. instances: The instances which should be selected for the evaluation sort: Whether to sort the results afterwards
Returns:: The marginal contribution of each solver (configuration) as: [(solver, config_id, marginal_contribution, portfolio_best_performance_without_solver)]

sparkle.PerformanceDataFrame.mask(self, cond, other=<no_default>, *, inplace: bool_t = False, axis: Axis | None = None, level: Level | None = None) → Self | None

Replace values where the condition is True.

Parameters

condbool Series/DataFrame, array-like, or callable: Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
otherscalar, Series/DataFrame, or callable: Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).
inplacebool, default False: Whether to perform the operation in place on the data.
axisint, default None: Alignment axis if needed. For Series this parameter is unused and defaults to 0.
levelint, default None: Alignment level if needed.

Returns

Same type as caller or None if inplace=True.

Notes

The mask method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is False the element is used; otherwise the corresponding element from the DataFrame other is used. If the axis of other does not align with axis of cond Series/DataFrame, the misaligned index positions will be filled with True.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the mask documentation in indexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)
0    99
1     1
2    99
3    99
4    99
dtype: int64

>>> s.where(s > 1, 10)
  10
  10
  2
  3
  4
dtype: int64
>>> s.mask(s > 1, 10)
   0
   1
  10
  10
  10
dtype: int64

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

sparkle.PerformanceDataFrame.max(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.max()
8

sparkle.PerformanceDataFrame.mean(self: PerformanceDataFrame, objective: str = None, solver: str = None, instance: str = None) → float: Return the mean value of a slice of the dataframe.

sparkle.PerformanceDataFrame.median(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return the median of the values over the requested axis.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 3])
>>> s.median()
2.0

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.median()
a   1.5
b   2.5
dtype: float64

Using axis=1

>>> df.median(axis=1)
tiger   1.5
zebra   2.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
...                   index=['tiger', 'zebra'])
>>> df.median(numeric_only=True)
a   1.5
dtype: float64

sparkle.PerformanceDataFrame.melt(self, id_vars=None, value_vars=None, var_name=None, value_name: Hashable = 'value', col_level: Level | None = None, ignore_index: bool = True) → DataFrame

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters

id_varsscalar, tuple, list, or ndarray, optional: Column(s) to use as identifier variables.
value_varsscalar, tuple, list, or ndarray, optional: Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_namescalar, default None: Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_namescalar, default ‘value’: Name to use for the ‘value’ column, can’t be an existing column label.
col_levelscalar, optional: If columns are a MultiIndex then use this level to melt.
ignore_indexbool, default True: If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

Returns

DataFrame: Unpivoted DataFrame.

Notes

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
   A variable  value
a        B      1
b        B      3
c        B      5
a        C      2
b        C      4
c        C      6

The names of ‘variable’ and ‘value’ columns can be customized:

>>> df.melt(id_vars=['A'], value_vars=['B'],
...         var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

Original index values can be kept around:

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
   A variable  value
a        B      1
b        B      3
c        B      5
a        C      2
b        C      4
c        C      6

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]
>>> df
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5

sparkle.PerformanceDataFrame.memory_usage(self, index: bool = True, deep: bool = False) → Series

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters

indexbool, default True: Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.
deepbool, default False: If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values.

Returns

Series: A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Notes

See the Frequently Asked Questions for more details.

Examples

>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
...              for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
   int64  float64            complex128  object  bool
0      1      1.0              1.0+0.0j       1  True
1      1      1.0              1.0+0.0j       1  True
2      1      1.0              1.0+0.0j       1  True
3      1      1.0              1.0+0.0j       1  True
4      1      1.0              1.0+0.0j       1  True

>>> df.memory_usage()
Index           128
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)
Index            128
int64          40000
float64        40000
complex128     80000
object        180000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['object'].astype('category').memory_usage(deep=True)
5244

sparkle.PerformanceDataFrame.merge(self, right: DataFrame | Series, how: MergeHow = 'inner', on: IndexLabel | AnyArrayLike | None = None, left_on: IndexLabel | AnyArrayLike | None = None, right_on: IndexLabel | AnyArrayLike | None = None, left_index: bool = False, right_index: bool = False, sort: bool = False, suffixes: Suffixes = ('_x', '_y'), copy: bool | None = None, indicator: str | bool = False, validate: MergeValidate | None = None) → DataFrame

Merge DataFrame or named Series objects with a database-style join.

A named Series object is treated as a DataFrame with a single named column.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Warning

If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

Parameters

rightDataFrame or named Series

Object to merge with.

how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘inner’

Type of merge to be performed.

left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
cross: creates the cartesian product from both frames, preserves the order of the left keys.

onlabel or list

Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.

left_onlabel or list, or array-like

Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.

right_onlabel or list, or array-like

Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.

left_indexbool, default False

Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.

right_indexbool, default False

Use the index from the right DataFrame as the join key. Same caveats as left_index.

sortbool, default False

Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword).

suffixeslist-like, default is (“_x”, “_y”)

A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.

copybool, default True

If False, avoid copy if possible.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

indicatorbool or str, default False

If True, adds a column to the output DataFrame called “_merge” with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left DataFrame, “right_only” for observations whose merge key only appears in the right DataFrame, and “both” if the observation’s merge key is found in both DataFrames.

validatestr, optional

If specified, checks if merge is of specified type.

“one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
“one_to_many” or “1:m”: check if merge keys are unique in left dataset.
“many_to_one” or “m:1”: check if merge keys are unique in right dataset.
“many_to_many” or “m:m”: allowed, but does not result in checks.

Returns

DataFrame: A DataFrame of the two merged objects.

Examples

>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
>>> df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
foo        1  foo        5
foo        1  foo        8
bar        2  bar        6
baz        3  baz        7
foo        5  foo        5
foo        5  foo        8

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',
...           suffixes=('_left', '_right'))
  lkey  value_left rkey  value_right
0  foo           1  foo            5
1  foo           1  foo            8
2  bar           2  bar            6
3  baz           3  baz            7
4  foo           5  foo            5
5  foo           5  foo            8

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
    Index(['value'], dtype='object')

>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
      a  b
0   foo  1
1   bar  2
>>> df2
      a  c
0   foo  3
1   baz  4

>>> df1.merge(df2, how='inner', on='a')
      a  b  c
0   foo  1  3

>>> df1.merge(df2, how='left', on='a')
      a  b  c
0   foo  1  3.0
1   bar  2  NaN

>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
    left
0   foo
1   bar
>>> df2
    right
0   7
1   8

>>> df1.merge(df2, how='cross')
   left  right
0   foo      7
1   foo      8
2   bar      7
3   bar      8

sparkle.PerformanceDataFrame.min(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.min()
0

sparkle.PerformanceDataFrame.mod(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Modulo of dataframe and other, element-wise (binary operator mod).

Equivalent to dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmod.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.mode(self, axis: Axis = 0, numeric_only: bool = False, dropna: bool = True) → DataFrame

Get the mode(s) of each element along the selected axis.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to iterate over while searching for the mode:

0 or ‘index’ : get mode of each column
1 or ‘columns’ : get mode of each row.

numeric_onlybool, default False

If True, only apply to numeric columns.

dropnabool, default True

Don’t consider counts of NaN/NaT.

Returns

DataFrame: The modes of each column or row.

Examples

>>> df = pd.DataFrame([('bird', 2, 2),
...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN.

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis='columns', numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN

sparkle.PerformanceDataFrame.mul(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.multiply(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.ne(self, other, axis: Axis = 'columns', level=None) → DataFrame

Get Not equal to of dataframe and other, element-wise (binary operator ne).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

DataFrame of bool: Result of the comparison.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

sparkle.PerformanceDataFrame.nlargest(self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = 'first') → DataFrame

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters

nint

Number of rows to return.

columnslabel or list of labels

Column label(s) to order by.

keep{‘first’, ‘last’, ‘all’}, default ‘first’

Where there are duplicate values:

first : prioritize the first occurrence(s)
last : prioritize the last occurrence(s)
all : keep all the ties of the smallest item even if it means selecting more than n items.

Returns

DataFrame: The first n rows ordered by the given columns in descending order.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
...                                   434000, 434000, 337000, 11300,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column “population”.

>>> df.nlargest(3, 'population')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'population', keep='last')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', the number of element kept can go beyond n if there are duplicate values for the smallest element, all the ties are kept:

>>> df.nlargest(3, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

However, nlargest does not keep n distinct largest elements:

>>> df.nlargest(5, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['population', 'GDP'])
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

sparkle.PerformanceDataFrame.notna(self) → DataFrame

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()
0     True
1     True
2    False
dtype: bool

sparkle.PerformanceDataFrame.notnull(self) → DataFrame

DataFrame.notnull is an alias for DataFrame.notna.

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.

Returns

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                              pd.Timestamp('1940-04-25')],
...                        name=['Alfred', 'Batman', ''],
...                        toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()
0     True
1     True
2    False
dtype: bool

sparkle.PerformanceDataFrame.nsmallest(self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = 'first') → DataFrame

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters

nint

Number of items to retrieve.

columnslist or str

Column name or names to order by.

keep{‘first’, ‘last’, ‘all’}, default ‘first’

Where there are duplicate values:

first : take the first occurrence.
last : take the last occurrence.
all : keep all the ties of the largest item even if it means selecting more than n items.

Returns

DataFrame

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
...                                   434000, 434000, 337000, 337000,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column “population”.

>>> df.nsmallest(3, 'population')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, 'population', keep='last')
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', the number of element kept can go beyond n if there are duplicate values for the largest element, all the ties are kept.

>>> df.nsmallest(3, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

However, nsmallest does not keep n distinct smallest elements:

>>> df.nsmallest(4, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ['population', 'GDP'])
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR

sparkle.PerformanceDataFrame.nunique(self, axis: Axis = 0, dropna: bool = True) → Series

Count number of distinct elements in specified axis.

Return Series with number of distinct elements. Can ignore NaN values.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
dropnabool, default True: Don’t include NaN in the counts.

Returns

Series

Examples

>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})
>>> df.nunique()
A    3
B    2
dtype: int64

>>> df.nunique(axis=1)
0    1
1    2
2    2
dtype: int64

Fill NA/NaN values by propagating the last valid observation to next valid.

Deprecated since version 2.0: Series/DataFrame.pad is deprecated. Use Series/DataFrame.ffill instead.

Returns

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

Please see examples for DataFrame.ffill() or Series.ffill().

sparkle.PerformanceDataFrame.pct_change(self, periods: int = 1, fill_method: ~typing.Literal['backfill', 'bfill', 'ffill', 'pad'] | None | ~typing.Literal[_NoDefault.no_default] = <no_default>, limit: int | None | ~typing.Literal[_NoDefault.no_default] = <no_default>, freq=None, **kwargs) → None

Fractional change between the current and a prior element.

Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.

Note

Despite the name of this method, it calculates fractional change (also known as per unit change or relative change) and not percentage change. If you need the percentage change, multiply these values by 100.

Parameters

periodsint, default 1: Periods to shift for forming percent change.
fill_method{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default ‘pad’: How to handle NAs before computing percent changes.

Deprecated since version 2.1: All options of fill_method are deprecated except fill_method=None.
limitint, default None: The number of consecutive NAs to fill before stopping.

Deprecated since version 2.1.
freqDateOffset, timedelta, or str, optional: Increment to use from time series API (e.g. ‘ME’ or BDay()).
**kwargs: Additional keyword arguments are passed into DataFrame.shift or Series.shift.

Returns

Series or DataFrame: The same type as the calling object.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64

>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64

>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64

>>> s.ffill().pct_change()
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13

>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351

>>> df.pct_change(axis='columns', periods=-1)
          2016      2015  2014
GOOG  0.179241  0.094112   NaN
APPL -0.252395 -0.011860   NaN

sparkle.PerformanceDataFrame.pipe(self, func: Callable[[...], T] | tuple[Callable[[...], T], str], *args, **kwargs) → T

Apply chainable functions that expect Series or DataFrames.

Parameters

funcfunction: Function to apply to the Series/DataFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
*argsiterable, optional: Positional arguments passed into func.
**kwargsmapping, optional: A dictionary of keyword arguments passed into func.

Returns

the return type of func.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects.

Examples

Constructing a income DataFrame from a dictionary.

>>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
>>> df = pd.DataFrame(data, columns=['Salary', 'Others'])
>>> df
   Salary  Others
0    8000  1000.0
1    9500     NaN
2    5000  2000.0

Functions that perform tax reductions on an income DataFrame.

>>> def subtract_federal_tax(df):
...     return df * 0.9
>>> def subtract_state_tax(df, rate):
...     return df * (1 - rate)
>>> def subtract_national_insurance(df, rate, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)

Instead of writing

>>> subtract_national_insurance(
...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
...     rate=0.05,
...     rate_increase=0.02)  

You can write

>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose national_insurance takes its data as df in the second argument:

>>> def subtract_national_insurance(rate, df, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)
>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(
...         (subtract_national_insurance, 'df'),
...         rate=0.05,
...         rate_increase=0.02
...     )
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12

sparkle.PerformanceDataFrame.pivot(self, *, columns, index=<no_default>, values=<no_default>) → DataFrame

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a “pivot” table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the User Guide for more on reshaping.

Parameters

columnsstr or object or a list of str: Column to use to make new frame’s columns.
indexstr or object or a list of str, optional: Column to use to make new frame’s index. If not given, uses existing index.
valuesstr, object or a list of the previous, optional: Column(s) to use for populating new frame’s values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.

Returns

DataFrame: Returns reshaped DataFrame.

Raises

ValueError:: When there are any index, columns combinations with multiple values. DataFrame.pivot_table when you need to aggregate.

Notes

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
...                            'two'],
...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
...                    'baz': [1, 2, 3, 4, 5, 6],
...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
    foo   bar  baz  zoo
0   one   A    1    x
1   one   B    2    y
2   one   C    3    z
3   two   A    4    q
4   two   B    5    w
5   two   C    6    t

>>> df.pivot(index='foo', columns='bar', values='baz')
bar  A   B   C
foo
one  1   2   3
two  4   5   6

>>> df.pivot(index='foo', columns='bar')['baz']
bar  A   B   C
foo
one  1   2   3
two  4   5   6

>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>> df = pd.DataFrame({
...        "lev1": [1, 1, 1, 2, 2, 2],
...        "lev2": [1, 1, 2, 1, 1, 2],
...        "lev3": [1, 2, 1, 2, 1, 2],
...        "lev4": [1, 2, 3, 4, 5, 6],
...        "values": [0, 1, 2, 3, 4, 5]})
>>> df
    lev1 lev2 lev3 lev4 values
0   1    1    1    1    0
1   1    1    2    2    1
2   1    2    1    3    2
3   2    1    2    4    3
4   2    1    1    5    4
5   2    2    2    6    5

>>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
lev2    1         2
lev3    1    2    1    2
lev1
1     0.0  1.0  2.0  NaN
2     4.0  3.0  NaN  5.0

>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
      lev3    1    2
lev1  lev2
   1     1  0.0  1.0
         2  2.0  NaN
   2     1  4.0  3.0
         2  NaN  5.0

A ValueError is raised if there are any duplicates.

>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
...                    "bar": ['A', 'A', 'B', 'C'],
...                    "baz": [1, 2, 3, 4]})
>>> df
   foo bar  baz
0  one   A    1
1  one   A    2
2  two   B    3
3  two   C    4

Notice that the first two rows are the same for our index and columns arguments.

>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
   ...
ValueError: Index contains duplicate entries, cannot reshape

sparkle.PerformanceDataFrame.pivot_table(self, values=None, index=None, columns=None, aggfunc: AggFuncType = 'mean', fill_value=None, margins: bool = False, dropna: bool = True, margins_name: Level = 'All', observed: bool | lib.NoDefault = <no_default>, sort: bool = True) → DataFrame

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters

valueslist-like or scalar, optional: Column or columns to aggregate.
indexcolumn, Grouper, array, or list of the previous: Keys to group by on the pivot table index. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.
columnscolumn, Grouper, array, or list of the previous: Keys to group by on the pivot table column. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.
aggfuncfunction, list of functions, dict, default “mean”: If a list of functions is passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves). If a dict is passed, the key is column to aggregate and the value is function or list of functions. If margin=True, aggfunc will be used to calculate the partial aggregates.
fill_valuescalar, default None: Value to replace missing values with (in the resulting pivot table, after aggregation).
marginsbool, default False: If margins=True, special All columns and rows will be added with partial group aggregates across the categories on the rows and columns.
dropnabool, default True: Do not include columns whose entries are all NaN. If True, rows with a NaN value in any column will be omitted before computing margins.
margins_namestr, default ‘All’: Name of the row / column that will contain the totals when margins is True.
observedbool, default False: This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Deprecated since version 2.2.0: The default value of False is deprecated and will change to True in a future version of pandas.
sortbool, default True: Specifies if the result should be sorted.

Added in version 1.3.0.

Returns

DataFrame: An Excel style pivot table.

Notes

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9

This first example aggregates values by taking the sum.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                        columns=['C'], aggfunc="sum")
>>> table
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

We can also fill missing values using the fill_value parameter.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                        columns=['C'], aggfunc="sum", fill_value=0)
>>> table
C        large  small
A   B
bar one      4      5
    two      7      6
foo one      4      1
    two      0      6

The next example aggregates by taking the mean across multiple columns.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                        aggfunc={'D': "mean", 'E': "mean"})
>>> table
                D         E
A   C
bar large  5.500000  7.500000
    small  5.500000  8.500000
foo large  2.000000  4.500000
    small  2.333333  4.333333

We can also calculate multiple types of aggregations for any given value column.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                        aggfunc={'D': "mean",
...                                 'E': ["min", "max", "mean"]})
>>> table
                  D   E
               mean max      mean  min
A   C
bar large  5.500000   9  7.500000    6
    small  5.500000   9  8.500000    8
foo large  2.000000   5  4.500000    4
    small  2.333333   6  4.333333    2

sparkle.PerformanceDataFrame.pop(self, item: Hashable) → Series

Return item and drop from frame. Raise KeyError if not found.

Parameters

itemlabel: Label of column to be popped.

Returns

Series

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

>>> df.pop('class')
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object

>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN

sparkle.PerformanceDataFrame.pow(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator pow).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.prod(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs)

Return the product of the values over the requested axis.

Parameters

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.prod with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0

>>> pd.Series([np.nan]).prod(min_count=1)
nan

sparkle.PerformanceDataFrame.product(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs)

Return the product of the values over the requested axis.

Parameters

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.prod with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0

>>> pd.Series([np.nan]).prod(min_count=1)
nan

sparkle.PerformanceDataFrame.quantile(self, q: float | AnyArrayLike | Sequence[float] = 0.5, axis: Axis = 0, numeric_only: bool = False, interpolation: QuantileInterpolation = 'linear', method: Literal['single', 'table'] = 'single') → Series | DataFrame

Return values at the given quantile over requested axis.

Parameters

qfloat or array-like, default 0.5 (50% quantile)

Value between 0 <= q <= 1, the quantile(s) to compute.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

numeric_onlybool, default False

Include only float, int or boolean data.

Changed in version 2.0.0: The default value of numeric_only is now False.

interpolation{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:

linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
lower: i.
higher: j.
nearest: i or j whichever is nearest.
midpoint: (i + j) / 2.

method{‘single’, ‘table’}, default ‘single’

Whether to compute quantiles per-column (‘single’) or over all columns (‘table’). When ‘table’, the only allowed interpolation methods are ‘nearest’, ‘lower’, and ‘higher’.

Returns

Series or DataFrame

If q is an array, a DataFrame will be returned where the
index is q, the columns are the columns of self, and the values are the quantiles.

If q is a float, a Series will be returned where the
index is the columns of self and the values are the quantiles.

Examples

>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
...                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying method=’table’ will compute the quantile over all columns.

>>> df.quantile(.1, method="table", interpolation="nearest")
a    1
b    1
Name: 0.1, dtype: int64
>>> df.quantile([.1, .5], method="table", interpolation="nearest")
     a    b
0.1  1    1
0.5  3  100

Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

>>> df = pd.DataFrame({'A': [1, 2],
...                    'B': [pd.Timestamp('2010'),
...                          pd.Timestamp('2011')],
...                    'C': [pd.Timedelta('1 days'),
...                          pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object

sparkle.PerformanceDataFrame.query(self, expr: str, *, inplace: bool = False, **kwargs) → DataFrame | None

Query the columns of a DataFrame with a boolean expression.

Parameters

exprstr

The query string to evaluate.

You can refer to variables in the environment by prefixing them with an ‘@’ character like @a + b.

You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named “Area (cm^2)” would be referenced as `Area (cm^2)`). Column names which are Python keywords (like “list”, “for”, “import”, etc) cannot be used.

For example, if one of your columns is called a a and you want to sum it with b, your query should be `a a` + b.

inplacebool

Whether to modify the DataFrame rather than creating a new one.

**kwargs

See the documentation for eval() for complete details on the keyword arguments accepted by DataFrame.query().

Returns

DataFrame or None: DataFrame resulting from the provided query expression or None if inplace=True.

Notes

The result of the evaluation of this expression is first passed to DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to DataFrame.__getitem__().

This method uses the top-level eval() function to evaluate the passed query.

The query() method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, and and or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The DataFrame.index and DataFrame.columns attributes of the DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.

For further details and examples see the query documentation in indexing.

Backtick quoted variables

Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.

During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped).

In a special case, quotes that make a pair around a backtick can confuse the parser. For example, `it's` > `that's` will raise an error, as it forms a quoted string ('s > `that') with a backtick inside.

See also the Python documentation about lexical analysis (https://docs.python.org/3/reference/lexical_analysis.html) in combination with the source code in pandas.core.computation.parsing.

Examples

>>> df = pd.DataFrame({'A': range(1, 6),
...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
   A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.query('A > B')
   A  B  C C
4  5  2    6

The previous expression is equivalent to

>>> df[df.A > df.B]
   A  B  C C
4  5  2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
   A   B  C C
0  1  10   10

The previous expression is equivalent to

>>> df[df.B == df['C C']]
   A   B  C C
0  1  10   10

sparkle.PerformanceDataFrame.radd(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Addition of dataframe and other, element-wise (binary operator radd).

