#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import warnings
from typing import Any, Callable, List, Optional, Union, cast, no_type_check
import pandas as pd
from pandas.api.types import is_hashable, CategoricalDtype # type: ignore[attr-defined]
from pyspark import pandas as ps
from pyspark.pandas.indexes.base import Index
from pyspark.pandas.internal import InternalField
from pyspark.pandas.series import Series
from pyspark.sql.types import StructField
[docs]class CategoricalIndex(Index):
"""
Index based on an underlying `Categorical`.
CategoricalIndex can only take on a limited,
and usually fixed, number of possible values (`categories`). Also,
it might have an order, but numerical operations
(additions, divisions, ...) are not possible.
Parameters
----------
data : array-like (1-dimensional)
The values of the categorical. If `categories` are given, values not in
`categories` will be replaced with NaN.
categories : index-like, optional
The categories for the categorical. Items need to be unique.
If the categories are not given here (and also not in `dtype`), they
will be inferred from the `data`.
ordered : bool, optional
Whether or not this categorical is treated as an ordered
categorical. If not given here or in `dtype`, the resulting
categorical will be unordered.
dtype : CategoricalDtype or "category", optional
If :class:`CategoricalDtype`, cannot be used together with
`categories` or `ordered`.
copy : bool, default False
Make a copy of input ndarray.
name : object, optional
Name to be stored in the index.
See Also
--------
Index : The base pandas-on-Spark Index type.
Examples
--------
>>> ps.CategoricalIndex(["a", "b", "c", "a", "b", "c"]) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
``CategoricalIndex`` can also be instantiated from a ``Categorical``:
>>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
>>> ps.CategoricalIndex(c) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
Ordered ``CategoricalIndex`` can have a min and max value.
>>> ci = ps.CategoricalIndex(
... ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"]
... )
>>> ci # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
categories=['c', 'b', 'a'], ordered=True, dtype='category')
From a Series:
>>> s = ps.Series(["a", "b", "c", "a", "b", "c"], index=[10, 20, 30, 40, 50, 60])
>>> ps.CategoricalIndex(s) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
From an Index:
>>> idx = ps.Index(["a", "b", "c", "a", "b", "c"])
>>> ps.CategoricalIndex(idx) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
"""
@no_type_check
def __new__(cls, data=None, categories=None, ordered=None, dtype=None, copy=False, name=None):
if not is_hashable(name):
raise TypeError("Index.name must be a hashable type")
if isinstance(data, (Series, Index)):
if dtype is None:
dtype = "category"
return Index(data, dtype=dtype, copy=copy, name=name)
return ps.from_pandas(
pd.CategoricalIndex(
data=data, categories=categories, ordered=ordered, dtype=dtype, name=name
)
)
@property
def dtype(self) -> CategoricalDtype:
return cast(CategoricalDtype, super().dtype)
@property
def codes(self) -> Index:
"""
The category codes of this categorical.
Codes are an Index of integers which are the positions of the actual
values in the categories Index.
There is no setter, use the other categorical methods and the normal item
setter to change values in the categorical.
Returns
-------
Index
A non-writable view of the `codes` Index.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.codes # doctest: +SKIP
Int64Index([0, 1, 1, 2, 2, 2], dtype='int64')
"""
return self._with_new_scol(
self.spark.column,
field=InternalField.from_struct_field(
StructField(
name=self._internal.index_spark_column_names[0],
dataType=self.spark.data_type,
nullable=self.spark.nullable,
)
),
).rename(None)
@property
def categories(self) -> pd.Index:
"""
The categories of this categorical.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.categories
Index(['a', 'b', 'c'], dtype='object')
"""
return self.dtype.categories
@categories.setter
def categories(self, categories: Union[pd.Index, List]) -> None:
dtype = CategoricalDtype(categories, ordered=self.ordered)
if len(self.categories) != len(dtype.categories):
raise ValueError(
"new categories need to have the same number of items as the old categories!"
)
internal = self._psdf._internal.copy(
index_fields=[self._internal.index_fields[0].copy(dtype=dtype)]
)
self._psdf._update_internal_frame(internal)
@property
def ordered(self) -> bool:
"""
Whether the categories have an ordered relationship.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.ordered
False
"""
return self.dtype.ordered
[docs] def add_categories(
self, new_categories: Union[pd.Index, Any, List], inplace: bool = False
) -> Optional["CategoricalIndex"]:
"""
Add new categories.
`new_categories` will be included at the last/highest place in the
categories and will be unused directly after this call.
Parameters
----------
new_categories : category or list-like of category
The new categories to be included.
inplace : bool, default False
Whether or not to add the categories inplace or return a copy of
this categorical with added categories.
.. deprecated:: 3.2.0
Returns
-------
CategoricalIndex or None
Categorical with new categories added or None if ``inplace=True``.