Equivalent to other + dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, add.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.rank(self, axis: int | Literal['index', 'columns', 'rows'] = 0, method: Literal['average', 'min', 'max', 'first', 'dense'] = 'average', numeric_only: bool = False, na_option: Literal['keep', 'top', 'bottom'] = 'keep', ascending: bool = True, pct: bool = False) → None

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Index to direct ranking. For Series this parameter is unused and defaults to 0.

method{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’

How to rank the group of records that have the same value (i.e. ties):

average: average rank of the group
min: lowest rank in the group
max: highest rank in the group
first: ranks assigned in order they appear in the array
dense: like ‘min’, but rank always increases by 1 between groups.

numeric_onlybool, default False

For DataFrame objects, rank only numeric columns if set to True.

Changed in version 2.0.0: The default value of numeric_only is now False.

na_option{‘keep’, ‘top’, ‘bottom’}, default ‘keep’

How to rank NaN values:

keep: assign NaN rank to NaN values
top: assign lowest rank to NaN values
bottom: assign highest rank to NaN values

ascendingbool, default True

Whether or not the elements should be ranked in ascending order.

pctbool, default False

Whether or not to display the returned rankings in percentile form.

Returns

same type as caller: Return a Series or DataFrame with data ranks as values.

Examples

>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

Ties are assigned the mean of the ranks (by default) for the group.

>>> s = pd.Series(range(5), index=list("abcde"))
>>> s["d"] = s["b"]
>>> s.rank()
a    1.0
b    2.5
c    4.0
d    2.5
e    5.0
dtype: float64

The following example shows how the method behaves with the above parameters:

default_rank: this is the default behaviour obtained without using any parameter.
max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.)
NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.
pct_rank: when setting pct = True, the ranking is expressed as percentile rank.

>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN

sparkle.PerformanceDataFrame.rdiv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.reindex(self, labels=None, *, index=None, columns=None, axis: Axis | None = None, method: ReindexMethod | None = None, copy: bool | None = None, level: Level | None = None, fill_value: Scalar | None = nan, limit: int | None = None, tolerance=None) → DataFrame

Conform DataFrame to new index with optional filling logic.

Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters

labelsarray-like, optional

New labels / index to conform the axis specified by ‘axis’ to.

indexarray-like, optional

New labels for the index. Preferably an Index object to avoid duplicating data.

columnsarray-like, optional

New labels for the columns. Preferably an Index object to avoid duplicating data.

axisint or str, optional

Axis to target. Can be either the axis name (‘index’, ‘columns’) or number (0, 1).

method{None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}

Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

None (default): don’t fill gaps
pad / ffill: Propagate last valid observation forward to next valid.
backfill / bfill: Use next valid observation to fill gap.
nearest: Use nearest valid observations to fill gap.

copybool, default True

Return a new object, even if the passed indexes are the same.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

levelint or name

Broadcast across a level, matching Index values on the passed MultiIndex level.

fill_valuescalar, default np.nan

Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

limitint, default None

Maximum number of consecutive elements to forward or backward fill.

toleranceoptional

Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns

DataFrame with changed index.

Examples

DataFrame.reindex supports two calling conventions

(index=index_labels, columns=column_labels, ...)
(labels, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Create a dataframe with some fictional data.

>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301],
...                   'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
...                   index=index)
>>> df
           http_status  response_time
Firefox            200           0.04
Chrome             200           0.02
Safari             404           0.07
IE10               404           0.08
Konqueror          301           1.00

Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned NaN.

>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...              'Chrome']
>>> df.reindex(new_index)
               http_status  response_time
Safari               404.0           0.07
Iceweasel              NaN            NaN
Comodo Dragon          NaN            NaN
IE10                 404.0           0.08
Chrome               200.0           0.02

We can fill in the missing values by passing a value to the keyword fill_value. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

>>> df.reindex(new_index, fill_value=0)
               http_status  response_time
Safari                 404           0.07
Iceweasel                0           0.00
Comodo Dragon            0           0.00
IE10                   404           0.08
Chrome                 200           0.02

>>> df.reindex(new_index, fill_value='missing')
              http_status response_time
Safari                404          0.07
Iceweasel         missing       missing
Comodo Dragon     missing       missing
IE10                  404          0.08
Chrome                200          0.02

We can also reindex the columns.

>>> df.reindex(columns=['http_status', 'user_agent'])
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

Or we can use “axis-style” keyword arguments

>>> df.reindex(['http_status', 'user_agent'], axis="columns")
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

To further illustrate the filling functionality in reindex, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates).

>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
...                    index=date_index)
>>> df2
            prices
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0

Suppose we decide to expand the dataframe to cover a wider date range.

>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
            prices
2009-12-29     NaN
2009-12-30     NaN
2009-12-31     NaN
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

The index entries that did not have a value in the original data frame (for example, ‘2009-12-29’) are by default filled with NaN. If desired, we can fill in the missing values using one of several options.

For example, to back-propagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

>>> df2.reindex(date_index2, method='bfill')
            prices
2009-12-29   100.0
2009-12-30   100.0
2009-12-31   100.0
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

Please note that the NaN value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original dataframe, use the fillna() method.

See the user guide for more.

sparkle.PerformanceDataFrame.reindex_like(self, other, method: Literal['backfill', 'bfill', 'pad', 'ffill', 'nearest'] | None = None, copy: bool | None = None, limit: int | None = None, tolerance=None) → None

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters

otherObject of the same data type

Its row and column indices are used to define the new indices of this object.

method{None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}

Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

None (default): don’t fill gaps
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use next valid observation to fill gap
nearest: use nearest valid observations to fill gap.

copybool, default True

Return a new object, even if the passed indexes are the same.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

limitint, default None

Maximum number of consecutive labels to fill for inexact matches.

toleranceoptional

Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns

Series or DataFrame: Same type as caller, but with changed indices on each axis.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...).

Examples

>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
...                     [31, 87.8, 'high'],
...                     [22, 71.6, 'medium'],
...                     [35, 95, 'medium']],
...                    columns=['temp_celsius', 'temp_fahrenheit',
...                             'windspeed'],
...                    index=pd.date_range(start='2014-02-12',
...                                        end='2014-02-15', freq='D'))

>>> df1
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium

>>> df2 = pd.DataFrame([[28, 'low'],
...                     [30, 'low'],
...                     [35.1, 'medium']],
...                    columns=['temp_celsius', 'windspeed'],
...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
...                                            '2014-02-15']))

>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium

>>> df2.reindex_like(df1)
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       NaN
2014-02-15          35.1              NaN    medium

sparkle.PerformanceDataFrame.remove_configuration(self: PerformanceDataFrame, solver: str, configuration: str | list[str]) → None: Drop one or more configurations from the Dataframe.

sparkle.PerformanceDataFrame.remove_empty_runs(self: PerformanceDataFrame) → None: Remove runs that contain no data, except for the first.

sparkle.PerformanceDataFrame.remove_instances(self: PerformanceDataFrame, instances: str | list[str]) → None: Drop instances from the Dataframe.

sparkle.PerformanceDataFrame.remove_objective(self: PerformanceDataFrame, objectives: str | list[str]) → None: Remove objective from the Dataframe.

sparkle.PerformanceDataFrame.remove_runs(self: PerformanceDataFrame, runs: int | list[int], instance_names: list[str] = None) → None

Drop one or more runs from the Dataframe.

Args:

runs: The run indices to be removed. If its an int,: the last n runs are removed. NOTE: If each instance has a different number of runs, the amount of removed runs is not uniform.
instance_names: The instances for which runs are to be removed.: By default None, which means runs are removed from all instances.

sparkle.PerformanceDataFrame.remove_solver(self: PerformanceDataFrame, solvers: str | list[str]) → None: Drop one or more solvers from the Dataframe.

Rename columns or index labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

See the user guide for more.

Parameters

mapperdict-like or function: Dict-like or function transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.
indexdict-like or function: Alternative to specifying axis (mapper, axis=0 is equivalent to index=mapper).
columnsdict-like or function: Alternative to specifying axis (mapper, axis=1 is equivalent to columns=mapper).
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.
copybool, default True: Also copy underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one. If True then value of copy is ignored.
levelint or level name, default None: In case of a MultiIndex, only rename labels in the specified level.
errors{‘ignore’, ‘raise’}, default ‘ignore’: If ‘raise’, raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed. If ‘ignore’, existing keys will be renamed and extra keys will be ignored.

Returns

DataFrame or None: DataFrame with the renamed axis labels or None if inplace=True.

Raises

KeyError: If any of the labels is not found in the selected axis and “errors=’raise’”.

Examples

DataFrame.rename supports two calling conventions

(index=index_mapper, columns=columns_mapper, ...)
(mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')

>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters:

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6

>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6

sparkle.PerformanceDataFrame.rename_axis(self, mapper: IndexLabel | lib.NoDefault = <no_default>, *, index=<no_default>, columns=<no_default>, axis: Axis = 0, copy: bool_t | None = None, inplace: bool_t = False) → Self | None

Set the name of the axis for the index or columns.

Parameters

mapperscalar, list-like, optional

Value to set the axis name attribute.

index, columnsscalar, list-like, dict-like or function, optional

A scalar, list-like, dict-like or functions transformations to apply to that axis’ values. Note that the columns parameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects.

Use either mapper and axis to specify the axis to target with mapper, or index and/or columns.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to rename. For Series this parameter is unused and defaults to 0.

copybool, default None

Also copy underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

inplacebool, default False

Modifies the object directly, instead of creating a new Series or DataFrame.

Returns

Series, DataFrame, or None: The same type as the caller or None if inplace=True.

Notes

DataFrame.rename_axis supports two calling conventions

(index=index_mapper, columns=columns_mapper, ...)
(mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

Series

>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0       dog
1       cat
2    monkey
dtype: object
>>> s.rename_axis("animal")
animal
0    dog
1    cat
2    monkey
dtype: object

DataFrame

>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
...                    "num_arms": [0, 0, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product([['mammal'],
...                                        ['dog', 'cat', 'monkey']],
...                                       names=['type', 'name'])
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(index={'type': 'class'})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2

sparkle.PerformanceDataFrame.reorder_levels(self, order: Sequence[int | str], axis: Axis = 0) → DataFrame

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters

orderlist of int or list of str: List representing new level order. Reference level by number (position) or by key (label).
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Where to reorder levels.

Returns

DataFrame

Examples

>>> data = {
...     "class": ["Mammals", "Mammals", "Reptiles"],
...     "diet": ["Omnivore", "Carnivore", "Carnivore"],
...     "species": ["Humans", "Dogs", "Snakes"],
... }
>>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
>>> df = df.set_index(["class", "diet"])
>>> df
                                  species
class      diet
Mammals    Omnivore                Humans
           Carnivore                 Dogs
Reptiles   Carnivore               Snakes

Let’s reorder the levels of the index:

>>> df.reorder_levels(["diet", "class"])
                                  species
diet      class
Omnivore  Mammals                  Humans
Carnivore Mammals                    Dogs
          Reptiles                 Snakes

sparkle.PerformanceDataFrame.replace(self, to_replace=None, value=<no_default>, *, inplace: bool_t = False, limit: int | None = None, regex: bool_t = False, method: Literal['pad', 'ffill', 'bfill'] | lib.NoDefault = <no_default>) → Self | None

Replace values given in to_replace with value.

Values of the Series/DataFrame are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Parameters

to_replacestr, regex, list, dict, Series, int, float, or None

How to find the values that will be replaced.

numeric, str or regex:
- numeric: numeric values equal to to_replace will be replaced with value
- str: string exactly matching to_replace will be replaced with value
- regex: regexs matching to_replace will be replaced with value
list of str, regex, or numeric:
- First, if to_replace and value are both lists, they must be the same length.
- Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn’t matter much for value since there are only a few possible substitution regexes you can use.
- str, regex and numeric rules apply as above.
dict:
- Dicts can be used to specify different replacement values for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value ‘a’ with ‘b’ and ‘y’ with ‘z’. To use a dict in this way, the optional value parameter should not be given.
- For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column ‘a’ and the value ‘z’ in column ‘b’ and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.
- For a DataFrame nested dictionaries, e.g., {'a': {'b': np.nan}}, are read as follows: look in column ‘a’ for the value ‘b’ and replace it with NaN. The optional value parameter should not be specified to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
None:
- This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.

See the examples section for examples of each of these.

valuescalar, dict, list, str, regex, default None

Value to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.

inplacebool, default False

If True, performs operation inplace and returns None.

limitint, default None

Maximum size gap to forward or backward fill.

Deprecated since version 2.1.0.

regexbool or same types as to_replace, default False

Whether to interpret to_replace and/or value as regular expressions. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.

method{‘pad’, ‘ffill’, ‘bfill’}

The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

Deprecated since version 2.1.0.

Returns

Series/DataFrame: Object after replacement.

Raises

AssertionError

If regex is not a bool and to_replace is not None.

TypeError

If to_replace is not a scalar, array-like, dict, or None
If to_replace is a dict and value is not a list, dict, ndarray, or Series
If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series.
When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced

ValueError

If a list or an ndarray is passed to to_replace and value but they are not the same length.

Notes

Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.
When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.

Examples

Scalar `to_replace` and `value`

>>> s = pd.Series([1, 2, 3, 4, 5])
>>> s.replace(1, 5)
0    5
1    2
2    3
3    4
4    5
dtype: int64

>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
...                    'B': [5, 6, 7, 8, 9],
...                    'C': ['a', 'b', 'c', 'd', 'e']})
>>> df.replace(0, 5)
    A  B  C
0  5  5  a
1  1  6  b
2  2  7  c
3  3  8  d
4  4  9  e

List-like `to_replace`

>>> df.replace([0, 1, 2, 3], 4)
    A  B  C
4  5  a
4  6  b
4  7  c
4  8  d
4  9  e

>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
    A  B  C
4  5  a
3  6  b
2  7  c
1  8  d
4  9  e

>>> s.replace([1, 2], method='bfill')
  3
  3
  3
  4
  5
dtype: int64

dict-like `to_replace`

>>> df.replace({0: 10, 1: 100})
        A  B  C
 10  5  a
100  6  b
  2  7  c
  3  8  d
  4  9  e

>>> df.replace({'A': 0, 'B': 5}, 100)
        A    B  C
100  100  a
  1    6  b
  2    7  c
  3    8  d
  4    9  e

>>> df.replace({'A': {0: 100, 4: 400}})
        A  B  C
100  5  a
  1  6  b
  2  7  c
  3  8  d
400  9  e

Regular expression `to_replace`

>>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
...                    'B': ['abc', 'bar', 'xyz']})
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
        A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
        A    B
0   new  abc
1   foo  bar
2  bait  xyz

>>> df.replace(regex=r'^ba.$', value='new')
        A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})
        A    B
0   new  abc
1   xyz  new
2  bait  xyz

>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
        A    B
0   new  abc
1   new  new
2  bait  xyz

Compare the behavior of s.replace({'a': None}) and s.replace('a', None) to understand the peculiarities of the to_replace parameter:

>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])

When one uses a dict as the to_replace value, it is like the value(s) in the dict are equal to the value parameter. s.replace({'a': None}) is equivalent to s.replace(to_replace={'a': None}, value=None, method=None):

>>> s.replace({'a': None})
    10
  None
  None
     b
  None
dtype: object

When value is not explicitly passed and to_replace is a scalar, list or tuple, replace uses the method parameter (default ‘pad’) to do the replacement. So this is why the ‘a’ values are being replaced by 10 in rows 1 and 2 and ‘b’ in row 4 in this case.

>>> s.replace('a')
  10
  10
  10
   b
   b
dtype: object

Deprecated since version 2.1.0: The ‘method’ parameter and padding behavior are deprecated.

On the other hand, if None is explicitly passed for value, it will be respected:

>>> s.replace('a', None)
    10
  None
  None
     b
  None
dtype: object

Changed in version 1.4.0: Previously the explicit None was silently ignored.

When regex=True, value is not None and to_replace is a string, the replacement will be applied in all columns of the DataFrame.

>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
...                    'B': ['a', 'b', 'c', 'd', 'e'],
...                    'C': ['f', 'g', 'h', 'i', 'j']})

>>> df.replace(to_replace='^[a-g]', value='e', regex=True)
    A  B  C
0  e  e
1  e  e
2  e  h
3  e  i
4  e  j

If value is not None and to_replace is a dictionary, the dictionary keys will be the DataFrame columns that the replacement will be applied.

>>> df.replace(to_replace={'B': '^[a-c]', 'C': '^[h-j]'}, value='e', regex=True)
    A  B  C
0  e  f
1  e  g
2  e  e
3  d  e
4  e  e

sparkle.PerformanceDataFrame.resample(self, rule, axis: Axis | lib.NoDefault = <no_default>, closed: Literal['right', 'left'] | None = None, label: Literal['right', 'left'] | None = None, convention: Literal['start', 'end', 's', 'e'] | lib.NoDefault = <no_default>, kind: Literal['timestamp', 'period'] | None | lib.NoDefault = <no_default>, on: Level | None = None, level: Level | None = None, origin: str | TimestampConvertibleTypes = 'start_day', offset: TimedeltaConvertibleTypes | None = None, group_keys: bool_t = False) → Resampler

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the on/level keyword parameter.

Parameters

ruleDateOffset, Timedelta or str

The offset string or object representing target conversion.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Which axis to use for up- or down-sampling. For Series this parameter is unused and defaults to 0. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.

Deprecated since version 2.0.0: Use frame.T.resample(…) instead.

closed{‘right’, ‘left’}, default None

Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

label{‘right’, ‘left’}, default None

Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

convention{‘start’, ‘end’, ‘s’, ‘e’}, default ‘start’

For PeriodIndex only, controls whether to use the start or end of rule.

Deprecated since version 2.2.0: Convert PeriodIndex to DatetimeIndex before resampling instead.

kind{‘timestamp’, ‘period’}, optional, default None

Pass ‘timestamp’ to convert the resulting index to a DateTimeIndex or ‘period’ to convert it to a PeriodIndex. By default the input representation is retained.

Deprecated since version 2.2.0: Convert index to desired type explicitly instead.

onstr, optional

For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

levelstr or int, optional

For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.

originTimestamp or str, default ‘start_day’

The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be one of the following:

‘epoch’: origin is 1970-01-01
‘start’: origin is the first value of the timeseries
‘start_day’: origin is the first day at midnight of the timeseries
‘end’: origin is the last value of the timeseries
‘end_day’: origin is the ceiling midnight of the last day

Added in version 1.3.0.

Note

Only takes effect for Tick-frequencies (i.e. fixed frequencies like days, hours, and minutes, rather than months or quarters).

offsetTimedelta or str, default is None

An offset timedelta added to the origin.

group_keysbool, default False

Whether to include the group keys in the result index when using .apply() on the resampled object.

Added in version 1.5.0: Not specifying group_keys will retain values-dependent behavior from pandas 1.4 and earlier (see pandas 1.5.0 Release notes for examples).

Changed in version 2.0.0: group_keys now defaults to False.

Returns

pandas.api.typing.Resampler: Resampler object.

Notes

See the user guide for more.

To learn more about the offset strings, please see this link.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='min')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: min, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample('3min').sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3min, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3).

>>> series.resample('3min', label='right').sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3min, dtype: int64

To include this value close the right side of the bin interval, as shown below.

>>> series.resample('3min', label='right', closed='right').sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3min, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample('30s').asfreq()[0:5]   # Select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30s, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the ffill method.

>>> series.resample('30s').ffill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30s, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample('30s').bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30s, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(arraylike):
...     return np.sum(arraylike) + 5
...
>>> series.resample('3min').apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3min, dtype: int64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
...                                     periods=8,
...                                     freq='W')
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
>>> df.resample('ME', on='week_starting').mean()
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df2 = pd.DataFrame(
...     d2,
...     index=pd.MultiIndex.from_product(
...         [days, ['morning', 'afternoon']]
...     )
... )
>>> df2
                      price  volume
2000-01-01 morning       10      50
           afternoon     11      60
2000-01-02 morning        9      40
           afternoon     13     100
2000-01-03 morning       14      50
           afternoon     18     100
2000-01-04 morning       17      40
           afternoon     19      50
>>> df2.resample('D', level=0).sum()
            price  volume
2000-01-01     21     110
2000-01-02     22     140
2000-01-03     32     150
2000-01-04     36      90

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7min, dtype: int64

>>> ts.resample('17min').sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17min, dtype: int64

>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17min, dtype: int64

>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17min, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17min, dtype: int64

>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17min, dtype: int64

If you want to take the largest Timestamp as the end of the bins:

>>> ts.resample('17min', origin='end').sum()
2000-10-01 23:35:00     0
2000-10-01 23:52:00    18
2000-10-02 00:09:00    27
2000-10-02 00:26:00    63
Freq: 17min, dtype: int64

In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

>>> ts.resample('17min', origin='end_day').sum()
2000-10-01 23:38:00     3
2000-10-01 23:55:00    15
2000-10-02 00:12:00    45
2000-10-02 00:29:00    45
Freq: 17min, dtype: int64

sparkle.PerformanceDataFrame.reset_index(self, level: IndexLabel | None = None, *, drop: bool = False, inplace: bool = False, col_level: Hashable = 0, col_fill: Hashable = '', allow_duplicates: bool | lib.NoDefault = <no_default>, names: Hashable | Sequence[Hashable] | None = None) → DataFrame | None

Reset the index, or a level of it.

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters

levelint, str, tuple, or list, default None: Only remove the given levels from the index. Removes all levels by default.
dropbool, default False: Do not try to insert index into dataframe columns. This resets the index to the default integer index.
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one.
col_levelint or str, default 0: If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level.
col_fillobject, default ‘’: If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.
allow_duplicatesbool, optional, default lib.no_default: Allow duplicate column labels to be created.

Added in version 1.5.0.
namesint, str or 1-dimensional list, default None: Using the given string, rename the DataFrame column which contains the index data. If the DataFrame has a MultiIndex, this has to be a list or tuple with length equal to the number of levels.

Added in version 1.5.0.

Returns

DataFrame or None: DataFrame with the new index or None if inplace=True.

Examples

>>> df = pd.DataFrame([('bird', 389.0),
...                    ('bird', 24.0),
...                    ('mammal', 80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
...                    (24.0, 'fly'),
...                    (80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

Using the names parameter, choose a name for the index column:

>>> df.reset_index(names=['classes', 'names'])
  classes   names  speed species
                     max    type
0    bird  falcon  389.0     fly
1    bird  parrot   24.0     fly
2  mammal    lion   80.5     run
3  mammal  monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

sparkle.PerformanceDataFrame.reset_value(self: PerformanceDataFrame, solver: str, instance: str, objective: str = None, run: int = None) → None: Reset a value in the dataframe.

sparkle.PerformanceDataFrame.rfloordiv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).

Equivalent to other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, floordiv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.rmod(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Modulo of dataframe and other, element-wise (binary operator rmod).

Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mod.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.rmul(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator rmul).

Equivalent to other * dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

Provide rolling window calculations.

Parameters

windowint, timedelta, str, offset, or BaseIndexer subclass

Size of the moving window.

If an integer, the fixed number of observations used for each window.

If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. To learn more about the offsets & frequency strings, please see this link.

If a BaseIndexer subclass, the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, closed and step will be passed to get_window_bounds.

min_periodsint, default None

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

For a window that is specified by an offset, min_periods will default to 1.

For a window that is specified by an integer, min_periods will default to the size of the window.

centerbool, default False

If False, set the window labels as the right edge of the window index.

If True, set the window labels as the center of the window index.

win_typestr, default None

If None, all points are evenly weighted.

If a string, it must be a valid scipy.signal window function.

Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature.

onstr, optional

For a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame’s index.

Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.

axisint or str, default 0

If 0 or 'index', roll across the rows.

If 1 or 'columns', roll across the columns.

For Series this parameter is unused and defaults to 0.

Deprecated since version 2.1.0: The axis keyword is deprecated. For axis=1, transpose the DataFrame first instead.

closedstr, default None

If 'right', the first point in the window is excluded from calculations.

If 'left', the last point in the window is excluded from calculations.

If 'both', the no points in the window are excluded from calculations.

If 'neither', the first and last points in the window are excluded from calculations.

Default None ('right').

step : int, default None

Added in version 1.5.0.

Evaluate the window at every step result, equivalent to slicing as [::step]. window must be an integer. Using a step argument other than None or 1 will produce a result with a different shape than the input.

method : str {‘single’, ‘table’}, default ‘single’

Added in version 1.3.0.

Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Returns

pandas.api.typing.Window or pandas.api.typing.Rolling: An instance of Window is returned if win_type is passed. Otherwise, an instance of Rolling is returned.

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

window

Rolling sum with a window length of 2 observations.

>>> df.rolling(2).sum()
     B
NaN
1.0
3.0
NaN
NaN

Rolling sum with a window span of 2 seconds.

>>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
...                        index=[pd.Timestamp('20130101 09:00:00'),
...                               pd.Timestamp('20130101 09:00:02'),
...                               pd.Timestamp('20130101 09:00:03'),
...                               pd.Timestamp('20130101 09:00:05'),
...                               pd.Timestamp('20130101 09:00:06')])

>>> df_time
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

>>> df_time.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Rolling sum with forward looking windows with 2 observations.

>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

min_periods

Rolling sum with a window length of 2 observations, but only needs a minimum of 1 observation to calculate a value.

>>> df.rolling(2, min_periods=1).sum()
     B
0.0
1.0
3.0
2.0
4.0

center

Rolling sum with the result assigned to the center of the window index.

>>> df.rolling(3, min_periods=1, center=True).sum()
     B
1.0
3.0
3.0
6.0
4.0

>>> df.rolling(3, min_periods=1, center=False).sum()
     B
0.0
1.0
3.0
3.0
6.0

step

Rolling sum with a window length of 2 observations, minimum of 1 observation to calculate a value, and a step of 2.

>>> df.rolling(2, min_periods=1, step=2).sum()
     B
0  0.0
2  3.0
4  4.0

win_type

Rolling sum with a window length of 2, using the Scipy 'gaussian' window type. std is required in the aggregation function.

>>> df.rolling(2, win_type='gaussian').sum(std=3)
          B
     NaN
0.986207
2.958621
     NaN
     NaN

on

Rolling sum with a window length of 2 days.

>>> df = pd.DataFrame({
...     'A': [pd.to_datetime('2020-01-01'),
...           pd.to_datetime('2020-01-01'),
...           pd.to_datetime('2020-01-02'),],
...     'B': [1, 2, 3], },
...     index=pd.date_range('2020', periods=3))

>>> df
                    A  B
2020-01-01 2020-01-01  1
2020-01-02 2020-01-01  2
2020-01-03 2020-01-02  3

>>> df.rolling('2D', on='A').sum()
                    A    B
2020-01-01 2020-01-01  1.0
2020-01-02 2020-01-01  3.0
2020-01-03 2020-01-02  6.0

sparkle.PerformanceDataFrame.round(self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs) → DataFrame

Round a DataFrame to a variable number of decimal places.

Parameters

decimalsint, dict, Series: Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.
*args: Additional keywords have no effect but might be accepted for compatibility with numpy.
**kwargs: Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns

DataFrame: A DataFrame with the affected columns rounded to the specified number of decimal places.

Examples

>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
...                   columns=['dogs', 'cats'])
>>> df
    dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18

By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({'dogs': 1, 'cats': 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

sparkle.PerformanceDataFrame.rpow(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.rsub(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator rsub).

Equivalent to other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, sub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.rtruediv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters

nint, optional: Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.
fracfloat, optional: Fraction of axis items to return. Cannot be used with n.
replacebool, default False: Allow or disallow sampling of the same row more than once.
weightsstr or ndarray-like, optional: Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.
random_stateint, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional: If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given.

Changed in version 1.4.0: np.random.Generator objects now accepted
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Axis to sample. Accepts axis number or name. Default is stat axis for given data type. For Series this parameter is unused and defaults to None.
ignore_indexbool, default False: If True, the resulting index will be labeled 0, 1, …, n - 1.

Added in version 1.3.0.

Returns

Series or DataFrame: A new object of same type as caller containing n items randomly sampled from the caller object.

Notes

If frac > 1, replacement should be set to True.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
...                    'num_wings': [2, 0, 0, 0],
...                    'num_specimen_seen': [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df['num_legs'].sample(n=3, random_state=1)
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1)
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8

sparkle.PerformanceDataFrame.save_csv(self: PerformanceDataFrame, csv_filepath: Path = None) → None

Write a CSV to the given path.

Args:: csv_filepath: String path to the csv file. Defaults to self.csv_filepath.

sparkle.PerformanceDataFrame.schedule_performance(self: ~sparkle.structures.performance_dataframe.PerformanceDataFrame, schedule: dict[slice(<class 'str'>, dict[slice(<class 'str'>, (<class 'str'>, <class 'str'>, <class 'int'>), None)], None)], target_solver: str | tuple[str, str] = None, objective: str | ~sparkle.types.objective.SparkleObjective = None) → float

Return the performance of a selection schedule on the portfolio.

Args:

schedule: Compute the best performance according to a selection schedule.: A schedule is a dictionary of instances, with a schedule per instance, consisting of a triple of solver, config_id and maximum runtime.

target_solver: If not None, store the found values in this solver of the DF. objective: The objective for which we calculate the best performance

Returns:

The performance of the schedule over the instances in the dictionary.

sparkle.PerformanceDataFrame.select_dtypes(self, include=None, exclude=None) → Self

Return a subset of the DataFrame’s columns based on the column dtypes.

Parameters

include, excludescalar or list-like: A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

Returns

DataFrame: The subset of the frame including the dtypes in include and excluding the dtypes in exclude.

Raises

ValueError

If both of include and exclude are empty
If include and exclude have overlapping elements
If any kind of string dtype is passed in.

Notes

To select all numeric types, use np.number or 'number'
To select strings you must use the object dtype, but note that this will return all object dtype columns. With pd.options.future.infer_string enabled, using "str" will work to select all string columns.
See the numpy dtype hierarchy
To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'
To select Pandas categorical dtypes, use 'category'
To select Pandas datetimetz dtypes, use 'datetimetz' or 'datetime64[ns, tz]'

Examples

>>> df = pd.DataFrame({'a': [1, 2] * 3,
...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0

>>> df.select_dtypes(include='bool')
   b
True
False
True
False
True
False

>>> df.select_dtypes(include=['float64'])
   c
1.0
2.0
1.0
2.0
1.0
2.0

>>> df.select_dtypes(exclude=['int64'])
       b    c
 True  1.0
False  2.0
 True  1.0
False  2.0
 True  1.0
False  2.0

sparkle.PerformanceDataFrame.sem(self, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters

axis{index (0), columns (1)}: For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.sem with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.

Returns

Series or DataFrame (if level specified)

>>> s = pd.Series([1, 2, 3])
>>> s.sem().round(6)
0.57735

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.sem()
a   0.5
b   0.5
dtype: float64

Using axis=1

>>> df.sem(axis=1)
tiger   0.5
zebra   0.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
...                   index=['tiger', 'zebra'])
>>> df.sem(numeric_only=True)
a   0.5
dtype: float64

sparkle.PerformanceDataFrame.set_axis(self, labels, *, axis: Axis = 0, copy: bool | None = None) → DataFrame

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or Index.

Parameters

labelslist-like, Index: The values for the new index.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to update. The value 0 identifies the rows. For Series this parameter is unused and defaults to 0.
copybool, default True: Whether to make a copy of the underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: An object of type DataFrame.

Parameters

copybool, default False: Specify if a copy of the object should be made.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True
allows_duplicate_labelsbool, optional: Whether the returned object allows duplicate labels.

Returns

Series or DataFrame: The same type as the caller.

Notes

This method returns a new object that’s a view on the same data as the input. Mutating the input or the output values will be reflected in the other.

This method is intended to be used in method chains.

“Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in DataFrame.attrs.

Examples

>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False

sparkle.PerformanceDataFrame.set_index(self, keys, *, drop: bool = True, append: bool = False, inplace: bool = False, verify_integrity: bool = False) → DataFrame | None

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.

Parameters

keyslabel or array-like or list of labels/arrays: This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, “array” encompasses Series, Index, np.ndarray, and instances of Iterator.
dropbool, default True: Delete columns to be used as the new index.
appendbool, default False: Whether to append columns to existing index.
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one.
verify_integritybool, default False: Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method.

Returns

DataFrame or None: Changed row labels or None if inplace=True.

Examples

>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
...                    'year': [2012, 2014, 2013, 2014],
...                    'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31

Set the index to become the ‘month’ column:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31

Create a MultiIndex using columns ‘year’ and ‘month’:

>>> df.set_index(['year', 'month'])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a MultiIndex using an Index and a column:

>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
      month  year  sale
1 1       1  2012    55
2 4       4  2014    40
3 9       7  2013    84
4 16     10  2014    31

Setter method to assign a value to the Dataframe.

Allows for setting the same value to multiple indices.

Args:

value: Value(s) to be assigned. If value is a list, first dimension is: the solver field, second dimension is if multiple different values are to be assigned. Must be the same shape as target.
solver: The solver(s) for which the value should be set.: If solver is a list, multiple solvers are set. If None, all solvers are set.
instance: The instance(s) for which the value should be set.: If instance is a list, multiple instances are set. If None, all instances are set.
configuration: The configuration(s) for which the value should be set.: When left None, set for all configurations
objective: The objectives for which the value should be set.: When left None, set for all objectives
run: The run index for which the value should be set.: If left None, set for all runs.
solver_fields: The level to which each value should be assinged.: Defaults to [“Value”].
append_write_csv: For concurrent writing to the PerformanceDataFrame.: If True, the value is directly appended to the CSV file. This will create duplicate entries in the file, but these are combined when loading the file.

sparkle.PerformanceDataFrame.shift(self, periods: int | Sequence[int] = 1, freq: Frequency | None = None, axis: Axis = 0, fill_value: Hashable = <no_default>, suffix: str | None = None) → DataFrame

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as “infer” as long as either freq or inferred_freq attribute is set in the index.

Parameters

periodsint or Sequence: Number of periods to shift. Can be positive or negative. If an iterable of ints, the data will be shifted once by each int. This is equivalent to shifting by one value at a time and concatenating all resulting frames. The resulting columns will have the shift suffixed to their column names. For multiple periods, axis must not be 1.
freqDateOffset, tseries.offsets, timedelta, or str, optional: Offset to use from the tseries module or time rule (e.g. ‘EOM’). If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as “infer” then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
axis{0 or ‘index’, 1 or ‘columns’, None}, default None: Shift direction. For Series this parameter is unused and defaults to 0.
fill_valueobject, optional: The scalar value to use for newly introduced missing values. the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. NaT is used. For extension dtypes, self.dtype.na_value is used.
suffixstr, optional: If str and periods is an iterable, this is added after the column name and before the shift value for each shifted column name.

Returns

DataFrame: Copy of input object, shifted.

Examples

>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],
...                    "Col2": [13, 23, 18, 33, 48],
...                    "Col3": [17, 27, 22, 37, 52]},
...                   index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52

>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0

>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01   NaN    10    13
2020-01-02   NaN    20    23
2020-01-03   NaN    15    18
2020-01-04   NaN    30    33
2020-01-05   NaN    45    48

>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27

>>> df.shift(periods=3, freq="D")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52

>>> df.shift(periods=3, freq="infer")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52

>>> df['Col1'].shift(periods=[0, 1, 2])
            Col1_0  Col1_1  Col1_2
2020-01-01      10     NaN     NaN
2020-01-02      20    10.0     NaN
2020-01-03      15    20.0    10.0
2020-01-04      30    15.0    20.0
2020-01-05      45    30.0    15.0

sparkle.PerformanceDataFrame.skew(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or scalar

>>> s = pd.Series([1, 2, 3])
>>> s.skew()
0.0

With a DataFrame

>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
...                   index=['tiger', 'zebra', 'cow'])
>>> df
        a   b   c
tiger   1   2   1
zebra   2   3   3
cow     3   4   5
>>> df.skew()
a   0.0
b   0.0
c   0.0
dtype: float64

Using axis=1

>>> df.skew(axis=1)
tiger   1.732051
zebra  -1.732051
cow     0.000000
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
...                   index=['tiger', 'zebra', 'cow'])
>>> df.skew(numeric_only=True)
a   0.0
dtype: float64

sparkle.PerformanceDataFrame.sort_index(self, *, axis: Axis = 0, level: IndexLabel | None = None, ascending: bool | Sequence[bool] = True, inplace: bool = False, kind: SortKind = 'quicksort', na_position: NaPosition = 'last', sort_remaining: bool = True, ignore_index: bool = False, key: IndexKeyFunc | None = None) → DataFrame | None

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.

Parameters

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns.
levelint or level name or list of ints or list of level names: If not None, sort on values in specified index level(s).
ascendingbool or list-like of bools, default True: Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one.
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’: Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
na_position{‘first’, ‘last’}, default ‘last’: Puts NaNs at the beginning if first; last puts NaNs at the end. Not implemented for MultiIndex.
sort_remainingbool, default True: If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.
ignore_indexbool, default False: If True, the resulting axis will be labeled 0, 1, …, n - 1.
keycallable, optional: If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.

Returns

DataFrame or None: The original DataFrame sorted by the labels or None if inplace=True.

Examples

>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
...                   columns=['A'])
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

>>> df.sort_index(ascending=False)
     A
3
5
1
 2
  4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4

sparkle.PerformanceDataFrame.sort_values(self, by: IndexLabel, *, axis: Axis = 0, ascending: bool | list[bool] | tuple[bool, ...] = True, inplace: bool = False, kind: SortKind = 'quicksort', na_position: str = 'last', ignore_index: bool = False, key: ValueKeyFunc | None = None) → DataFrame | None

Sort by the values along either axis.

Parameters

bystr or list of str

Name or list of names to sort by.

if axis is 0 or ‘index’ then by may contain index levels and/or column labels.
if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.

axis“{0 or ‘index’, 1 or ‘columns’}”, default 0

Axis to be sorted.

ascendingbool or list of bool, default True

Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

inplacebool, default False

If True, perform operation in-place.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’

Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.

na_position{‘first’, ‘last’}, default ‘last’

Puts NaNs at the beginning if first; last puts NaNs at the end.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

keycallable, optional

Apply the key function to the values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently.

Returns

DataFrame or None: DataFrame with sorted values or None if inplace=True.

Examples

>>> df = pd.DataFrame({
...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Sort by col1

>>> df.sort_values(by=['col1'])
  col1  col2  col3 col4
  A     2     0    a
  A     1     1    B
  B     9     9    c
  C     4     3    F
  D     7     2    e
NaN     8     4    D

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
  col1  col2  col3 col4
  A     1     1    B
  A     2     0    a
  B     9     9    c
  C     4     3    F
  D     7     2    e
NaN     8     4    D

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
  col1  col2  col3 col4
  D     7     2    e
  C     4     3    F
  B     9     9    c
  A     2     0    a
  A     1     1    B
NaN     8     4    D

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
  col1  col2  col3 col4
NaN     8     4    D
  D     7     2    e
  C     4     3    F
  B     9     9    c
  A     2     0    a
  A     1     1    B

Sorting with a key function

>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
   col1  col2  col3 col4
  A     2     0    a
  A     1     1    B
  B     9     9    c
NaN     8     4    D
  D     7     2    e
  C     4     3    F

Natural sort with the key argument, using the natsort <https://github.com/SethMMorton/natsort> package.

>>> df = pd.DataFrame({
...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
...    "value": [10, 20, 30, 40, 50]
... })
>>> df
    time  value
0    0hr     10
1  128hr     20
2   72hr     30
3   48hr     40
4   96hr     50
>>> from natsort import index_natsorted
>>> df.sort_values(
...     by="time",
...     key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
    time  value
0    0hr     10
3   48hr     40
2   72hr     30
4   96hr     50
1  128hr     20

sparkle.PerformanceDataFrame.squeeze(self, axis: int | Literal['index', 'columns', 'rows'] | None = None)

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don’t know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters

axis{0 or ‘index’, 1 or ‘columns’, None}, default None: A specific axis to squeeze. By default, all length-1 axes are squeezed. For Series this parameter is unused and defaults to None.

Returns

DataFrame, Series, or scalar: The projection after squeezing axis or all the axes.

Examples

>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0    2
dtype: int64

>>> even_primes.squeeze()
2

Squeezing objects with more than one value in every axis does nothing:

>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1    3
2    5
3    7
dtype: int64

>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

>>> df_a = df[['a']]
>>> df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

>>> df_a.squeeze('columns')
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

>>> df_0a.squeeze('rows')
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_0a.squeeze()
1

sparkle.PerformanceDataFrame.stack(self, level: IndexLabel = -1, dropna: bool | lib.NoDefault = <no_default>, sort: bool | lib.NoDefault = <no_default>, future_stack: bool = False)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

if the columns have a single level, the output is a Series;

if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Parameters

levelint, str, list, default -1: Level(s) to stack from the column axis onto the index axis, defined as one index or label, or a list of indices or labels.
dropnabool, default True: Whether to drop rows in the resulting Frame/Series with missing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.
sortbool, default True: Whether to sort the levels of the resulting MultiIndex.
future_stackbool, default False: Whether to use the new implementation that will replace the current implementation in pandas 3.0. When True, dropna and sort have no impact on the result and must remain unspecified. See pandas 2.1.0 Release notes for more details.

Returns

DataFrame or Series: Stacked dataframe or series.

Notes

The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).

Reference the user guide for more examples.

Examples

Single level columns

>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
...                                     index=['cat', 'dog'],
...                                     columns=['weight', 'height'])

Stacking a dataframe with a single level column axis returns a Series:

>>> df_single_level_cols
     weight height
cat       0      1
dog       2      3
>>> df_single_level_cols.stack(future_stack=True)
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64

Multi level columns: simple case

>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol1)

Stacking a dataframe with a multi-level column axis:

>>> df_multi_level_cols1
     weight
         kg    pounds
cat       1        2
dog       2        4
>>> df_multi_level_cols1.stack(future_stack=True)
            weight
cat kg           1
    pounds       2
dog kg           2
    pounds       4

Missing values

>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:

>>> df_multi_level_cols2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0
>>> df_multi_level_cols2.stack(future_stack=True)
        weight  height
cat kg     1.0     NaN
    m      NaN     2.0
dog kg     3.0     NaN
    m      NaN     4.0

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0, future_stack=True)
             kg    m
cat weight  1.0  NaN
    height  NaN  2.0
dog weight  3.0  NaN
    height  NaN  4.0
>>> df_multi_level_cols2.stack([0, 1], future_stack=True)
cat  weight  kg    1.0
     height  m     2.0
dog  weight  kg    3.0
     height  m     4.0
dtype: float64

sparkle.PerformanceDataFrame.std(self, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters

axis{index (0), columns (1)}: For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.

Returns

Series or DataFrame (if level specified)

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

Examples

>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
...                    'age': [21, 25, 62, 43],
...                    'height': [1.61, 1.87, 1.49, 2.01]}
...                   ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

The standard deviation of the columns can be found as follows:

>>> df.std()
age       18.786076
height     0.237417
dtype: float64

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.std(ddof=0)
age       16.269219
height     0.205609
dtype: float64

sparkle.PerformanceDataFrame.sub(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.subtract(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.sum(self, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs)

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns

Series or scalar

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.sum()
14

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
0.0

This can be controlled with the min_count parameter. For example, if you’d like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([], dtype="float64").sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()
0.0

>>> pd.Series([np.nan]).sum(min_count=1)
nan

sparkle.PerformanceDataFrame.swapaxes(self, axis1: int | Literal['index', 'columns', 'rows'], axis2: int | Literal['index', 'columns', 'rows'], copy: bool | None = None) → None: Interchange axes and swap values axes appropriately.

Deprecated since version 2.1.0: swapaxes is deprecated and will be removed. Please use transpose instead.

Returns

same as input

Examples

Please see examples for DataFrame.transpose().

sparkle.PerformanceDataFrame.swaplevel(self, i: Axis = -2, j: Axis = -1, axis: Axis = 0) → DataFrame

Swap levels i and j in a MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters

i, jint or str: Levels of the indices to be swapped. Can pass level name as string.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to swap levels on. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

Returns

DataFrame: DataFrame with levels swapped in MultiIndex.

Examples

>>> df = pd.DataFrame(
...     {"Grade": ["A", "B", "A", "C"]},
...     index=[
...         ["Final exam", "Final exam", "Coursework", "Coursework"],
...         ["History", "Geography", "History", "Geography"],
...         ["January", "February", "March", "April"],
...     ],
... )
>>> df
                                    Grade
Final exam  History     January      A
            Geography   February     B
Coursework  History     March        A
            Geography   April        C

In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.