Raises
------
ValueError
If the new categories include old categories or do not validate as
categories
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.add_categories('x') # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c', 'x'], ordered=False, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(
self.to_series().cat.add_categories(new_categories=new_categories)
).rename(self.name)
[docs] def as_ordered(self, inplace: bool = False) -> Optional["CategoricalIndex"]:
"""
Set the Categorical to be ordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to True.
Returns
-------
CategoricalIndex or None
Ordered Categorical or None if ``inplace=True``.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.as_ordered() # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=True, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(self.to_series().cat.as_ordered()).rename(self.name)
[docs] def as_unordered(self, inplace: bool = False) -> Optional["CategoricalIndex"]:
"""
Set the Categorical to be unordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to False.
Returns
-------
CategoricalIndex or None
Unordered Categorical or None if ``inplace=True``.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc")).as_ordered()
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=True, dtype='category')
>>> idx.as_unordered() # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(self.to_series().cat.as_unordered()).rename(self.name)
[docs] def remove_categories(
self, removals: Union[pd.Index, Any, List], inplace: bool = False
) -> Optional["CategoricalIndex"]:
"""
Remove the specified categories.
`removals` must be included in the old categories. Values which were in
the removed categories will be set to NaN
Parameters
----------
removals : category or list of categories
The categories which should be removed.
inplace : bool, default False
Whether or not to remove the categories inplace or return a copy of
this categorical with removed categories.
.. deprecated:: 3.2.0
Returns
-------
CategoricalIndex or None
Categorical with removed categories or None if ``inplace=True``.
Raises
------
ValueError
If the removals are not contained in the categories
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.remove_categories('b') # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', nan, nan, 'c', 'c', 'c'],
categories=['a', 'c'], ordered=False, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(self.to_series().cat.remove_categories(removals)).rename(self.name)
[docs] def remove_unused_categories(self, inplace: bool = False) -> Optional["CategoricalIndex"]:
"""
Remove categories which are not used.
Parameters
----------
inplace : bool, default False
Whether or not to drop unused categories inplace or return a copy of
this categorical with unused categories dropped.
.. deprecated:: 3.2.0
Returns
-------
cat : CategoricalIndex or None
Categorical with unused categories dropped or None if ``inplace=True``.
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"), categories=['a', 'b', 'c', 'd'])
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c', 'd'], ordered=False, dtype='category')
>>> idx.remove_unused_categories() # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(self.to_series().cat.remove_unused_categories()).rename(self.name)
[docs] def rename_categories(
self, new_categories: Union[list, dict, Callable], inplace: bool = False
) -> Optional["CategoricalIndex"]:
"""
Rename categories.
Parameters
----------
new_categories : list-like, dict-like or callable
New categories which will replace old categories.
* list-like: all items must be unique and the number of items in
the new categories must match the existing number of categories.
* dict-like: specifies a mapping from
old categories to new. Categories not contained in the mapping
are passed through and extra categories in the mapping are
ignored.
* callable : a callable that is called on all items in the old
categories and whose return values comprise the new categories.
inplace : bool, default False
Whether or not to rename the categories inplace or return a copy of
this categorical with renamed categories.
.. deprecated:: 3.2.0
Returns
-------
cat : CategoricalIndex or None
Categorical with removed categories or None if ``inplace=True``.
Raises
------
ValueError
If new categories are list-like and do not have the same number of
items than the current categories or do not validate as categories
See Also
--------
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> idx = ps.CategoricalIndex(["a", "a", "b"])
>>> idx.rename_categories([0, 1])
CategoricalIndex([0, 0, 1], categories=[0, 1], ordered=False, dtype='category')
For dict-like ``new_categories``, extra keys are ignored and
categories not in the dictionary are passed through
>>> idx.rename_categories({'a': 'A', 'c': 'C'})
CategoricalIndex(['A', 'A', 'b'], categories=['A', 'b'], ordered=False, dtype='category')
You may also provide a callable to create the new categories
>>> idx.rename_categories(lambda x: x.upper())
CategoricalIndex(['A', 'A', 'B'], categories=['A', 'B'], ordered=False, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(self.to_series().cat.rename_categories(new_categories)).rename(
self.name
)
[docs] def reorder_categories(
self,
new_categories: Union[pd.Index, Any, List],
ordered: Optional[bool] = None,
inplace: bool = False,
) -> Optional["CategoricalIndex"]:
"""
Reorder categories as specified in new_categories.
`new_categories` needs to include all old categories and no new category
items.
Parameters
----------
new_categories : Index-like
The categories in new order.
ordered : bool, optional
Whether or not the categorical is treated as an ordered categorical.
If not given, do not change the ordered information.
inplace : bool, default False
Whether or not to reorder the categories inplace or return a copy of
this categorical with reordered categories.