>>> df.swaplevel()
                                    Grade
Final exam  January     History         A
            February    Geography       B
Coursework  March       History         A
            April       Geography       C

By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.

>>> df.swaplevel(0)
                                    Grade
January     History     Final exam      A
February    Geography   Final exam      B
March       History     Coursework      A
April       Geography   Coursework      C

We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.

>>> df.swaplevel(0, 1)
                                    Grade
History     Final exam  January         A
Geography   Final exam  February        B
History     Coursework  March           A
Geography   Coursework  April           C

sparkle.PerformanceDataFrame.tail(self, n: int = 5) → None

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first |n| rows, equivalent to df[|n|:].

If n is larger than the number of rows, this function returns all rows.

Parameters

nint, default 5: Number of rows to select.

Returns

type of caller: The last n rows of the caller object.

Examples

>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the last 5 lines

>>> df.tail()
   animal
monkey
parrot
 shark
 whale
 zebra

Viewing the last n lines (three in this case)

>>> df.tail(3)
  animal
6  shark
7  whale
8  zebra

For negative values of n

>>> df.tail(-3)
   animal
  lion
monkey
parrot
 shark
 whale
 zebra

sparkle.PerformanceDataFrame.take(self, indices, axis: int | Literal['index', 'columns', 'rows'] = 0, **kwargs) → None

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

Parameters

indicesarray-like: An array of ints indicating which positions to take.
axis{0 or ‘index’, 1 or ‘columns’, None}, default 0: The axis on which to select elements. 0 means that we are selecting rows, 1 means that we are selecting columns. For Series this parameter is unused and defaults to 0.
**kwargs: For compatibility with numpy.take(). Has no effect on the output.

Returns

same type as caller: An array-like containing the elements taken from the object.

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=['name', 'class', 'max_speed'],
...                   index=[0, 2, 3, 1])
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That’s because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
     name   class  max_speed
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5

sparkle.PerformanceDataFrame.to_clipboard(self, *, excel: bool = True, sep: str | None = None, **kwargs) → None

Copy object to the system clipboard.

Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.

Parameters

excelbool, default True

Produce output in a csv format for easy pasting into excel.

True, use the provided separator for csv pasting.
False, write a string representation of the object to the clipboard.

sepstr, default '\t'

Field delimiter.

**kwargs

These parameters will be passed to DataFrame.to_csv.

Notes

Requirements for your platform.

Linux : xclip, or xsel (with PyQt4 modules)

Windows : none

macOS : none

This method uses the processes developed for the package pyperclip. A solution to render any output string format is given in the examples.

Examples

Copy the contents of a DataFrame to the clipboard.

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])

>>> df.to_clipboard(sep=',')  
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6

We can omit the index by passing the keyword index and setting it to false.

>>> df.to_clipboard(sep=',', index=False)  
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6

Using the original pyperclip package for any string output format.

import pyperclip
html = df.style.to_html()
pyperclip.copy(html)

sparkle.PerformanceDataFrame.to_csv(self, path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None, *, sep: str = ',', na_rep: str = '', float_format: str | Callable | None = None, columns: Sequence[Hashable] | None = None, header: bool_t | list[str] = True, index: bool_t = True, index_label: IndexLabel | None = None, mode: str = 'w', encoding: str | None = None, compression: CompressionOptions = 'infer', quoting: int | None = None, quotechar: str = '"', lineterminator: str | None = None, chunksize: int | None = None, date_format: str | None = None, doublequote: bool_t = True, escapechar: str | None = None, decimal: str = '.', errors: OpenFileErrors = 'strict', storage_options: StorageOptions | None = None) → str | None

Write object to a comma-separated values (csv) file.

Parameters

path_or_bufstr, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with newline=’’, disabling universal newlines. If a binary file object is passed, mode might need to contain a ‘b’.

sepstr, default ‘,’

String of length 1. Field delimiter for the output file.

na_repstr, default ‘’

Missing data representation.

float_formatstr, Callable, default None

Format string for floating point numbers. If a Callable is given, it takes precedence over other numeric formatting parameters, like decimal.

columnssequence, optional

Columns to write.

headerbool or list of str, default True

Write out the column names. If a list of strings is given it is assumed to be aliases for the column names.

indexbool, default True

Write row names (index).

index_labelstr or sequence, or False, default None

Column label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.

mode{‘w’, ‘x’, ‘a’}, default ‘w’

Forwarded to either open(mode=) or fsspec.open(mode=) to control the file opening. Typical values include:

‘w’, truncate the file first.
‘x’, exclusive creation, failing if the file already exists.
‘a’, append to the end of file if it exists.

encodingstr, optional

A string representing the encoding to use in the output file, defaults to ‘utf-8’. encoding is not supported if path_or_buf is a non-binary file object.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

May be a dict with key ‘method’ as compression mode and other entries as additional compression options if compression mode is ‘zip’.

Passing compression options as keys in dict is supported for compression modes ‘gzip’, ‘bz2’, ‘zstd’, and ‘zip’.

quotingoptional constant from csv module

Defaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.

quotecharstr, default ‘"’

String of length 1. Character used to quote fields.

lineterminatorstr, optional

The newline character or character sequence to use in the output file. Defaults to os.linesep, which depends on the OS in which this method is called (’\n’ for linux, ‘\r\n’ for Windows, i.e.).

Changed in version 1.5.0: Previously was line_terminator, changed for consistency with read_csv and the standard library ‘csv’ module.

chunksizeint or None

Rows to write at a time.

date_formatstr, default None

Format string for datetime objects.

doublequotebool, default True

Control quoting of quotechar inside a field.

escapecharstr, default None

String of length 1. Character used to escape sep and quotechar when appropriate.

decimalstr, default ‘.’

Character recognized as decimal separator. E.g. use ‘,’ for European data.

errorsstr, default ‘strict’

Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Returns

None or str: If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.

Examples

Create ‘out.csv’ containing ‘df’ without indices

>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv('out.csv', index=False)  

Create ‘out.zip’ containing ‘out.csv’

>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  

To write a csv file to a new folder or nested folder you will first need to create it using either Pathlib or os:

>>> from pathlib import Path  
>>> filepath = Path('folder/subfolder/out.csv')  
>>> filepath.parent.mkdir(parents=True, exist_ok=True)  
>>> df.to_csv(filepath)  

>>> import os  
>>> os.makedirs('folder/subfolder', exist_ok=True)  
>>> df.to_csv('folder/subfolder/out.csv')  

sparkle.PerformanceDataFrame.to_dict(self, orient: Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index'] = 'dict', *, into: type[MutableMappingT] | MutableMappingT = <class 'dict'>, index: bool = True) → MutableMappingT | list[MutableMappingT]

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Parameters

orientstr {‘dict’, ‘list’, ‘series’, ‘split’, ‘tight’, ‘records’, ‘index’}

Determines the type of the values of the dictionary.

‘dict’ (default) : dict like {column -> {index -> value}}
‘list’ : dict like {column -> [values]}
‘series’ : dict like {column -> Series(values)}
‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
‘tight’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values], ‘index_names’ -> [index.names], ‘column_names’ -> [column.names]}
‘records’ : list like [{column -> value}, … , {column -> value}]
‘index’ : dict like {index -> {column -> value}}

Added in version 1.4.0: ‘tight’ as an allowed value for the orient argument

intoclass, default dict

The collections.abc.MutableMapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.

indexbool, default True

Whether to include the index item (and index_names item if orient is ‘tight’) in the returned dictionary. Can only be False when orient is ‘split’ or ‘tight’.

Added in version 2.0.0.

Returns

dict, list or collections.abc.MutableMapping: Return a collections.abc.MutableMapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

Examples

>>> df = pd.DataFrame({'col1': [1, 2],
...                    'col2': [0.5, 0.75]},
...                   index=['row1', 'row2'])
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}

>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}

>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]

>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

>>> df.to_dict('tight')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]

sparkle.PerformanceDataFrame.to_excel(self, excel_writer: FilePath | WriteExcelBuffer | ExcelWriter, *, sheet_name: str = 'Sheet1', na_rep: str = '', float_format: str | None = None, columns: Sequence[Hashable] | None = None, header: Sequence[Hashable] | bool_t = True, index: bool_t = True, index_label: IndexLabel | None = None, startrow: int = 0, startcol: int = 0, engine: Literal['openpyxl', 'xlsxwriter'] | None = None, merge_cells: bool_t = True, inf_rep: str = 'inf', freeze_panes: tuple[int, int] | None = None, storage_options: StorageOptions | None = None, engine_kwargs: dict[str, Any] | None = None) → None

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters

excel_writerpath-like, file-like, or ExcelWriter object: File path or existing ExcelWriter.
sheet_namestr, default ‘Sheet1’: Name of sheet which will contain DataFrame.
na_repstr, default ‘’: Missing data representation.
float_formatstr, optional: Format string for floating point numbers. For example float_format="%.2f" will format 0.1234 to 0.12.
columnssequence or list of str, optional: Columns to write.
headerbool or list of str, default True: Write out the column names. If a list of string is given it is assumed to be aliases for the column names.
indexbool, default True: Write row names (index).
index_labelstr or sequence, optional: Column label for index column(s) if desired. If not specified, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
startrowint, default 0: Upper left cell row to dump data frame.
startcolint, default 0: Upper left cell column to dump data frame.
enginestr, optional: Write engine to use, ‘openpyxl’ or ‘xlsxwriter’. You can also set this via the options io.excel.xlsx.writer or io.excel.xlsm.writer.
merge_cellsbool, default True: Write MultiIndex and Hierarchical Rows as merged cells.
inf_repstr, default ‘inf’: Representation for infinity (there is no native representation for infinity in Excel).
freeze_panestuple of int (length 2), optional: Specifies the one-based bottommost row and rightmost column that is to be frozen.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Added in version 1.2.0.
engine_kwargsdict, optional: Arbitrary keyword arguments passed to excel engine.

Notes

For compatibility with to_csv(), to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.

Examples

Create, write to and save a workbook:

>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
...                    index=['row 1', 'row 2'],
...                    columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx")  

To specify the sheet name:

>>> df1.to_excel("output.xlsx",
...              sheet_name='Sheet_name_1')  

If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object:

>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer:  
...     df1.to_excel(writer, sheet_name='Sheet_name_1')
...     df2.to_excel(writer, sheet_name='Sheet_name_2')

ExcelWriter can also be used to append to an existing Excel file:

>>> with pd.ExcelWriter('output.xlsx',
...                     mode='a') as writer:  
...     df1.to_excel(writer, sheet_name='Sheet_name_3')

To set the library that is used to write the Excel file, you can pass the engine keyword (the default engine is automatically chosen depending on the file extension):

>>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  

sparkle.PerformanceDataFrame.to_feather(self, path: FilePath | WriteBuffer[bytes], **kwargs) → None

Write a DataFrame to the binary Feather format.

Parameters

pathstr, path object, file-like object: String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. If a string or a path, it will be used as Root Directory path when writing a partitioned dataset.
**kwargs :: Additional keywords passed to pyarrow.feather.write_feather(). This includes the compression, compression_level, chunksize and version keywords.

Notes

This function writes the dataframe as a feather file. Requires a default index. For saving the DataFrame with your custom index use a method that supports custom indices e.g. to_parquet.

Examples

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]])
>>> df.to_feather("file.feather")  

sparkle.PerformanceDataFrame.to_gbq(self, destination_table: str, *, project_id: str | None = None, chunksize: int | None = None, reauth: bool = False, if_exists: ToGbqIfexist = 'fail', auth_local_webserver: bool = True, table_schema: list[dict[str, str]] | None = None, location: str | None = None, progress_bar: bool = True, credentials=None) → None

Write a DataFrame to a Google BigQuery table.

Deprecated since version 2.2.0: Please use pandas_gbq.to_gbq instead.

This function requires the pandas-gbq package.

See the How to authenticate with Google BigQuery guide for authentication instructions.

Parameters

destination_tablestr

Name of table to be written, in the form dataset.tablename.

project_idstr, optional

Google BigQuery Account project ID. Optional when available from the environment.

chunksizeint, optional

Number of rows to be inserted in each chunk from the dataframe. Set to None to load the whole dataframe at once.

reauthbool, default False

Force Google BigQuery to re-authenticate the user. This is useful if multiple accounts are used.

if_existsstr, default ‘fail’

Behavior when the destination table exists. Value can be one of:

'fail': If table exists raise pandas_gbq.gbq.TableCreationError.
'replace': If table exists, drop it, recreate it, and insert data.
'append': If table exists, insert data. Create if does not exist.

auth_local_webserverbool, default True

Use the `local webserver flow`_ instead of the `console flow`_ when getting user credentials.

New in version 0.2.0 of pandas-gbq.

Changed in version 1.5.0: Default value is changed to True. Google has deprecated the auth_local_webserver = False “out of band” (copy-paste) flow.

table_schemalist of dicts, optional

List of BigQuery table fields to which according DataFrame columns conform to, e.g. [{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field.

New in version 0.3.1 of pandas-gbq.

locationstr, optional

Location where the load job should run. See the BigQuery locations documentation for a list of available locations. The location must match that of the target dataset.

New in version 0.5.0 of pandas-gbq.

progress_barbool, default True

Use the library tqdm to show the progress bar for the upload, chunk by chunk.

New in version 0.5.0 of pandas-gbq.

credentialsgoogle.auth.credentials.Credentials, optional

Credentials for accessing Google APIs. Use this parameter to override default credentials, such as to use Compute Engine google.auth.compute_engine.Credentials or Service Account google.oauth2.service_account.Credentials directly.

New in version 0.8.0 of pandas-gbq.

Examples

Example taken from Google BigQuery documentation

>>> project_id = "my-project"
>>> table_id = 'my_dataset.my_table'
>>> df = pd.DataFrame({
...                   "my_string": ["a", "b", "c"],
...                   "my_int64": [1, 2, 3],
...                   "my_float64": [4.0, 5.0, 6.0],
...                   "my_bool1": [True, False, True],
...                   "my_bool2": [False, True, False],
...                   "my_dates": pd.date_range("now", periods=3),
...                   }
...                   )

>>> df.to_gbq(table_id, project_id=project_id)  

sparkle.PerformanceDataFrame.to_hdf(self, path_or_buf: FilePath | HDFStore, *, key: str, mode: Literal['a', 'w', 'r+'] = 'a', complevel: int | None = None, complib: Literal['zlib', 'lzo', 'bzip2', 'blosc'] | None = None, append: bool_t = False, format: Literal['fixed', 'table'] | None = None, index: bool_t = True, min_itemsize: int | dict[str, int] | None = None, nan_rep=None, dropna: bool_t | None = None, data_columns: Literal[True] | list[str] | None = None, errors: OpenFileErrors = 'strict', encoding: str = 'UTF-8') → None

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

Warning

One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.

For more information see the user guide.

Parameters

path_or_bufstr or pandas.HDFStore

File path or HDFStore object.

keystr

Identifier for the group in the store.

mode{‘a’, ‘w’, ‘r+’}, default ‘a’

Mode to open file:

‘w’: write, a new file is created (an existing file with the same name would be deleted).
‘a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created.
‘r+’: similar to ‘a’, but the file must already exist.

complevel{0-9}, default None

Specifies a compression level for data. A value of 0 or None disables compression.

complib{‘zlib’, ‘lzo’, ‘bzip2’, ‘blosc’}, default ‘zlib’

Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError.

appendbool, default False

For Table formats, append the input data to the existing.

format{‘fixed’, ‘table’, None}, default ‘fixed’

Possible values:

‘fixed’: Fixed format. Fast writing/reading. Not-appendable, nor searchable.
‘table’: Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”.

indexbool, default True

Write DataFrame index as a column.

min_itemsizedict or int, optional

Map column names to minimum string sizes for columns.

nan_repAny, optional

How to represent null values as str. Not allowed with append=True.

dropnabool, default False, optional

Remove missing values.

data_columnslist of columns or True, optional

List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See Query via data columns. for more information. Applicable only to format=’table’.

errorsstr, default ‘strict’

Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.

encoding : str, default “UTF-8”

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
...                   index=['a', 'b', 'c'])  
>>> df.to_hdf('data.h5', key='df', mode='w')  

We can add another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])  
>>> s.to_hdf('data.h5', key='s')  

Reading from HDF file:

>>> pd.read_hdf('data.h5', 'df')  
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')  
0    1
1    2
2    3
3    4
dtype: int64

Render a DataFrame as an HTML table.

Parameters

bufstr, Path or StringIO-like, optional, default None

Buffer to write to. If None, the output is returned as a string.

columnsarray-like, optional, default None

The subset of columns to write. Writes all columns by default.

col_spacestr or int, list or dict of int or str, optional

The minimum width of each column in CSS length units. An int is assumed to be px units..

headerbool, optional

Whether to print column labels, default True.

indexbool, optional, default True

Whether to print index (row) labels.

na_repstr, optional, default ‘NaN’

String representation of NaN to use.

formatterslist, tuple or dict of one-param. functions, optional

Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

float_formatone-parameter function, optional, default None

Formatter function to apply to columns’ elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.

sparsifybool, optional, default True

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_namesbool, optional, default True

Prints the names of the indexes.

justifystr, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are

left
right
center
justify
justify-all
start
end
inherit
match-parent
initial
unset.

max_rowsint, optional

Maximum number of rows to display in the console.

max_colsint, optional

Maximum number of columns to display in the console.

show_dimensionsbool, default False

Display DataFrame dimensions (number of rows by number of columns).

decimalstr, default ‘.’

Character recognized as decimal separator, e.g. ‘,’ in Europe.

bold_rowsbool, default True

Make the row labels bold in the output.

classesstr or list or tuple, default None

CSS class(es) to apply to the resulting html table.

escapebool, default True

Convert the characters <, >, and & to HTML-safe sequences.

notebook{True, False}, default False

Whether the generated HTML is for IPython Notebook.

borderint

A border=border attribute is included in the opening <table> tag. Default pd.options.display.html.border.

table_idstr, optional

A css id is included in the opening <table> tag if specified.

render_linksbool, default False

Convert URLs to HTML links.

encodingstr, default “utf-8”

Set character encoding.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

Examples

>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
>>> html_string = '''<table border="1" class="dataframe">
...   <thead>
...     <tr style="text-align: right;">
...       <th></th>
...       <th>col1</th>
...       <th>col2</th>
...     </tr>
...   </thead>
...   <tbody>
...     <tr>
...       <th>0</th>
...       <td>1</td>
...       <td>4</td>
...     </tr>
...     <tr>
...       <th>1</th>
...       <td>2</td>
...       <td>3</td>
...     </tr>
...   </tbody>
... </table>'''
>>> assert html_string == df.to_html()

sparkle.PerformanceDataFrame.to_json(self, path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None, *, orient: Literal['split', 'records', 'index', 'table', 'columns', 'values'] | None = None, date_format: str | None = None, double_precision: int = 10, force_ascii: bool_t = True, date_unit: TimeUnit = 'ms', default_handler: Callable[[Any], JSONSerializable] | None = None, lines: bool_t = False, compression: CompressionOptions = 'infer', index: bool_t | None = None, indent: int | None = None, storage_options: StorageOptions | None = None, mode: Literal['a', 'w'] = 'w') → str | None

Convert the object to a JSON string.

Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters

path_or_bufstr, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.

orientstr

Indication of expected JSON string format.

Series:
- default is ‘index’
- allowed values are: {‘split’, ‘records’, ‘index’, ‘table’}.
DataFrame:
- default is ‘columns’
- allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.
The format of the JSON string:
- ‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
- ‘records’ : list like [{column -> value}, … , {column -> value}]
- ‘index’ : dict like {index -> {column -> value}}
- ‘columns’ : dict like {column -> {index -> value}}
- ‘values’ : just the values array
- ‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}}
Describing the data, where data component is like orient='records'.

date_format{None, ‘epoch’, ‘iso’}

Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.

double_precisionint, default 10

The number of decimal places to use when encoding floating point values. The possible maximal value is 15. Passing double_precision greater than 15 will raise a ValueError.

force_asciibool, default True

Force encoded string to be ASCII.

date_unitstr, default ‘ms’ (milliseconds)

The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.

default_handlercallable, default None

Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.

linesbool, default False

If ‘orient’ is ‘records’ write out line-delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list-like.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

indexbool or None, default None

The index is only used when ‘orient’ is ‘split’, ‘index’, ‘column’, or ‘table’. Of these, ‘index’ and ‘column’ do not support index=False.

indentint, optional

Length of whitespace used to indent each record.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

modestr, default ‘w’ (writing)

Specify the IO mode for output when supplying a path_or_buf. Accepted args are ‘w’ (writing) and ‘a’ (append) only. mode=’a’ is only supported when lines is True and orient is ‘records’.

Returns

None or str: If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

Notes

The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release.

orient='table' contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema.

Examples

>>> from json import loads, dumps
>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )

>>> result = df.to_json(orient="split")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> result = df.to_json(orient="records")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> result = df.to_json(orient="index")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "row 1": {
        "col 1": "a",
        "col 2": "b"
    },
    "row 2": {
        "col 1": "c",
        "col 2": "d"
    }
}

Encoding/decoding a Dataframe using 'columns' formatted JSON:

>>> result = df.to_json(orient="columns")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "col 1": {
        "row 1": "a",
        "row 2": "c"
    },
    "col 2": {
        "row 1": "b",
        "row 2": "d"
    }
}

Encoding/decoding a Dataframe using 'values' formatted JSON:

>>> result = df.to_json(orient="values")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
[
    [
        "a",
        "b"
    ],
    [
        "c",
        "d"
    ]
]

Encoding with Table Schema:

>>> result = df.to_json(orient="table")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)  
{
    "schema": {
        "fields": [
            {
                "name": "index",
                "type": "string"
            },
            {
                "name": "col 1",
                "type": "string"
            },
            {
                "name": "col 2",
                "type": "string"
            }
        ],
        "primaryKey": [
            "index"
        ],
        "pandas_version": "1.4.0"
    },
    "data": [
        {
            "index": "row 1",
            "col 1": "a",
            "col 2": "b"
        },
        {
            "index": "row 2",
            "col 1": "c",
            "col 2": "d"
        }
    ]
}

Render object to a LaTeX tabular, longtable, or nested table.

Requires \usepackage{{booktabs}}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{{table.tex}}.

Changed in version 2.0.0: Refactored to use the Styler implementation via jinja2 templating.

Parameters

bufstr, Path or StringIO-like, optional, default None: Buffer to write to. If None, the output is returned as a string.
columnslist of label, optional: The subset of columns to write. Writes all columns by default.
headerbool or list of str, default True: Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
indexbool, default True: Write row names (index).
na_repstr, default ‘NaN’: Missing data representation.
formatterslist of functions or dict of {{str: function}}, optional: Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns.
float_formatone-parameter function or str, optional, default None: Formatter for floating point numbers. For example float_format="%.2f" and float_format="{{:0.2f}}".format will both result in 0.1234 being formatted as 0.12.
sparsifybool, optional: Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module.
index_namesbool, default True: Prints the names of the indexes.
bold_rowsbool, default False: Make the row labels bold in the output.
column_formatstr, optional: The columns format as specified in LaTeX table format e.g. ‘rcl’ for 3 columns. By default, ‘l’ will be used for all columns except columns of numbers, which default to ‘r’.
longtablebool, optional: Use a longtable environment instead of tabular. Requires adding a usepackage{{longtable}} to your LaTeX preamble. By default, the value will be read from the pandas config module, and set to True if the option styler.latex.environment is “longtable”.

Changed in version 2.0.0: The pandas option affecting this argument has changed.
escapebool, optional: By default, the value will be read from the pandas config module and set to True if the option styler.format.escape is “latex”. When set to False prevents from escaping latex special characters in column names.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to False.
encodingstr, optional: A string representing the encoding to use in the output file, defaults to ‘utf-8’.
decimalstr, default ‘.’: Character recognized as decimal separator, e.g. ‘,’ in Europe.
multicolumnbool, default True: Use multicolumn to enhance MultiIndex columns. The default will be read from the config module, and is set as the option styler.sparse.columns.

Changed in version 2.0.0: The pandas option affecting this argument has changed.
multicolumn_formatstr, default ‘r’: The alignment for multicolumns, similar to column_format The default will be read from the config module, and is set as the option styler.latex.multicol_align.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to “r”.
multirowbool, default True: Use multirow to enhance MultiIndex rows. Requires adding a usepackage{{multirow}} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module, and is set as the option styler.sparse.index.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to True.
captionstr or tuple, optional: Tuple (full_caption, short_caption), which results in \caption[short_caption]{{full_caption}}; if a single string is passed, no short caption will be set.
labelstr, optional: The LaTeX label to be placed inside \label{{}} in the output. This is used with \ref{{}} in the main .tex file.
positionstr, optional: The LaTeX positional argument for tables, to be placed after \begin{{}} in the output.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

Notes

As of v2.0.0 this method has changed to use the Styler implementation as part of Styler.to_latex() via jinja2 templating. This means that jinja2 is a requirement, and needs to be installed, for this method to function. It is advised that users switch to using Styler, since that implementation is more frequently updated and contains much more flexibility with the output.

Examples

Convert a general DataFrame to LaTeX with formatting:

>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
...                        age=[26, 45],
...                        height=[181.23, 177.65]))
>>> print(df.to_latex(index=False,
...                   formatters={"name": str.upper},
...                   float_format="{:.1f}".format,
... ))  
\begin{tabular}{lrr}
\toprule
name & age & height \\
\midrule
RAPHAEL & 26 & 181.2 \\
DONATELLO & 45 & 177.7 \\
\bottomrule
\end{tabular}

sparkle.PerformanceDataFrame.to_markdown(self, buf: FilePath | WriteBuffer[str] | None = None, *, mode: str = 'wt', index: bool = True, storage_options: StorageOptions | None = None, **kwargs) → str | None

Print DataFrame in Markdown-friendly format.

Parameters

bufstr, Path or StringIO-like, optional, default None: Buffer to write to. If None, the output is returned as a string.
modestr, optional: Mode in which file is opened, “wt” by default.
indexbool, optional, default True: Add index (row) labels.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
**kwargs: These parameters will be passed to tabulate.

Returns

str: DataFrame in Markdown-friendly format.

Notes

Requires the tabulate package.

Examples

>>> df = pd.DataFrame(
...     data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
... )
>>> print(df.to_markdown())
|    | animal_1   | animal_2   |
|---:|:-----------|:-----------|
|  0 | elk        | dog        |
|  1 | pig        | quetzal    |

Output markdown with a tabulate option.

>>> print(df.to_markdown(tablefmt="grid"))
+----+------------+------------+
|    | animal_1   | animal_2   |
+====+============+============+
|  0 | elk        | dog        |
+----+------------+------------+
|  1 | pig        | quetzal    |
+----+------------+------------+

sparkle.PerformanceDataFrame.to_numpy(self, dtype: npt.DTypeLike | None = None, copy: bool = False, na_value: object = <no_default>) → np.ndarray

Convert the DataFrame to a NumPy array.

By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are float16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.

Parameters

dtypestr or numpy.dtype, optional: The dtype to pass to numpy.asarray().
copybool, default False: Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.
na_valueAny, optional: The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

Returns

numpy.ndarray

Examples

>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
       [2, 4]])

With heterogeneous data, the lowest common type will have to be used.

>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
       [2. , 4.5]])

For a mix of numeric and non-numeric types, the output array will have object dtype.

>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
       [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)

Write a DataFrame to the ORC format.

Added in version 1.5.0.

Parameters

pathstr, file-like object or None, default None: If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned.
engine{‘pyarrow’}, default ‘pyarrow’: ORC library to use.
indexbool, optional: If True, include the dataframe’s index(es) in the file output. If False, they will not be written to the file. If None, similar to infer the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.
engine_kwargsdict[str, Any] or None, default None: Additional keyword arguments passed to pyarrow.orc.write_table().

Returns

bytes if no path argument is provided else None

Raises

NotImplementedError: Dtype of one or more columns is category, unsigned integers, interval, period or sparse.
ValueError: engine is not pyarrow.

Notes

Before using this function you should read the user guide about ORC and install optional dependencies.
This function requires pyarrow library.
For supported dtypes please refer to supported ORC features in Arrow.
Currently timezones in datetime columns are not preserved when a dataframe is converted into ORC files.

Examples

>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
>>> df.to_orc('df.orc')  
>>> pd.read_orc('df.orc')  
   col1  col2
0     1     4
1     2     3

If you want to get a buffer to the orc content you can write it to io.BytesIO

>>> import io
>>> b = io.BytesIO(df.to_orc())  
>>> b.seek(0)  
0
>>> content = b.read()  

sparkle.PerformanceDataFrame.to_parquet(self, path: FilePath | WriteBuffer[bytes] | None = None, *, engine: Literal['auto', 'pyarrow', 'fastparquet'] = 'auto', compression: str | None = 'snappy', index: bool | None = None, partition_cols: list[str] | None = None, storage_options: StorageOptions | None = None, **kwargs) → bytes | None

Write a DataFrame to the binary parquet format.

This function writes the dataframe as a parquet file. You can choose different parquet backends, and have the option of compression. See the user guide for more details.

Parameters

pathstr, path object, file-like object, or None, default None: String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. If None, the result is returned as bytes. If a string or path, it will be used as Root Directory path when writing a partitioned dataset.
engine{‘auto’, ‘pyarrow’, ‘fastparquet’}, default ‘auto’: Parquet library to use. If ‘auto’, then the option io.parquet.engine is used. The default io.parquet.engine behavior is to try ‘pyarrow’, falling back to ‘fastparquet’ if ‘pyarrow’ is unavailable.
compressionstr or None, default ‘snappy’: Name of the compression to use. Use None for no compression. Supported options: ‘snappy’, ‘gzip’, ‘brotli’, ‘lz4’, ‘zstd’.
indexbool, default None: If True, include the dataframe’s index(es) in the file output. If False, they will not be written to the file. If None, similar to True the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.
partition_colslist, optional, default None: Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
**kwargs: Additional arguments passed to the parquet library. See pandas io for more details.

Returns

bytes if no path argument is provided else None

Notes

This function requires either the fastparquet or pyarrow library.

Examples

>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
...               compression='gzip')  
>>> pd.read_parquet('df.parquet.gzip')  
   col1  col2
0     1     3
1     2     4

If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don’t use partition_cols, which creates multiple files.

>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()

sparkle.PerformanceDataFrame.to_period(self, freq: Frequency | None = None, axis: Axis = 0, copy: bool | None = None) → DataFrame

Convert DataFrame from DatetimeIndex to PeriodIndex.

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).

Parameters

freqstr, default: Frequency of the PeriodIndex.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to convert (the index by default).
copybool, default True: If False then underlying input data is not copied.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: The DataFrame has a PeriodIndex.

Examples

>>> idx = pd.to_datetime(
...     [
...         "2001-03-31 00:00:00",
...         "2002-05-31 00:00:00",
...         "2003-08-31 00:00:00",
...     ]
... )

>>> idx
DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
dtype='datetime64[ns]', freq=None)

>>> idx.to_period("M")
PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')

For the yearly frequency

>>> idx.to_period("Y")
PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')

sparkle.PerformanceDataFrame.to_pickle(self, path: FilePath | WriteBuffer[bytes], *, compression: CompressionOptions = 'infer', protocol: int = 5, storage_options: StorageOptions | None = None) → None

Pickle (serialize) object to file.

Parameters

pathstr, path object, or file-like object: String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. File path where the pickled object will be stored.
compressionstr or dict, default ‘infer’: For on-the-fly compression of the output data. If ‘infer’ and ‘path’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.
protocolint: Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.

[1]
https://docs.python.org/3/library/pickle.html.
storage_optionsdict, optional: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Examples

>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})  
>>> original_df  
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> original_df.to_pickle("./dummy.pkl")  

>>> unpickled_df = pd.read_pickle("./dummy.pkl")  
>>> unpickled_df  
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9

sparkle.PerformanceDataFrame.to_records(self, index: bool = True, column_dtypes=None, index_dtypes=None) → recarray

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters

indexbool, default True

Include index in resulting record array, stored in ‘index’ field or using the index label, if set.

column_dtypesstr, type, dict, default None

If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types.

index_dtypesstr, type, dict, default None

If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.

This mapping is applied only if index=True.

Returns

numpy.rec.recarray: NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

Examples

>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
...                   index=['a', 'b'])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to ‘index’. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])

>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.

Parameters

namestr

Name of SQL table.

consqlalchemy.engine.(Engine or Connection) or sqlite3.Connection

Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable. See here. If passing a sqlalchemy.engine.Connection which is already in a transaction, the transaction will not be committed. If passing a sqlite3.Connection, it will not be possible to roll back the record insertion.

schemastr, optional

Specify the schema (if database flavor supports this). If None, use default schema.

if_exists{‘fail’, ‘replace’, ‘append’}, default ‘fail’

How to behave if the table already exists.

fail: Raise a ValueError.
replace: Drop the table before inserting new values.
append: Insert new values to the existing table.

indexbool, default True

Write DataFrame index as a column. Uses index_label as the column name in the table. Creates a table index for this column.

index_labelstr or sequence, default None

Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.

chunksizeint, optional

Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once.

dtypedict or scalar, optional

Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.

method{None, ‘multi’, callable}, optional

Controls the SQL insertion clause used:

None : Uses standard SQL INSERT clause (one per row).
‘multi’: Pass multiple values in a single INSERT clause.
callable with signature (pd_table, conn, keys, data_iter).

Details and a sample callable implementation can be found in the section insert method.

Returns

None or int

Number of rows affected by to_sql. None is returned if the callable passed into method does not return an integer number of rows.

The number of returned rows affected is the sum of the rowcount attribute of sqlite3.Cursor or SQLAlchemy connectable which may not reflect the exact number of written rows as stipulated in the sqlite3 or SQLAlchemy.

Added in version 1.4.0.

Raises

ValueError: When the table already exists and if_exists is ‘fail’ (the default).

Notes

Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

Not all datastores support method="multi". Oracle, for example, does not support multi-value insert.

References

Examples

Create an in-memory SQLite database.

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)

Create a table from scratch with 3 rows.

>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
     name
0  User 1
1  User 2
2  User 3

>>> df.to_sql(name='users', con=engine)
3
>>> from sqlalchemy import text
>>> with engine.connect() as conn:
...    conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

An sqlalchemy.engine.Connection can also be passed to con:

>>> with engine.begin() as connection:
...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
...     df1.to_sql(name='users', con=connection, if_exists='append')
2

This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql(name='users', con=engine, if_exists='append')
2
>>> with engine.connect() as conn:
...    conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
 (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
 (1, 'User 7')]

Overwrite the table with just df2.

>>> df2.to_sql(name='users', con=engine, if_exists='replace',
...            index_label='id')
2
>>> with engine.connect() as conn:
...    conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 6'), (1, 'User 7')]

Use method to define a callable insertion method to do nothing if there’s a primary key conflict on a table in a PostgreSQL database.

>>> from sqlalchemy.dialects.postgresql import insert
>>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
...     # "a" is the primary key in "conflict_table"
...     data = [dict(zip(keys, row)) for row in data_iter]
...     stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
...     result = conn.execute(stmt)
...     return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing)  
0

For MySQL, a callable to update columns b and c if there’s a conflict on a primary key.

>>> from sqlalchemy.dialects.mysql import insert
>>> def insert_on_conflict_update(table, conn, keys, data_iter):
...     # update columns "b" and "c" on primary key conflict
...     data = [dict(zip(keys, row)) for row in data_iter]
...     stmt = (
...         insert(table.table)
...         .values(data)
...     )
...     stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
...     result = conn.execute(stmt)
...     return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update)  
2

Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
     A
0  1.0
1  NaN
2  2.0

>>> from sqlalchemy.types import Integer
>>> df.to_sql(name='integers', con=engine, index=False,
...           dtype={"A": Integer()})
3

>>> with engine.connect() as conn:
...   conn.execute(text("SELECT * FROM integers")).fetchall()
[(1,), (None,), (2,)]

sparkle.PerformanceDataFrame.to_stata(self, path: FilePath | WriteBuffer[bytes], *, convert_dates: dict[Hashable, str] | None = None, write_index: bool = True, byteorder: ToStataByteorder | None = None, time_stamp: datetime.datetime | None = None, data_label: str | None = None, variable_labels: dict[Hashable, str] | None = None, version: int | None = 114, convert_strl: Sequence[Hashable] | None = None, compression: CompressionOptions = 'infer', storage_options: StorageOptions | None = None, value_labels: dict[Hashable, dict[float, str]] | None = None) → None

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. “dta” files contain a Stata dataset.

Parameters

pathstr, path object, or buffer

String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function.

convert_datesdict

Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are ‘tc’, ‘td’, ‘tm’, ‘tw’, ‘th’, ‘tq’, ‘ty’. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to ‘tc’. Raises NotImplementedError if a datetime column has timezone information.

write_indexbool

Write the index to Stata dataset.

byteorderstr

Can be “>”, “<”, “little”, or “big”. default is sys.byteorder.

time_stampdatetime

A datetime to use as file creation date. Default is the current time.

data_labelstr, optional

A label for the data set. Must be 80 characters or smaller.

variable_labelsdict

Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller.

version{114, 117, 118, 119, None}, default 114

Version to use in the output dta file. Set to None to let pandas decide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.

Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.

convert_strllist, optional

List of column names to convert to string columns to Stata StrL format. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

value_labelsdict of dicts

Dictionary containing columns as keys and dictionaries of column value to labels as values. Labels for a single variable must be 32,000 characters or smaller.

Added in version 1.4.0.

Raises

NotImplementedError

If datetimes contain timezone information
Column dtype is not representable in Stata

ValueError

Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime
Column listed in convert_dates is not in DataFrame
Categorical label contains more than 32,000 characters

Examples

>>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',
...                               'parrot'],
...                    'speed': [350, 18, 361, 15]})
>>> df.to_stata('animals.dta')  

Render a DataFrame to a console-friendly tabular output.

Parameters

bufstr, Path or StringIO-like, optional, default None

Buffer to write to. If None, the output is returned as a string.

columnsarray-like, optional, default None

The subset of columns to write. Writes all columns by default.

col_spaceint, list or dict of int, optional

The minimum width of each column. If a list of ints is given every integers corresponds with one column. If a dict is given, the key references the column, while the value defines the space to use..

headerbool or list of str, optional

Write out the column names. If a list of columns is given, it is assumed to be aliases for the column names.

indexbool, optional, default True

Whether to print index (row) labels.

na_repstr, optional, default ‘NaN’

String representation of NaN to use.

formatterslist, tuple or dict of one-param. functions, optional

Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

float_formatone-parameter function, optional, default None

Formatter function to apply to columns’ elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.

sparsifybool, optional, default True

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_namesbool, optional, default True

Prints the names of the indexes.

justifystr, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are

left
right
center
justify
justify-all
start
end
inherit
match-parent
initial
unset.

max_rowsint, optional

Maximum number of rows to display in the console.

max_colsint, optional

Maximum number of columns to display in the console.

show_dimensionsbool, default False

Display DataFrame dimensions (number of rows by number of columns).

decimalstr, default ‘.’

Character recognized as decimal separator, e.g. ‘,’ in Europe.

line_widthint, optional

Width to wrap a line in characters.

min_rowsint, optional

The number of rows to display in the console in a truncated repr (when number of rows is above max_rows).

max_colwidthint, optional

Max width to truncate each column in characters. By default, no limit.

encodingstr, default “utf-8”

Set character encoding.

Returns

str or None: If buf is None, returns the result as a string. Otherwise returns None.

Examples

>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
   col1  col2
0     1     4
1     2     5
2     3     6

sparkle.PerformanceDataFrame.to_timestamp(self, freq: Frequency | None = None, how: ToTimestampHow = 'start', axis: Axis = 0, copy: bool | None = None) → DataFrame

Cast to DatetimeIndex of timestamps, at beginning of period.

Parameters

freqstr, default frequency of PeriodIndex: Desired frequency.
how{‘s’, ‘e’, ‘start’, ‘end’}: Convention for converting period to timestamp; start of period vs. end.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to convert (the index by default).
copybool, default True: If False then underlying input data is not copied.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: The DataFrame has a DatetimeIndex.

Examples

>>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d, index=idx)
>>> df1
      col1   col2
2023     1      3
2024     2      4

The resulting timestamps will be at the beginning of the year in this case

>>> df1 = df1.to_timestamp()
>>> df1
            col1   col2
2023-01-01     1      3
2024-01-01     2      4
>>> df1.index
DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)

Using freq which is the offset that the Timestamps will have

>>> df2 = pd.DataFrame(data=d, index=idx)
>>> df2 = df2.to_timestamp(freq='M')
>>> df2
            col1   col2
2023-01-31     1      3
2024-01-31     2      4
>>> df2.index
DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)

sparkle.PerformanceDataFrame.to_xarray(self)

Return an xarray object from the pandas object.

Returns

xarray.DataArray or xarray.Dataset: Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.

Notes

See the xarray docs

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
...                    ('parrot', 'bird', 24.0, 2),
...                    ('lion', 'mammal', 80.5, 4),
...                    ('monkey', 'mammal', np.nan, 4)],
...                   columns=['name', 'class', 'max_speed',
...                            'num_legs'])
>>> df
     name   class  max_speed  num_legs
0  falcon    bird      389.0         2
1  parrot    bird       24.0         2
2    lion  mammal       80.5         4
3  monkey  mammal        NaN         4

>>> df.to_xarray()  
<xarray.Dataset>
Dimensions:    (index: 4)
Coordinates:
  * index      (index) int64 32B 0 1 2 3
Data variables:
    name       (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
    class      (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
    max_speed  (index) float64 32B 389.0 24.0 80.5 nan
    num_legs   (index) int64 32B 2 2 4 4

>>> df['max_speed'].to_xarray()  
<xarray.DataArray 'max_speed' (index: 4)>
array([389. ,  24. ,  80.5,   nan])
Coordinates:
  * index    (index) int64 0 1 2 3

>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
...                         '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
...                               'animal': ['falcon', 'parrot',
...                                          'falcon', 'parrot'],
...                               'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])

>>> df_multiindex
                   speed
date       animal
2018-01-01 falcon    350
           parrot     18
2018-01-02 falcon    361
           parrot     15

>>> df_multiindex.to_xarray()  
<xarray.Dataset>
Dimensions:  (date: 2, animal: 2)
Coordinates:
  * date     (date) datetime64[ns] 2018-01-01 2018-01-02
  * animal   (animal) object 'falcon' 'parrot'
Data variables:
    speed    (date, animal) int64 350 18 361 15

Render a DataFrame to an XML document.

Added in version 1.3.0.

Parameters

path_or_bufferstr, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.

indexbool, default True

Whether to include index in XML document.

root_namestr, default ‘data’

The name of root element in XML document.

row_namestr, default ‘row’

The name of row element in XML document.

na_repstr, optional

Missing data representation.

attr_colslist-like, optional

List of columns to write as attributes in row element. Hierarchical columns will be flattened with underscore delimiting the different levels.

elem_colslist-like, optional

List of columns to write as children in row element. By default, all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels.

namespacesdict, optional

All namespaces to be defined in root element. Keys of dict should be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example,

namespaces = {"": "https://example.com"}

prefixstr, optional

Namespace prefix to be used for every element and/or attribute in document. This should be one of the keys in namespaces dict.

encodingstr, default ‘utf-8’

Encoding of the resulting document.

xml_declarationbool, default True

Whether to include the XML declaration at start of document.

pretty_printbool, default True

Whether output should be pretty printed with indentation and line breaks.

parser{‘lxml’,’etree’}, default ‘lxml’

Parser module to use for building of tree. Only ‘lxml’ and ‘etree’ are supported. With ‘lxml’, the ability to use XSLT stylesheet is supported.

stylesheetstr, path object or file-like object, optional

A URL, file-like object, or a raw string containing an XSLT script used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requires lxml to be installed. Only XSLT 1.0 scripts and not later versions is currently supported.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buffer’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

Added in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Returns

None or str: If io is None, returns the resulting XML format as a string. Otherwise returns None.

Examples

>>> df = pd.DataFrame({'shape': ['square', 'circle', 'triangle'],
...                    'degrees': [360, 360, 180],
...                    'sides': [4, np.nan, 3]})

>>> df.to_xml()  
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row>
    <index>0</index>
    <shape>square</shape>
    <degrees>360</degrees>
    <sides>4.0</sides>
  </row>
  <row>
    <index>1</index>
    <shape>circle</shape>
    <degrees>360</degrees>
    <sides/>
  </row>
  <row>
    <index>2</index>
    <shape>triangle</shape>
    <degrees>180</degrees>
    <sides>3.0</sides>
  </row>
</data>

>>> df.to_xml(attr_cols=[
...           'index', 'shape', 'degrees', 'sides'
...           ])  
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row index="0" shape="square" degrees="360" sides="4.0"/>
  <row index="1" shape="circle" degrees="360"/>
  <row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>

>>> df.to_xml(namespaces={"doc": "https://example.com"},
...           prefix="doc")  
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
  <doc:row>
    <doc:index>0</doc:index>
    <doc:shape>square</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides>4.0</doc:sides>
  </doc:row>
  <doc:row>
    <doc:index>1</doc:index>
    <doc:shape>circle</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides/>
  </doc:row>
  <doc:row>
    <doc:index>2</doc:index>
    <doc:shape>triangle</doc:shape>
    <doc:degrees>180</doc:degrees>
    <doc:sides>3.0</doc:sides>
  </doc:row>
</doc:data>

sparkle.PerformanceDataFrame.transform(self, func: AggFuncType, axis: Axis = 0, *args, **kwargs) → DataFrame

Call func on self producing a DataFrame with the same axis shape as self.

Parameters

funcfunction, str, list-like or dict-like

Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.

Accepted combinations are:

function
string function name
list-like of functions and/or function names, e.g. [np.exp, 'sqrt']
dict-like of axis labels -> functions, function names or list-like of such.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns

DataFrame: A DataFrame that must have the same length as self.

Raises

ValueError : If the returned DataFrame has a different length than self.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

Examples

>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})
>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056

You can call transform on a GroupBy object:

>>> df = pd.DataFrame({
...     "Date": [
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df
         Date  Data
0  2015-05-08     5
1  2015-05-07     8
2  2015-05-06     6
3  2015-05-05     1
4  2015-05-08    50
5  2015-05-07   100
6  2015-05-06    60
7  2015-05-05   120
>>> df.groupby('Date')['Data'].transform('sum')
0     55
1    108
2     66
3    121
4     55
5    108
6     66
7    121
Name: Data, dtype: int64

>>> df = pd.DataFrame({
...     "c": [1, 1, 1, 2, 2, 2, 2],
...     "type": ["m", "n", "o", "m", "m", "n", "n"]
... })
>>> df
   c type
0  1    m
1  1    n
2  1    o
3  2    m
4  2    m
5  2    n
6  2    n
>>> df['size'] = df.groupby('c')['type'].transform(len)
>>> df
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4

sparkle.PerformanceDataFrame.transpose(self, *args, copy: bool = False) → DataFrame

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property T is an accessor to the method transpose().

Parameters

*argstuple, optional

Accepted for compatibility with NumPy.

copybool, default False

Whether to copy the data after transposing, even for DataFrames with a single dtype.

Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

DataFrame: The transposed DataFrame.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4

>>> df1_transposed = df1.T  # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    int64
col2    int64
dtype: object
>>> df1_transposed.dtypes
0    int64
1    int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {'name': ['Alice', 'Bob'],
...       'score': [9.5, 8],
...       'employed': [False, True],
...       'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
    name  score  employed  kids
0  Alice    9.5     False     0
1    Bob    8.0      True     0

>>> df2_transposed = df2.T  # or df2.transpose()
>>> df2_transposed
              0     1
name      Alice   Bob
score       9.5   8.0
employed  False  True
kids          0     0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name         object
score       float64
employed       bool
kids          int64
dtype: object
>>> df2_transposed.dtypes
0    object
1    object
dtype: object

sparkle.PerformanceDataFrame.truediv(self, other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

DataFrame: Result of the arithmetic operation.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

sparkle.PerformanceDataFrame.truncate(self, before=None, after=None, axis: int | Literal['index', 'columns', 'rows'] | None = None, copy: bool | None = None) → None

Truncate a Series or DataFrame before and after some index value.

This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.

Parameters

beforedate, str, int: Truncate all rows before this index value.
afterdate, str, int: Truncate all rows after this index value.
axis{0 or ‘index’, 1 or ‘columns’}, optional: Axis to truncate. Truncates the index (rows) by default. For Series this parameter is unused and defaults to 0.
copybool, default is True,: Return a copy of the truncated section.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

type of caller: The truncated Series or DataFrame.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples

>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
...                    'B': ['f', 'g', 'h', 'i', 'j'],
...                    'C': ['k', 'l', 'm', 'n', 'o']},
...                   index=[1, 2, 3, 4, 5])
>>> df
   A  B  C
1  a  f  k
2  b  g  l
3  c  h  m
4  d  i  n
5  e  j  o

>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n

The columns of a DataFrame can be truncated.

>>> df.truncate(before="A", after="B", axis="columns")
   A  B
a  f
b  g
c  h
d  i
e  j

For Series, only rows can be truncated.

>>> df['A'].truncate(before=2, after=4)
2    b
3    c
4    d
Name: A, dtype: object

The index values in truncate can be datetimes or string dates.

>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
                     A
2016-01-31 23:59:56  1
2016-01-31 23:59:57  1
2016-01-31 23:59:58  1
2016-01-31 23:59:59  1
2016-02-01 00:00:00  1

>>> df.truncate(before=pd.Timestamp('2016-01-05'),
...             after=pd.Timestamp('2016-01-10')).tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

>>> df.truncate('2016-01-05', '2016-01-10').tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Note that truncate assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

>>> df.loc['2016-01-05':'2016-01-10', :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1

sparkle.PerformanceDataFrame.tz_convert(self, tz, axis: int | Literal['index', 'columns', 'rows'] = 0, level=None, copy: bool | None = None) → None

Convert tz-aware axis to target time zone.

Parameters

tzstr or tzinfo object or None: Target time zone. Passing None will convert to UTC and remove the timezone information.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to convert
levelint, str, default None: If axis is a MultiIndex, convert a specific level. Otherwise must be None.
copybool, default True: Also make a copy of the underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Returns

Series/DataFrame: Object with time zone converted axis.

Raises

TypeError: If the axis is tz-naive.

Examples

Change to another time zone:

>>> s = pd.Series(
...     [1],
...     index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),
... )
>>> s.tz_convert('Asia/Shanghai')
2018-09-15 07:30:00+08:00    1
dtype: int64

Pass None to convert to UTC and get a tz-naive index:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_convert(None)
2018-09-14 23:30:00    1
dtype: int64

sparkle.PerformanceDataFrame.tz_localize(self, tz, axis: Axis = 0, level=None, copy: bool_t | None = None, ambiguous: TimeAmbiguous = 'raise', nonexistent: TimeNonexistent = 'raise') → Self

Localize tz-naive index of a Series or DataFrame to target time zone.

This operation localizes the Index. To localize the values in a timezone-naive Series, use Series.dt.tz_localize().

Parameters

tzstr or tzinfo or None

Time zone to localize. Passing None will remove the time zone information and preserve local time.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to localize

levelint, str, default None

If axis ia a MultiIndex, localize a specific level. Otherwise must be None.

copybool, default True

Also make a copy of the underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’

When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.

‘infer’ will attempt to infer fall dst-transition hours based on order
bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
‘NaT’ will return NaT where there are ambiguous times
‘raise’ will raise an AmbiguousTimeError if there are ambiguous times.

nonexistentstr, default ‘raise’

A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are:

‘shift_forward’ will shift the nonexistent time forward to the closest existing time
‘shift_backward’ will shift the nonexistent time backward to the closest existing time
‘NaT’ will return NaT where there are nonexistent times
timedelta objects will shift nonexistent times by the timedelta
‘raise’ will raise an NonExistentTimeError if there are nonexistent times.

Returns

Series/DataFrame: Same type as the input.

Raises

TypeError: If the TimeSeries is tz-aware and tz is not None.

Examples

Localize local times:

>>> s = pd.Series(
...     [1],
...     index=pd.DatetimeIndex(['2018-09-15 01:30:00']),
... )
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00    1
dtype: int64

Pass None to convert to tz-naive index and preserve local time:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_localize(None)
2018-09-15 01:30:00    1
dtype: int64

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.Series(range(7),
...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 03:00:00',
...                                       '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00    0
2018-10-28 02:00:00+02:00    1
2018-10-28 02:30:00+02:00    2
2018-10-28 02:00:00+01:00    3
2018-10-28 02:30:00+01:00    4
2018-10-28 03:00:00+01:00    5
2018-10-28 03:30:00+01:00    6
dtype: int64

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.Series(range(3),
...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
...                                       '2018-10-28 02:36:00',
...                                       '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00    0
2018-10-28 02:36:00+02:00    1
2018-10-28 03:46:00+01:00    2
dtype: int64

If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or ‘shift_forward’ or ‘shift_backward’.

>>> s = pd.Series(range(2),
...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
...                                       '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00    0
2015-03-29 03:30:00+02:00              1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
2015-03-29 03:30:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64

sparkle.PerformanceDataFrame.unstack(self, level: IndexLabel = -1, fill_value=None, sort: bool = True)

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Parameters

levelint, str, or list of these, default -1 (last level): Level(s) of index to unstack, can pass level name.
fill_valueint, str or dict: Replace NaN with this value if the unstack produces missing values.
sortbool, default True: Sort the level(s) in the resulting MultiIndex columns.

Returns

Series or DataFrame

Notes

Reference the user guide for more examples.

Examples

>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
...                                    ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64

>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0

>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0

>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64

sparkle.PerformanceDataFrame.update(self, other, join: UpdateJoin = 'left', overwrite: bool = True, filter_func=None, errors: IgnoreRaise = 'ignore') → None

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters

otherDataFrame, or object coercible into a DataFrame

Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.

join{‘left’}, default ‘left’

Only left join is implemented, keeping the index and columns of the original object.

overwritebool, default True

How to handle non-NA values for overlapping keys:

True: overwrite original DataFrame’s values with values from other.
False: only update values that are NA in the original DataFrame.

filter_funccallable(1d-array) -> bool 1d-array, optional

Can choose to replace values other than NA. Return True for values that should be updated.

errors{‘raise’, ‘ignore’}, default ‘ignore’

If ‘raise’, will raise a ValueError if the DataFrame and other both contain non-NA data in the same place.

Returns

None: This method directly changes calling object.

Raises

ValueError

When errors=’raise’ and there’s overlapping non-NA data.
When errors is not either ‘ignore’ or ‘raise’

NotImplementedError

If join != ‘left’

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame’s length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  y
2  c  f

For Series, its name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e', 'f'], name='B')
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400., 500., 600.]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0

sparkle.PerformanceDataFrame.value_counts(self, subset: IndexLabel | None = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True) → Series

Return a Series containing the frequency of each distinct row in the Dataframe.

Parameters

subsetlabel or list of labels, optional: Columns to use when counting unique combinations.
normalizebool, default False: Return proportions rather than frequencies.
sortbool, default True: Sort by frequencies when True. Sort by DataFrame column values when False.
ascendingbool, default False: Sort in ascending order.
dropnabool, default True: Don’t include counts of rows that contain NA values.

Added in version 1.3.0.

Returns

Series

Notes

The returned Series will have a MultiIndex with one level per input column but an Index (non-multi) for a single label. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
...                    'num_wings': [2, 0, 0, 0]},
...                   index=['falcon', 'dog', 'cat', 'ant'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0

>>> df.value_counts()
num_legs  num_wings
4         0            2
2         2            1
6         0            1
Name: count, dtype: int64

>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
Name: count, dtype: int64

>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
Name: count, dtype: int64

>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
2         2            0.25
6         0            0.25
Name: proportion, dtype: float64

With dropna set to False we can also count rows with NA values.

>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
>>> df
  first_name middle_name
0       John       Smith
1       Anne        <NA>
2       John        <NA>
3       Beth      Louise

>>> df.value_counts()
first_name  middle_name
Beth        Louise         1
John        Smith          1
Name: count, dtype: int64

>>> df.value_counts(dropna=False)
first_name  middle_name
Anne        NaN            1
Beth        Louise         1
John        Smith          1
            NaN            1
Name: count, dtype: int64

>>> df.value_counts("first_name")
first_name
John    2
Anne    1
Beth    1
Name: count, dtype: int64

sparkle.PerformanceDataFrame.var(self, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs)

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters

axis{index (0), columns (1)}: For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.var with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.

Returns

Series or DataFrame (if level specified)

Examples

>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
...                    'age': [21, 25, 62, 43],
...                    'height': [1.61, 1.87, 1.49, 2.01]}
...                   ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

>>> df.var()
age       352.916667
height      0.056367
dtype: float64

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.var(ddof=0)
age       264.687500
height      0.042275
dtype: float64

sparkle.PerformanceDataFrame.verify_indexing(self: PerformanceDataFrame, objective: str, run_id: int) → tuple[str, int]

Method to check whether data indexing is correct.

Users are allowed to use the Performance Dataframe without the second and fourth dimension (Objective and Run respectively) in the case they only have one objective or only do one run. This method adjusts the indexing for those cases accordingly.

Args:: objective: The given objective name run_id: The given run index
Returns:: A tuple representing the (possibly adjusted) Objective and Run index.

sparkle.PerformanceDataFrame.verify_objective(self: PerformanceDataFrame, objective: str) → str

Method to check whether the specified objective is valid.

Users are allowed to index the dataframe without specifying all dimensions. However, when dealing with multiple objectives this is not allowed and this is verified here. If we have only one objective this is returned. Otherwise, if an objective is specified by the user this is returned.

Args:: objective: The objective given by the user

sparkle.PerformanceDataFrame.verify_run_id(self: PerformanceDataFrame, run_id: int) → int

Method to check whether run id is valid.

Similar to verify_objective but here we check the dimensionality of runs.

Args:: run_id: the run as specified by the user.

sparkle.PerformanceDataFrame.where(self, cond, other=nan, *, inplace: bool_t = False, axis: Axis | None = None, level: Level | None = None) → Self | None

Replace values where the condition is False.

Parameters

condbool Series/DataFrame, array-like, or callable: Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
otherscalar, Series/DataFrame, or callable: Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).
inplacebool, default False: Whether to perform the operation in place on the data.
axisint, default None: Alignment axis if needed. For Series this parameter is unused and defaults to 0.
levelint, default None: Alignment level if needed.

Returns

Same type as caller or None if inplace=True.

Notes

The where method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is True the element is used; otherwise the corresponding element from the DataFrame other is used. If the axis of other does not align with axis of cond Series/DataFrame, the misaligned index positions will be filled with False.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the where documentation in indexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)
0    99
1     1
2    99
3    99
4    99
dtype: int64

>>> s.where(s > 1, 10)
  10
  10
  2
  3
  4
dtype: int64
>>> s.mask(s > 1, 10)
   0
   1
  10
  10
  10
dtype: int64

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

sparkle.PerformanceDataFrame.xs(self, key: Hashable | Sequence[Hashable], axis: int | Literal['index', 'columns', 'rows'] = 0, level: Hashable | Sequence[Hashable] | None = None, drop_level: bool = True) → None

Return cross-section from the Series/DataFrame.

This method takes a key argument to select data at a particular level of a MultiIndex.

Parameters

keylabel or tuple of label: Label contained in the index, or partially in a MultiIndex.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Axis to retrieve cross-section on.
levelobject, defaults to first n levels (n=1 or len(key)): In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position.
drop_levelbool, default True: If False, returns object with same levels as self.

Returns

Series or DataFrame: Cross-section from the original Series or DataFrame corresponding to the selected index levels.

Notes

xs can not be used to set values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see MultiIndex Slicers.

Examples

>>> d = {'num_legs': [4, 4, 2, 2],
...      'num_wings': [0, 0, 2, 2],
...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
...      'animal': ['cat', 'dog', 'bat', 'penguin'],
...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
                           num_legs  num_wings
class  animal  locomotion
mammal cat     walks              4          0
       dog     walks              4          0
       bat     flies              2          2
bird   penguin walks              2          2

Get values at specified index

>>> df.xs('mammal')
                   num_legs  num_wings
animal locomotion
cat    walks              4          0
dog    walks              4          0
bat    flies              2          2

Get values at several indexes

>>> df.xs(('mammal', 'dog', 'walks'))
num_legs     4
num_wings    0
Name: (mammal, dog, walks), dtype: int64

Get values at specified index and level

>>> df.xs('cat', level=1)
                   num_legs  num_wings
class  locomotion
mammal walks              4          0

Get values at several indexes and levels

>>> df.xs(('bird', 'walks'),
...       level=[0, 'locomotion'])
         num_legs  num_wings
animal
penguin         2          2

Get values at specified column and axis

>>> df.xs('num_wings', axis=1)
class   animal   locomotion
mammal  cat      walks         0
        dog      walks         0
        bat      flies         2
bird    penguin  walks         2
Name: num_wings, dtype: int64

RunSolver

SATVerifier

SelectionScenario

A scenario for a Selector.

sparkle.SelectionScenario.__init__(self: SelectionScenario, parent_directory: Path, selector: Selector, objective: SparkleObjective, performance_data: PerformanceDataFrame | Path, feature_data: FeatureDataFrame | Path, feature_extractors: list[str] = None, solver_cutoff: int | float = None, extractor_cutoff: int | float = None, ablate: bool = False, subdir_path: Path = None) → None: Initialize a scenario for a selector.

sparkle.SelectionScenario.create_scenario(self: SelectionScenario) → None: Prepare the scenario directories.

sparkle.SelectionScenario.create_scenario_file(self: SelectionScenario) → None

Create the scenario file.

Write the scenario to file.

sparkle.SelectionScenario.from_file(scenario_file: Path) → SelectionScenario: Reads scenario file and initalises SelectorScenario.

sparkle.SelectionScenario.serialise(self: SelectionScenario) → dict: Serialize the scenario.

Selector

The Selector class for handling Algorithm Selection.

sparkle.Selector.__init__(self: Selector, selector_class: AbstractModelBasedSelector, model_class: AbstractPredictor | ClassifierMixin | RegressorMixin) → None

Initialize the Selector object.

Args:: selector_class: The (name of) Selector class to construct. model_class: The (name of) model class the selector will use.

sparkle.Selector.construct(self: Selector, selection_scenario: SelectionScenario, run_on: Runner = Runner.SLURM, job_name: str = None, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, base_dir: Path = PosixPath('.')) → Run

Construct the Selector.

Args:: selection_scenario: The scenario to construct the Selector for. run_on: Which runner to use. Defaults to slurm. job_name: Name to give the construction job when submitting. sbatch_options: Additional options to pass to sbatch. slurm_prepend: Slurm script to prepend to the sbatch base_dir: The base directory to run the Selector in.
Returns:: The construction Run

sparkle.Selector.run(self: Selector, selector_path: Path, instance: str, feature_data: FeatureDataFrame) → list: Run the Selector, returning the prediction schedule upon success.

sparkle.Selector.run_cli(self: Selector, scenario_path: Path, instance_set: InstanceSet | list[Path], feature_data: Path, run_on: Runner = Runner.LOCAL, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, job_name: str = None, dependencies: list[Run] = None, log_dir: Path = None) → Run

Run the Selector CLI and write result to the Scenario PerformanceDataFrame.

Args:: scenario_path: The path to the scenario with the Selector to run. instance_set: The instance set to run the Selector on. feature_data: The instance feature data to use. run_on: Which runner to use. Defaults to slurm. sbatch_options: Additional options to pass to sbatch. slurm_prepend: Slurm script to prepend to the sbatch job_name: Name to give the Slurm job when submitting. dependencies: List of dependencies to add to the job. log_dir: The directory to write logs to.
Returns:: The Run object.

Settings

SlurmBatch

SolutionVerifier

Solver

Class to handle a solver and its directories.

sparkle.Solver.__init__(self: Solver, directory: Path, runsolver_exec: Path = None, deterministic: bool = None, verifier: SolutionVerifier = None) → None

Initialize solver.

Args:

directory: Directory of the solver. runsolver_exec: Path to the runsolver executable.

By default, runsolver in directory.

deterministic: Bool indicating determinism of the algorithm.: Defaults to False.

verifier: The solution verifier to use. If None, no verifier is used.

sparkle.Solver.__repr__(self: Solver) → str: Return detailed representation of the solver.

sparkle.Solver.__str__(self: Solver) → str: Return the string representation of the solver.

sparkle.Solver.build_cmd(self: Solver, instance: str | list[str], objectives: list[SparkleObjective], seed: int, cutoff_time: int = None, configuration: dict = None, log_dir: Path = None) → list[str]

Build the solver call on an instance with a configuration.

Args:: instance: Path to the instance. objectives: List of sparkle objectives. seed: Seed of the solver. cutoff_time: Cutoff time for the solver. configuration: Configuration of the solver. log_dir: Directory path for logs.
Returns:: List of commands and arguments to execute the solver.

sparkle.Solver.config_str_to_dict(config_str: str) → dict[str, str]: Parse a configuration string to a dictionary.

sparkle.Solver.get_configuration_space(self: Solver) → ConfigurationSpace: Get the ConfigurationSpace of the PCS file.

sparkle.Solver.get_pcs_file(self: Solver, port_type: PCSConvention) → Path

Get path of the parameter file of a specific convention.

Args:

port_type: Port type of the parameter file. If None, will return the: file with the shortest name.

Returns:

Path to the parameter file. None if it can not be resolved.

sparkle.Solver.parse_solver_output(solver_output: str, solver_call: list[str | Path] = None, objectives: list[SparkleObjective] = None, verifier: SolutionVerifier = None) → dict[str, Any]

Parse the output of the solver.

Args:: solver_output: The output of the solver run which needs to be parsed solver_call: The solver call used to run the solver objectives: The objectives to apply to the solver output verifier: The verifier to check the solver output
Returns:: Dictionary representing the parsed solver output

sparkle.Solver.port_pcs(self: Solver, port_type: PCSConvention) → None: Port the parameter file to the given port type.

sparkle.Solver.read_pcs_file(self: Solver) → bool: Checks if the pcs file can be read.

sparkle.Solver.run(self: Solver, instances: str | list[str] | InstanceSet | list[InstanceSet], objectives: list[SparkleObjective], seed: int, cutoff_time: int = None, configuration: dict = None, run_on: Runner = Runner.LOCAL, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, log_dir: Path = None) → SlurmRun | list[dict[str, Any]] | dict[str, Any]

Run the solver on an instance with a certain configuration.

Args:

instances: The instance(s) to run the solver on, list in case of multi-file.: In case of an instance set, will run on all instances in the set.

objectives: List of sparkle objectives. seed: Seed to run the solver with. Fill with abitrary int in case of

determnistic solver.

cutoff_time: The cutoff time for the solver, measured through RunSolver.: If None, will be executed without RunSolver.

configuration: The solver configuration to use. Can be empty. run_on: Whether to run on slurm or locally. sbatch_options: The sbatch options to use. slurm_prepend: The script to prepend to a slurm script. log_dir: The log directory to use.

Returns:

Solver output dict possibly with runsolver values.

sparkle.Solver.run_performance_dataframe(self: Solver, instances: str | list[str] | InstanceSet, performance_dataframe: PerformanceDataFrame, config_ids: str | list[str] = None, run_ids: list[int] | list[list[int]] = None, cutoff_time: int = None, objective: SparkleObjective = None, train_set: InstanceSet = None, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, dependencies: list[SlurmRun] = None, log_dir: Path = None, base_dir: Path = None, job_name: str = None, run_on: Runner = Runner.SLURM) → Run

Run the solver from and place the results in the performance dataframe.

This in practice actually runs Solver.run, but has a little script before/after, to read and write to the performance dataframe.

Args:

instances: The instance(s) to run the solver on. In case of an instance set,: or list, will create a job for all instances in the set/list.

config_ids: The config indices to use in the performance dataframe. performance_dataframe: The performance dataframe to use. run_ids: List of run ids to use. If list of list, a list of runs is given

per instance. Otherwise, all runs are used for each instance.

cutoff_time: The cutoff time for the solver, measured through RunSolver. objective: The objective to use, only relevant when determining the best

configuration.

train_set: The training set to use. If present, will determine the best: configuration of the solver using these instances and run with it on all instances in the instance argument.

sbatch_options: List of slurm batch options to use slurm_prepend: Slurm script to prepend to the sbatch dependencies: List of slurm runs to use as dependencies log_dir: Path where to place output files. Defaults to CWD. base_dir: Path where to place output files. job_name: Name of the job

If None, will generate a name based on Solver and Instances

run_on: On which platform to run the jobs. Default: Slurm.

Returns:

SlurmRun or Local run of the job.

SolverStatus

SparkleCallable

SparkleObjective

UseTime

about

cli_types

configspace

configurator

This package provides configurator support for Sparkle.

class sparkle.configurator.AblationScenario(configuration_scenario: ConfigurationScenario, test_set: InstanceSet, cutoff_length: str, concurrent_clis: int, best_configuration: dict, ablation_racing: bool = False)[source]

Class for ablation analysis.

check_for_ablation() → bool[source]: Checks if ablation has terminated successfully.

static check_requirements(verbose: bool = False) → bool[source]: Check if Ablation Analysis is installed.

create_configuration_file() → Path[source]

Create a configuration file for ablation analysis.

Returns:: Path to the created configuration file.

create_instance_file(test: bool = False) → Path[source]: Create an instance file for ablation analysis.

create_scenario(override_dirs: bool = False) → None[source]: Create scenario directory and files.

static download_requirements(ablation_url: str = 'https://github.com/ADA-research/Sparkle/raw/refs/heads/development/Resources/Other/ablationAnalysis-0.9.4.zip') → None[source]: Download Ablation Analysis executable.

static from_file(path: Path, config_scenario: ConfigurationScenario) → AblationScenario[source]: Reads scenario file and initalises AblationScenario.

read_ablation_table() → list[list[str]][source]: Read from ablation table of a scenario.

property scenario_dir: Path: Return the path of the scenario directory.

submit_ablation(log_dir: Path, sbatch_options: list[str] = [], slurm_prepend: str | list[str] | Path = None, run_on: Runner = Runner.SLURM) → list[Run][source]

Submit an ablation job.

Args:: log_dir: Directory to store job logs sbatch_options: Options to pass to sbatch slurm_prepend: Script to prepend to sbatch script run_on: Determines to which RunRunner queue the job is added
Returns:: A list of Run objects. Empty when running locally.

property table_file: Path: Return the path of the table file.

property tmp_dir: Path: Return the path of the tmp directory.

property validation_dir: Path: Return the path of the validation directory.

property validation_dir_tmp: Path: Return the path of the validation tmp directory.

class sparkle.configurator.ConfigurationScenario(solver: Solver, instance_set: InstanceSet, sparkle_objectives: list[SparkleObjective], number_of_runs: int, parent_directory: Path, timestamp: str = None)[source]

Template class to handle a configuration scenarios.

property ablation_scenario: AblationScenario: Return the ablation scenario for the scenario if it exists.

property configuration_ids: list[str]

Return the IDs of the configurations for the scenario.

Only exists after the scenario has been created.

Returns:: List of configuration IDs, one for each run.

property configurator: Configurator: Return the type of configurator the scenario belongs to.

create_scenario() → None[source]

Create scenario with solver and instances in the parent directory.

This prepares all the necessary subdirectories related to configuration.

Args:: parent_directory: Directory in which the scenario should be created.

create_scenario_file() → Path[source]: Create a file with the configuration scenario.

property directory: Path: Return the path of the scenario directory.

classmethod find_scenario(directory: Path, solver: Solver, instance_set: InstanceSet, timestamp: str = None) → ConfigurationScenario[source]: Resolve a scenario from a directory and Solver / Training set.

static from_file(scenario_file: Path) → ConfigurationScenario[source]: Reads scenario file and initalises ConfigurationScenario.

property name: str: Return the name of the scenario.

property results_directory: Path: Return the path of the results directory.

property scenario_file_path: Path: Return the path of the scenario file.

serialise() → dict[source]: Serialize the configuration scenario.

property timestamp: str: Return the timestamp.

property tmp: Path: Return the path of the tmp directory.

property validation: Path: Return the path of the validation directory.

class sparkle.configurator.Configurator(multi_objective_support: bool = False)[source]

Abstact class to use different configurators like SMAC.

static check_requirements(verbose: bool = False) → bool[source]: Check if the configurator is installed.

configure(configuration_commands: list[str], data_target: PerformanceDataFrame, output: Path, scenario: ConfigurationScenario, configuration_ids: list[str] = None, validate_after: bool = True, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, num_parallel_jobs: int = None, base_dir: Path = None, run_on: Runner = Runner.SLURM) → Run[source]

Start configuration job.

This method is shared by the configurators and should be called by the implementation/subclass of the configurator.

Args:: configuration_commands: List of configurator commands to execute data_target: Performance data to store the results. output: Output directory. scenario: ConfigurationScenario to execute. configuration_ids: List of configuration ids that are to be created validate_after: Whether the configurations should be validated sbatch_options: List of slurm batch options to use slurm_prepend: Slurm script to prepend to the sbatch num_parallel_jobs: The maximum number of jobs to run in parallel base_dir: The base_dir of RunRunner where the sbatch scripts will be placed run_on: On which platform to run the jobs. Default: Slurm.
Returns:: A RunRunner Run object.

static download_requirements() → None[source]: Download the configurator.

get_status_from_logs() → None[source]: Method to scan the log files of the configurator for warnings.

property name: str: Return the name of the configurator.

static organise_output(output_source: Path, output_target: Path, scenario: ConfigurationScenario, configuration_id: str) → None | str[source]

Method to restructure and clean up after a single configurator call.

Args:: output_source: Path to the output file of the configurator run. output_target: Path to the Performance DataFrame to store result. scenario: ConfigurationScenario of the configuration. configuration_id: ID (of the run) of the configuration.

static save_configuration(scenario: ConfigurationScenario, configuration_id: str, configuration: dict, output_target: Path) → dict | None[source]

Method to save a configuration to a file.

If the output_target is None, return the configuration.

Args:: scenario: ConfigurationScenario of the configuration. Should be removed. configuration_id: ID (of the run) of the configuration. configuration: Configuration to save. output_target: Path to the Performance DataFrame to store result.

static scenario_class() → ConfigurationScenario[source]: Return the scenario class of the configurator.

extractor

feature_dataframe

features

general

get_solver_call_params

get_time_pid_random_string

implementations

importlib

inspect

instance

This package provides instance set support for Sparkle.

class sparkle.instance.FileInstanceSet(target: Path)[source]

Object representation of a set of single-file instances.

property name: str: Get instance set name.

class sparkle.instance.InstanceSet(target: Path | list[str, Path])[source]

Base object representation of a set of instances.

property all_paths: list[Path]: Returns all file paths in the instance set as a flat list.

get_path_by_name(name: str) → Path | list[Path][source]: Retrieves an instance paths by its name. Returns None upon failure.

property instance_names: list[str]: Get processed instance names for instances.

property instance_paths: list[Path]: Get processed instance paths.

property instances: list[str]: Get instance names with relative path.

property name: str: Get instance set name.

property size: int: Returns the number of instances in the set.

sparkle.instance.Instance_Set(target: any) → InstanceSet[source]: The combined interface for all instance set types.

class sparkle.instance.IterableFileInstanceSet(target: Path)[source]

Object representation of files containing multiple instances.

property size: int: Returns the number of instances in the set.

class sparkle.instance.MultiFileInstanceSet(target: Path)[source]

Object representation of a set of multi-file instances.

property all_paths: list[Path]: Returns all file paths in the instance set as a flat list.

property instances: list[str]: Get instance names with relative path for multi-file instances.

instances

objective

objective_string_regex

objective_variable_regex

parameters

performance_dataframe

platform

This package provides platform support for Sparkle.

class sparkle.platform.Option(name: str, section: str, type: Any, default_value: Any, alternatives: tuple[str, ...], help: str = '', cli_kwargs: dict[str, Any] = {})[source]

Class to define an option in the Settings.

alternatives: tuple[str, ...]: Alias for field number 4

property args: list[str]: Return the option names as a command line arguments.

cli_kwargs: dict[str, Any]: Alias for field number 6

default_value: Any: Alias for field number 3

help: str: Alias for field number 5

property kwargs: dict[str, Any]: Return the option attributes as kwargs.

name: str: Alias for field number 0

section: str: Alias for field number 1

type: Any: Alias for field number 2

class sparkle.platform.Settings(file_path: Path, argsv: Namespace = None)[source]

Class to read, write, set, and get settings.

property ablation_max_parallel_runs_per_node: int: Get the ablation max parallel runs per node.

property ablation_racing_flag: bool: Get the ablation racing flag.

property appendices: bool: Whether to include appendices in the report.

apply_arguments(argsv: Namespace) → None[source]: Apply the arguments to the settings.

static check_settings_changes(cur_settings: Settings, prev_settings: Settings, verbose: bool = True) → bool[source]

Check if there are changes between the previous and the current settings.

Prints any section changes, printing None if no setting was found.

Args:: cur_settings: The current settings prev_settings: The previous settings verbose: Verbosity of the function
Returns:: True iff there are changes.

property configurator: Configurator: Get the configurator class (instance).

property configurator_max_iterations: int: Get the amount of configurator iterations to do.

property configurator_number_of_runs: int: Get the amount of configurator runs to do.

property configurator_solver_call_budget: int: The amount of calls a configurator can do to the solver.

property extractor_cutoff_time: int: Extractor cutoff time in seconds.

get_configurator_output_path(configurator: Configurator) → Path[source]: Return the configurator output path.

get_configurator_settings(configurator_name: str) → dict[str, any][source]: Return the settings of a specific configurator.

property irace_first_test: int: Return the first test for IRACE.

property irace_max_experiments: int: Return the max experiments for IRACE.

property irace_max_iterations: int: Return the max iterations for IRACE.

property irace_max_time: int: Return the max time in seconds for IRACE.

property irace_mu: int: Return the mu for IRACE.

property minimum_marginal_contribution: float: Get the minimum marginal contribution.

property objectives: list[SparkleObjective]: Get the objectives for Sparkle.

property parallel_portfolio_check_interval: int: Return the check interval for the parallel portfolio.

property parallel_portfolio_num_seeds_per_solver: int: Return the number of seeds per solver for the parallel portfolio.

property paramils_cli_cores: int: The number of CPU cores to use for ParamILS.

property paramils_cpu_time_budget: int: Return the CPU time budget for ParamILS.

property paramils_focused_approach: bool: Return the focused approach for ParamILS.

property paramils_max_iterations: int: Return the max iterations for ParamILS.

property paramils_max_runs: int: Return the max runs for ParamILS.

property paramils_min_runs: int: Return the min runs for ParamILS.

property paramils_number_initial_configurations: int: Return the number of initial configurations for ParamILS.

property paramils_random_restart: float: Return the random restart for ParamILS.

property paramils_use_cpu_time_in_tunertime: bool: Return the use cpu time for ParamILS.

read_settings_ini(file_path: Path) → None[source]: Read the settings from an INI file.

property run_on: Runner: On which compute to run (Local or Slurm).

property sbatch_settings: list[str]: Return the sbatch settings.

property seed: int: Seed to use in CLI commands.

property selection_class: str: Get the selection class.

property selection_model: str: Get the selection model.

property slurm_job_prepend: str: Return the slurm job prepend.

property slurm_jobs_in_parallel: int: Return the (maximum) number of jobs to run in parallel.

property smac2_cli_cores: int: Return the SMAC2 CLI cores.

property smac2_cpu_time_budget: int: Return the SMAC2 CPU budget per configuration run in seconds.

property smac2_max_iterations: int: Return the SMAC2 max iterations.

property smac2_target_cutoff_length: str: Return the SMAC2 target cutoff length.

property smac2_use_tunertime_in_cpu_time_budget: bool: Return whether SMAC2 time should be used in CPU time budget.

property smac2_wallclock_time_budget: int: Return the SMAC2 wallclock budget per configuration run in seconds.

property smac3_cpu_time_budget: int: Return the SMAC3 cputime budget in seconds.

property smac3_crash_cost: float: Return the SMAC3 crash cost.

property smac3_facade: str: Return the SMAC3 facade.

property smac3_facade_max_ratio: float: Return the SMAC3 facade max ratio.

property smac3_max_budget: int: Return the SMAC3 max budget.

property smac3_min_budget: int: Return the SMAC3 min budget.

property smac3_number_of_trials: int: Return the SMAC3 number of trials.

property smac3_termination_cost_threshold: float: Return the SMAC3 termination cost threshold.

property smac3_use_default_config: bool: Return whether SMAC3 should use the default config.

property smac3_wallclock_time_budget: int: Return the SMAC3 walltime budget in seconds.

property solver_cutoff_time: int: Solver cutoff time in seconds.

property verbosity_level: VerbosityLevel: Verbosity level to use in CLI commands.

write_settings_ini(file_path: Path) → None[source]: Write the settings to an INI file.

write_used_settings() → None[source]: Write the used settings to the default locations.

re

resolve_objective

runsolver

selector

This package provides selector support for Sparkle.

class sparkle.selector.SelectionScenario(parent_directory: Path, selector: Selector, objective: SparkleObjective, performance_data: PerformanceDataFrame | Path, feature_data: FeatureDataFrame | Path, feature_extractors: list[str] = None, solver_cutoff: int | float = None, extractor_cutoff: int | float = None, ablate: bool = False, subdir_path: Path = None)[source]

A scenario for a Selector.

create_scenario() → None[source]: Prepare the scenario directories.

create_scenario_file() → None[source]

Create the scenario file.

Write the scenario to file.

static from_file(scenario_file: Path) → SelectionScenario[source]: Reads scenario file and initalises SelectorScenario.

property instance_sets: list[str]: Get all the instance sets used in this scenario.

serialise() → dict[source]: Serialize the scenario.

property solvers: list[str]: Get the solvers used for the selector.

property test_instance_sets: list[str]: Get the test instance sets.

property test_instances: list[str]: Get the test instances.

property training_instance_sets: list[str]: Get the training instance sets.

property training_instances: list[str]: Get the training instances.

class sparkle.selector.Selector(selector_class: AbstractModelBasedSelector, model_class: AbstractPredictor | ClassifierMixin | RegressorMixin)[source]

The Selector class for handling Algorithm Selection.

construct(selection_scenario: SelectionScenario, run_on: Runner = Runner.SLURM, job_name: str = None, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, base_dir: Path = PosixPath('.')) → Run[source]

Construct the Selector.

Args:: selection_scenario: The scenario to construct the Selector for. run_on: Which runner to use. Defaults to slurm. job_name: Name to give the construction job when submitting. sbatch_options: Additional options to pass to sbatch. slurm_prepend: Slurm script to prepend to the sbatch base_dir: The base directory to run the Selector in.
Returns:: The construction Run

property name: str: Return the name of the selector.

run(selector_path: Path, instance: str, feature_data: FeatureDataFrame) → list[source]: Run the Selector, returning the prediction schedule upon success.

run_cli(scenario_path: Path, instance_set: InstanceSet | list[Path], feature_data: Path, run_on: Runner = Runner.LOCAL, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, job_name: str = None, dependencies: list[Run] = None, log_dir: Path = None) → Run[source]

Run the Selector CLI and write result to the Scenario PerformanceDataFrame.

Args:: scenario_path: The path to the scenario with the Selector to run. instance_set: The instance set to run the Selector on. feature_data: The instance feature data to use. run_on: Which runner to use. Defaults to slurm. sbatch_options: Additional options to pass to sbatch. slurm_prepend: Slurm script to prepend to the sbatch job_name: Name to give the Slurm job when submitting. dependencies: List of dependencies to add to the job. log_dir: The directory to write logs to.
Returns:: The Run object.

settings_objects

slurm_parsing

selector

This package provides selector support for Sparkle.

class sparkle.selector.Extractor(directory: Path)[source]

Extractor base class for extracting features from instances.

build_cmd(instance: Path | list[Path], feature_group: str = None, output_file: Path = None, cutoff_time: int = None, log_dir: Path = None) → list[str][source]

Builds a command line string seperated by space.

Args:

instance: The instance to run on feature_group: The optional feature group to run the extractor for. output_file: Optional file to write the output to. runsolver_args: The arguments for runsolver. If not present,

will run the extractor without runsolver.

cutoff_time: The maximum runtime. log_dir: Directory path for logs.

Returns:

The command seperated per item in the list.

property feature_groups: list[str]: Returns the various feature groups the Extractor has.

property features: list[tuple[str, str]]: Determines the features of the extractor.

get_feature_vector(result: Path, runsolver_values: Path = None) → list[str][source]

Extracts feature vector from an output file.

Args:: result: The raw output of the extractor runsolver_values: The output of runsolver.
Returns:: A list of features. Vector of missing values upon failure.

property groupwise_computation: bool: Determines if you can call the extractor per group for parallelisation.

property output_dimension: int: The size of the output vector of the extractor.

run(instance: Path | list[Path], feature_group: str = None, output_file: Path = None, cutoff_time: int = None, log_dir: Path = None) → list[list[Any]] | list[Any] | None[source]

Runs an extractor job with Runrunner.

Args:

extractor_path: Path to the executable instance: Path to the instance to run on feature_group: The feature group to compute. Must be supported by the

extractor to use.

output_file: Target output. If None, piped to the RunRunner job. cutoff_time: CPU cutoff time in seconds log_dir: Directory to write logs. Defaults to CWD.

Returns:

The features or None if an output file is used, or features can not be found.

run_cli(instance_set: InstanceSet | list[Path], feature_dataframe: FeatureDataFrame, cutoff_time: int, feature_group: str = None, run_on: Runner = Runner.SLURM, sbatch_options: list[str] = None, srun_options: list[str] = None, parallel_jobs: int = None, slurm_prepend: str | list[str] | Path = None, dependencies: list[Run] = None, log_dir: Path = None) → None[source]

Run the Extractor CLI and write result to the FeatureDataFrame.

Args:

instance_set: The instance set to run the Extractor on. feature_dataframe: The feature dataframe to write to. cutoff_time: CPU cutoff time in seconds feature_group: The feature group to compute. If left empty,

will run on all feature groups.

run_on: The runner to use. sbatch_options: Additional options to pass to sbatch. srun_options: Additional options to pass to srun. parallel_jobs: Number of parallel jobs to run. slurm_prepend: Slurm script to prepend to the sbatch dependencies: List of dependencies to add to the job. log_dir: The directory to write logs to.

class sparkle.selector.SelectionScenario(parent_directory: Path, selector: Selector, objective: SparkleObjective, performance_data: PerformanceDataFrame | Path, feature_data: FeatureDataFrame | Path, feature_extractors: list[str] = None, solver_cutoff: int | float = None, extractor_cutoff: int | float = None, ablate: bool = False, subdir_path: Path = None)[source]

A scenario for a Selector.

create_scenario() → None[source]: Prepare the scenario directories.

create_scenario_file() → None[source]

Create the scenario file.

Write the scenario to file.

static from_file(scenario_file: Path) → SelectionScenario[source]: Reads scenario file and initalises SelectorScenario.

property instance_sets: list[str]: Get all the instance sets used in this scenario.

serialise() → dict[source]: Serialize the scenario.

property solvers: list[str]: Get the solvers used for the selector.

property test_instance_sets: list[str]: Get the test instance sets.

property test_instances: list[str]: Get the test instances.

property training_instance_sets: list[str]: Get the training instance sets.

property training_instances: list[str]: Get the training instances.

class sparkle.selector.Selector(selector_class: AbstractModelBasedSelector, model_class: AbstractPredictor | ClassifierMixin | RegressorMixin)[source]

The Selector class for handling Algorithm Selection.

construct(selection_scenario: SelectionScenario, run_on: Runner = Runner.SLURM, job_name: str = None, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, base_dir: Path = PosixPath('.')) → Run[source]

Construct the Selector.

Args:: selection_scenario: The scenario to construct the Selector for. run_on: Which runner to use. Defaults to slurm. job_name: Name to give the construction job when submitting. sbatch_options: Additional options to pass to sbatch. slurm_prepend: Slurm script to prepend to the sbatch base_dir: The base directory to run the Selector in.
Returns:: The construction Run

property name: str: Return the name of the selector.

run(selector_path: Path, instance: str, feature_data: FeatureDataFrame) → list[source]: Run the Selector, returning the prediction schedule upon success.

run_cli(scenario_path: Path, instance_set: InstanceSet | list[Path], feature_data: Path, run_on: Runner = Runner.LOCAL, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, job_name: str = None, dependencies: list[Run] = None, log_dir: Path = None) → Run[source]

Run the Selector CLI and write result to the Scenario PerformanceDataFrame.

Args:: scenario_path: The path to the scenario with the Selector to run. instance_set: The instance set to run the Selector on. feature_data: The instance feature data to use. run_on: Which runner to use. Defaults to slurm. sbatch_options: Additional options to pass to sbatch. slurm_prepend: Slurm script to prepend to the sbatch job_name: Name to give the Slurm job when submitting. dependencies: List of dependencies to add to the job. log_dir: The directory to write logs to.
Returns:: The Run object.

solver

This package provides solver support for Sparkle.

class sparkle.solver.Solver(directory: Path, runsolver_exec: Path = None, deterministic: bool = None, verifier: SolutionVerifier = None)[source]

Class to handle a solver and its directories.

build_cmd(instance: str | list[str], objectives: list[SparkleObjective], seed: int, cutoff_time: int = None, configuration: dict = None, log_dir: Path = None) → list[str][source]

Build the solver call on an instance with a configuration.

Args:: instance: Path to the instance. objectives: List of sparkle objectives. seed: Seed of the solver. cutoff_time: Cutoff time for the solver. configuration: Configuration of the solver. log_dir: Directory path for logs.
Returns:: List of commands and arguments to execute the solver.

static config_str_to_dict(config_str: str) → dict[str, str][source]: Parse a configuration string to a dictionary.

get_configuration_space() → ConfigurationSpace[source]: Get the ConfigurationSpace of the PCS file.

get_pcs_file(port_type: PCSConvention) → Path[source]

Get path of the parameter file of a specific convention.

Args:

port_type: Port type of the parameter file. If None, will return the: file with the shortest name.

Returns:

Path to the parameter file. None if it can not be resolved.

static parse_solver_output(solver_output: str, solver_call: list[str | Path] = None, objectives: list[SparkleObjective] = None, verifier: SolutionVerifier = None) → dict[str, Any][source]

Parse the output of the solver.

Args:: solver_output: The output of the solver run which needs to be parsed solver_call: The solver call used to run the solver objectives: The objectives to apply to the solver output verifier: The verifier to check the solver output
Returns:: Dictionary representing the parsed solver output

property pcs_file: Path: Get path of the parameter file.

port_pcs(port_type: PCSConvention) → None[source]: Port the parameter file to the given port type.

read_pcs_file() → bool[source]: Checks if the pcs file can be read.

run(instances: str | list[str] | InstanceSet | list[InstanceSet], objectives: list[SparkleObjective], seed: int, cutoff_time: int = None, configuration: dict = None, run_on: Runner = Runner.LOCAL, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, log_dir: Path = None) → SlurmRun | list[dict[str, Any]] | dict[str, Any][source]

Run the solver on an instance with a certain configuration.

Args:

instances: The instance(s) to run the solver on, list in case of multi-file.: In case of an instance set, will run on all instances in the set.

objectives: List of sparkle objectives. seed: Seed to run the solver with. Fill with abitrary int in case of

determnistic solver.

cutoff_time: The cutoff time for the solver, measured through RunSolver.: If None, will be executed without RunSolver.

configuration: The solver configuration to use. Can be empty. run_on: Whether to run on slurm or locally. sbatch_options: The sbatch options to use. slurm_prepend: The script to prepend to a slurm script. log_dir: The log directory to use.

Returns:

Solver output dict possibly with runsolver values.

run_performance_dataframe(instances: str | list[str] | InstanceSet, performance_dataframe: PerformanceDataFrame, config_ids: str | list[str] = None, run_ids: list[int] | list[list[int]] = None, cutoff_time: int = None, objective: SparkleObjective = None, train_set: InstanceSet = None, sbatch_options: list[str] = None, slurm_prepend: str | list[str] | Path = None, dependencies: list[SlurmRun] = None, log_dir: Path = None, base_dir: Path = None, job_name: str = None, run_on: Runner = Runner.SLURM) → Run[source]

Run the solver from and place the results in the performance dataframe.

This in practice actually runs Solver.run, but has a little script before/after, to read and write to the performance dataframe.

Args:

instances: The instance(s) to run the solver on. In case of an instance set,: or list, will create a job for all instances in the set/list.

config_ids: The config indices to use in the performance dataframe. performance_dataframe: The performance dataframe to use. run_ids: List of run ids to use. If list of list, a list of runs is given

per instance. Otherwise, all runs are used for each instance.

cutoff_time: The cutoff time for the solver, measured through RunSolver. objective: The objective to use, only relevant when determining the best

configuration.

train_set: The training set to use. If present, will determine the best: configuration of the solver using these instances and run with it on all instances in the instance argument.

sbatch_options: List of slurm batch options to use slurm_prepend: Slurm script to prepend to the sbatch dependencies: List of slurm runs to use as dependencies log_dir: Path where to place output files. Defaults to CWD. base_dir: Path where to place output files. job_name: Name of the job

If None, will generate a name based on Solver and Instances

run_on: On which platform to run the jobs. Default: Slurm.

Returns:

SlurmRun or Local run of the job.

property wrapper: str: Get name of the wrapper file.

property wrapper_extension: str: Get the extension of the wrapper file.

property wrapper_file: Path: Get path of the wrapper file.

solver_wrapper_parsing

sparkle_callable

status

structures

This package provides Sparkle’s wrappers for Pandas DataFrames.

class sparkle.structures.FeatureDataFrame(csv_filepath: Path, instances: list[str] = [], extractor_data: dict[str, list[tuple[str, str]]] = {})[source]

Class to manage feature data CSV files and common operations on them.

add_extractor(extractor: str, extractor_features: list[tuple[str, str]], values: list[list[float]] = None) → None[source]

Add an extractor and its feature names to the dataframe.

Arguments:: extractor: Name of the extractor extractor_features: Tuples of [FeatureGroup, FeatureName] values: Initial values of the Extractor per instance in the dataframe.

Defaults to FeatureDataFrame.missing_value.

add_instances(instance: str | list[str], values: list[float] = None) → None[source]: Add one or more instances to the dataframe.

property extractors: list[str]: Returns all unique extractors in the DataFrame.

property features: list[str]: Return the features in the dataframe.

get_feature_groups(extractor: str | list[str] = None) → list[str][source]

Retrieve the feature groups in the dataframe.

Args:

extractor: Optional. If extractor(s) are given,: yields only feature groups of that extractor.

Returns:

A list of feature groups.

get_instance(instance: str) → list[float][source]: Return the feature vector of an instance.

get_value(instance: str, extractor: str, feature_group: str, feature_name: str) → None[source]: Return a value in the dataframe.

has_missing_value() → bool[source]: Return whether there are missing values in the feature data.

has_missing_vectors() → bool[source]: Returns True if there are any Extractors still to be run on any instance.

impute_missing_values() → None[source]: Imputes all NaN values by taking the average feature value.

property instances: list[str]: Return the instances in the dataframe.

property num_features: int: Return the number of features in the dataframe.

remaining_jobs() → list[tuple[str, str, str]][source]

Determines needed feature computations per instance/extractor/group.

Returns:

list: A list of tuples representing (Extractor, Instance, Feature Group).: that needs to be computed.

remove_extractor(extractor: str) → None[source]: Remove an extractor from the dataframe.

remove_instances(instances: str | list[str]) → None[source]: Remove an instance from the dataframe.

reset_dataframe() → bool[source]: Resets all values to FeatureDataFrame.missing_value.

save_csv(csv_filepath: Path = None) → None[source]

Write a CSV to the given path.

Args:: csv_filepath: String path to the csv file. Defaults to self.csv_filepath.

set_value(instance: str, extractor: str, feature_group: str, feature_name: str, value: float) → None[source]: Set a value in the dataframe.

sort() → None[source]: Sorts the DataFrame by Multi-Index for readability.

class sparkle.structures.PerformanceDataFrame(csv_filepath: Path, solvers: list[str] = None, configurations: dict[str, dict[str, dict]] = None, objectives: list[str | SparkleObjective] = None, instances: list[str] = None, n_runs: int = 1)[source]

Class to manage performance data and common operations on them.

add_configuration(solver: str, configuration_id: str | list[str], configuration: dict[str, Any] | list[dict[str, Any]] = None) → None[source]

Add new configurations for a solver to the dataframe.

If the key already exists, update the value.

Args:: solver: The name of the solver to be added. configuration_id: The name of the configuration to be added. configuration: The configuration to be added.

add_instance(instance_name: str, initial_values: Any | list[Any] = None) → None[source]

Add and instance to the DataFrame.

Args:: instance_name: The name of the instance to be added. initial_values: The values assigned for each index of the new instance.

If list, must match the column dimension (Value, Seed, Configuration).

add_objective(objective_name: str, initial_value: float = None) → None[source]: Add an objective to the DataFrame.

add_runs(num_extra_runs: int, instance_names: list[str] = None, initial_values: Any | list[Any] = None) → None[source]

Add runs to the DataFrame.

Args:

num_extra_runs: The number of runs to be added. instance_names: The instances for which runs are to be added.

By default None, which means runs are added to all instances.

initial_values: The initial value for each objective of each new run.: If a list, needs to have a value for Value, Seed and Configuration.

add_solver(solver_name: str, configurations: list[str, dict] = None, initial_value: float | list[str | float] = None) → None[source]

Add a new solver to the dataframe. Initializes value to None by default.

Args:: solver_name: The name of the solver to be added. configurations: A list of configuration keys for the solver. initial_value: The value assigned for each index of the new solver.

If not None, must match the index dimension (n_obj * n_inst * n_runs).

best_configuration(solver: str, objective: SparkleObjective = None, instances: list[str] = None) → tuple[str, float][source]

Return the best configuration for the given objective over the instances.

Args:: solver: The solver for which we determine the best configuration objective: The objective for which we calculate the best configuration instances: The instances which should be selected for the evaluation
Returns:: The best configuration id and its aggregated performance.

best_instance_performance(objective: str | SparkleObjective = None, instances: list[str] = None, run_id: int = None, exclude_solvers: list[str, str] = None) → Series[source]

Return the best performance for each instance in the portfolio.

Args:

objective: The objective for which we calculate the best performance instances: The instances which should be selected for the evaluation run_id: The run for which we calculate the best performance. If None,

we consider all runs.

exclude_solvers: List of (solver, config_id) to exclude in the calculation.

Returns:

The best performance for each instance in the portfolio.

best_performance(exclude_solvers: list[str, str] = [], instances: list[str] = None, objective: str | SparkleObjective = None) → float[source]

Return the overall best performance of the portfolio.

Args:

exclude_solvers: List of (solver, config_id) to exclude in the calculation.: Defaults to none.
instances: The instances which should be selected for the evaluation: If None, use all instances.

objective: The objective for which we calculate the best performance

Returns:

The aggregated best performance of the portfolio over all instances.

clean_csv() → None[source]: Set all values in Performance Data to None.

clone(csv_filepath: Path = None) → PerformanceDataFrame[source]

Create a copy of this object.

Args:

csv_filepath: The new filepath to use for saving the object to.: If None, will not be saved. Warning: If the original path is used, it could lead to dataloss!

property configuration_ids: list[str]: Return the list of configuration keys.

configuration_performance(solver: str, configuration: str | list[str] = None, objective: str | SparkleObjective = None, instances: list[str] = None, per_instance: bool = False) → tuple[str, float][source]

Return the (best) configuration performance for objective over the instances.

Args:: solver: The solver for which we determine evaluate the configuration configuration: The configuration (id) to evaluate objective: The objective for which we calculate find the best value instances: The instances which should be selected for the evaluation per_instance: Whether to return the performance per instance,

or aggregated.
Returns:: The (best) configuration id and its aggregated performance.

property configurations: dict[str, dict[str, dict]]: Return a dictionary (copy) containing the configurations for each solver.

filter_objective(objective: str | list[str]) → None[source]: Filter the Dataframe to a subset of objectives.

get_configurations(solver_name: str) → list[str][source]: Return the list of configuration keys for a solver.

get_full_configuration(solver: str, configuration_id: str | list[str]) → dict | list[dict][source]: Return the actual configuration associated with the configuration key.

get_instance_num_runs(instance: str) → int[source]: Return the number of runs for an instance.

get_job_list(rerun: bool = False) → list[tuple[str, str]][source]

Return a list of performance computation jobs there are to be done.

Get a list of tuple[instance, solver] to run from the performance data. If rerun is False (default), get only the tuples that don’t have a value, else (True) get all the tuples.

Args:: rerun: Boolean indicating if we want to rerun all jobs
Returns:: A tuple of (solver, config, instance, run) combinations

get_solver_ranking(objective: str | SparkleObjective = None, instances: list[str] = None) → list[tuple[str, dict, float]][source]: Return a list with solvers ranked by average performance.

get_value(solver: str | list[str] = None, instance: str | list[str] = None, configuration: str = None, objective: str = None, run: int = None, solver_fields: list[str] = ['Value']) → float | str | list[Any][source]: Index a value of the DataFrame and return it.

property has_missing_values: bool: Returns True if there are any missing values in the dataframe.

property instances: list[str]: Return the instances as a Pandas Index object.

is_missing(solver: str, instance: str) → int[source]: Checks if a solver/instance is missing values.

marginal_contribution(objective: str | SparkleObjective = None, instances: list[str] = None, sort: bool = False) → list[float][source]

Return the marginal contribution of the solver configuration on the instances.

Args:: objective: The objective for which we calculate the marginal contribution. instances: The instances which should be selected for the evaluation sort: Whether to sort the results afterwards
Returns:: The marginal contribution of each solver (configuration) as: [(solver, config_id, marginal_contribution, portfolio_best_performance_without_solver)]

mean(objective: str = None, solver: str = None, instance: str = None) → float[source]: Return the mean value of a slice of the dataframe.

property multi_objective: bool: Return whether the dataframe represent MO or not.

property num_instances: int: Return the number of instances.

property num_objectives: int: Retrieve the number of objectives in the DataFrame.

property num_runs: int: Return the maximum number of runs of each instance.

property num_solver_configurations: int: Return the number of solver configurations.

property num_solvers: int: Return the number of solvers.

property objective_names: list[str]: Return the objective names as a list of strings.

property objectives: list[SparkleObjective]: Return the objectives as a list of SparkleObjectives.

remove_configuration(solver: str, configuration: str | list[str]) → None[source]: Drop one or more configurations from the Dataframe.

remove_empty_runs() → None[source]: Remove runs that contain no data, except for the first.

remove_instances(instances: str | list[str]) → None[source]: Drop instances from the Dataframe.

remove_objective(objectives: str | list[str]) → None[source]: Remove objective from the Dataframe.

remove_runs(runs: int | list[int], instance_names: list[str] = None) → None[source]

Drop one or more runs from the Dataframe.

Args:

runs: The run indices to be removed. If its an int,: the last n runs are removed. NOTE: If each instance has a different number of runs, the amount of removed runs is not uniform.
instance_names: The instances for which runs are to be removed.: By default None, which means runs are removed from all instances.

remove_solver(solvers: str | list[str]) → None[source]: Drop one or more solvers from the Dataframe.

reset_value(solver: str, instance: str, objective: str = None, run: int = None) → None[source]: Reset a value in the dataframe.

property run_ids: list[int]: Return the run ids as a list of integers.

save_csv(csv_filepath: Path = None) → None[source]

Write a CSV to the given path.

Args:: csv_filepath: String path to the csv file. Defaults to self.csv_filepath.

schedule_performance(schedule: dict[slice(<class 'str'>, dict[slice(<class 'str'>, (<class 'str'>, <class 'str'>, <class 'int'>), None)], None)], target_solver: str | tuple[str, str] = None, objective: str | ~sparkle.types.objective.SparkleObjective = None) → float[source]

Return the performance of a selection schedule on the portfolio.

Args:

schedule: Compute the best performance according to a selection schedule.: A schedule is a dictionary of instances, with a schedule per instance, consisting of a triple of solver, config_id and maximum runtime.

target_solver: If not None, store the found values in this solver of the DF. objective: The objective for which we calculate the best performance

Returns:

The performance of the schedule over the instances in the dictionary.

Setter method to assign a value to the Dataframe.

Allows for setting the same value to multiple indices.

Args:

value: Value(s) to be assigned. If value is a list, first dimension is: the solver field, second dimension is if multiple different values are to be assigned. Must be the same shape as target.
solver: The solver(s) for which the value should be set.: If solver is a list, multiple solvers are set. If None, all solvers are set.
instance: The instance(s) for which the value should be set.: If instance is a list, multiple instances are set. If None, all instances are set.
configuration: The configuration(s) for which the value should be set.: When left None, set for all configurations
objective: The objectives for which the value should be set.: When left None, set for all objectives
run: The run index for which the value should be set.: If left None, set for all runs.
solver_fields: The level to which each value should be assinged.: Defaults to [“Value”].
append_write_csv: For concurrent writing to the PerformanceDataFrame.: If True, the value is directly appended to the CSV file. This will create duplicate entries in the file, but these are combined when loading the file.

property solvers: list[str]: Return the solver present as a list of strings.

verify_indexing(objective: str, run_id: int) → tuple[str, int][source]

Method to check whether data indexing is correct.

Users are allowed to use the Performance Dataframe without the second and fourth dimension (Objective and Run respectively) in the case they only have one objective or only do one run. This method adjusts the indexing for those cases accordingly.

Args:: objective: The given objective name run_id: The given run index
Returns:: A tuple representing the (possibly adjusted) Objective and Run index.

verify_objective(objective: str) → str[source]

Method to check whether the specified objective is valid.

Users are allowed to index the dataframe without specifying all dimensions. However, when dealing with multiple objectives this is not allowed and this is verified here. If we have only one objective this is returned. Otherwise, if an objective is specified by the user this is returned.

Args:: objective: The objective given by the user

verify_run_id(run_id: int) → int[source]

Method to check whether run id is valid.

Similar to verify_objective but here we check the dimensionality of runs.

Args:: run_id: the run as specified by the user.

tools

Init for the tools module.

class sparkle.tools.PCSConverter[source]

Parser class independent file of notation.

static export(configspace: ConfigurationSpace, pcs_format: PCSConvention, file: Path) → str | None[source]

Exports a config space object to a specific PCS convention.

Args:: configspace: ConfigurationSpace, the space to convert pcs_format: PCSConvention, the convention to conver to file: Path, the file to write to. If None, will return string.
Returns:: String in case of no file path given, otherwise None.

static get_convention(file: Path) → PCSConvention[source]: Determines the format of a pcs file.

static parse(file: Path, convention: PCSConvention = None) → ConfigurationSpace[source]: Determines the format of a pcs file and parses into Configuration Space.

static parse_irace(content: list[str] | Path) → ConfigurationSpace[source]: Parses a irace file.

static parse_paramils(content: list[str] | Path) → ConfigurationSpace[source]: Parses a paramils file.

static parse_smac(content: list[str] | Path) → ConfigurationSpace[source]: Parses a SMAC2 file.

static validate(file_path: Path) → bool[source]: Validate a pcs file.

sparkle.tools.RunSolver: alias of RunSolverResolver

class sparkle.tools.SlurmBatch(srcfile: Path)[source]

Class to parse a Slurm batch file and get structured information.

Attributes

sbatch_options: list[str]: The SBATCH options. Ex.: [”–array=-22%250”, “–mem-per-cpu=3000”]
cmd_params: list[str]: The parameters to pass to the command
cmd: str: The command to execute
srun_options: list[str]: A list of arguments to pass to srun. Ex.: [“-n1”, “–nodes=1”]
file: Path: The loaded file Path

sparkle.tools.get_solver_call_params(args_dict: dict, prefix: str = '-', postfix: str = ' ') → list[str][source]

Gather the additional parameters for the solver call.

Args:: args_dict: Dictionary mapping argument names to their currently held values prefix: Prefix of the command line options postfix: Postfix of the command line options
Returns:: A list of parameters for the solver call

sparkle.tools.get_time_pid_random_string() → str[source]

Return a combination of time, Process ID, and random int as string.

Returns:: A random string composed of time, PID and a random positive integer value.

types

This package provides types for Sparkle applications.

class sparkle.types.FeatureGroup(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]: Various feature groups.

class sparkle.types.FeatureSubgroup(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]: Various feature subgroups. Only used for embedding in with feature names.

class sparkle.types.FeatureType(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]

Various feature types.

static with_subgroup(subgroup: FeatureSubgroup, feature: FeatureType) → str[source]: Return a standardised string with a subgroup embedded.

class sparkle.types.SolverStatus(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]

Possible return states for solver runs.

property positive: bool: Return whether the status is positive.

class sparkle.types.SparkleCallable(directory: Path, runsolver_exec: Path = None)[source]

Sparkle Callable class.

build_cmd() → list[str | Path][source]: A method that builds the commandline call string.

run() → None[source]: A method that runs the callable.

property runsolver_exec: Path: Return the path of the runsolver executable.

class sparkle.types.SparkleObjective(name: str, run_aggregator: ~typing.Callable = <function mean>, instance_aggregator: ~typing.Callable = <function mean>, solver_aggregator: ~typing.Callable = None, minimise: bool = True, post_process: ~typing.Callable = None, use_time: ~sparkle.types.objective.UseTime = UseTime.NO, metric: bool = False)[source]

Objective for Sparkle specified by user.

property stem: str: Return the stem of the objective name.

property time: bool: Return whether the objective is time based.

class sparkle.types.UseTime(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]: Enum describing what type of time to use.

sparkle.types._check_class(candidate: Callable) → bool[source]: Verify whether a loaded class is a valid objective class.

sparkle.types.resolve_objective(objective_name: str) → SparkleObjective[source]

Try to resolve the objective class by (case-sensitive) name.

convention: objective_name(variable-k)?(:[min|max])?(:[metric|objective])? Here, min|max refers to the minimisation or maximisation of the objective and metric|objective refers to whether the objective should be optimized or just recorded.

Order of resolving:: class_name of user defined SparkleObjectives class_name of sparkle defined SparkleObjectives default SparkleObjective with minimization unless specified as max
Args:: objective_name: The name of the objective class. Can include parameter value k.
Returns:: Instance of the Objective class or None if not found.

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