.. deprecated:: 3.2.0
Returns
-------
cat : CategoricalIndex or None
Categorical with removed categories or None if ``inplace=True``.
Raises
------
ValueError
If the new categories do not contain all old category items or any
new ones
See Also
--------
rename_categories : Rename categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.reorder_categories(['c', 'b', 'a']) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['c', 'b', 'a'], ordered=False, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(
self.to_series().cat.reorder_categories(new_categories=new_categories, ordered=ordered)
).rename(self.name)
[docs] def set_categories(
self,
new_categories: Union[pd.Index, List],
ordered: Optional[bool] = None,
rename: bool = False,
inplace: bool = False,
) -> Optional["CategoricalIndex"]:
"""
Set the categories to the specified new_categories.
`new_categories` can include new categories (which will result in
unused categories) or remove old categories (which results in values
set to NaN). If `rename==True`, the categories will simply be renamed
(less or more items than in old categories will result in values set to
NaN or in unused categories respectively).
This method can be used to perform more than one action of adding,
removing, and reordering simultaneously and is therefore faster than
performing the individual steps via the more specialised methods.
On the other hand this methods does not do checks (e.g., whether the
old categories are included in the new categories on a reorder), which
can result in surprising changes, for example when using special string
dtypes, which does not consider a S1 string equal to a single char
python string.
Parameters
----------
new_categories : Index-like
The categories in new order.
ordered : bool, default False
Whether or not the categorical is treated as an ordered categorical.
If not given, do not change the ordered information.
rename : bool, default False
Whether or not the new_categories should be considered as a rename
of the old categories or as reordered categories.
inplace : bool, default False
Whether or not to reorder the categories in-place or return a copy
of this categorical with reordered categories.
.. deprecated:: 3.2.0
Returns
-------
CategoricalIndex with reordered categories or None if inplace.
Raises
------
ValueError
If new_categories does not validate as categories
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
Examples
--------
>>> idx = ps.CategoricalIndex(list("abbccc"))
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.set_categories(['b', 'c']) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex([nan, 'b', 'b', 'c', 'c', 'c'],
categories=['b', 'c'], ordered=False, dtype='category')
>>> idx.set_categories([1, 2, 3], rename=True)
CategoricalIndex([1, 2, 2, 3, 3, 3], categories=[1, 2, 3], ordered=False, dtype='category')
>>> idx.set_categories([1, 2, 3], rename=True, ordered=True)
CategoricalIndex([1, 2, 2, 3, 3, 3], categories=[1, 2, 3], ordered=True, dtype='category')
"""
if inplace:
warnings.warn(
"Argument `inplace` will be removed in 4.0.0.",
FutureWarning,
)
raise ValueError("cannot use inplace with CategoricalIndex")
return CategoricalIndex(
self.to_series().cat.set_categories(new_categories, ordered=ordered, rename=rename)
).rename(self.name)
[docs] def map( # type: ignore[override]
self, mapper: Union[dict, Callable[[Any], Any], pd.Series]
) -> "Index":
"""
Map values using input correspondence (a dict, Series, or function).
Maps the values (their categories, not the codes) of the index to new
categories. If the mapping correspondence is one-to-one the result is a
`CategoricalIndex` which has the same order property as the original,
otherwise an `Index` is returned.
If a `dict` or `Series` is used any unmapped category is mapped to missing values.
Note that if this happens an `Index` will be returned.
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
Returns
-------
CategoricalIndex or Index
Mapped index.
See Also
--------
Index.map : Apply a mapping correspondence on an `Index`.
Series.map : Apply a mapping correspondence on a `Series`
Series.apply : Apply more complex functions on a `Series`
Examples
--------
>>> idx = ps.CategoricalIndex(['a', 'b', 'c'])
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'c'],
categories=['a', 'b', 'c'], ordered=False, dtype='category')
>>> idx.map(lambda x: x.upper()) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['A', 'B', 'C'],
categories=['A', 'B', 'C'], ordered=False, dtype='category')
>>> pser = pd.Series([1, 2, 3], index=pd.CategoricalIndex(['a', 'b', 'c'], ordered=True))
>>> idx.map(pser) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex([1, 2, 3],
categories=[1, 2, 3], ordered=False, dtype='category')
>>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'}) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['first', 'second', 'third'],
categories=['first', 'second', 'third'], ordered=False, dtype='category')
If the mapping is one-to-one the ordering of the categories is preserved:
>>> idx = ps.CategoricalIndex(['a', 'b', 'c'], ordered=True)
>>> idx # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex(['a', 'b', 'c'],
categories=['a', 'b', 'c'], ordered=True, dtype='category')
>>> idx.map({'a': 3, 'b': 2, 'c': 1}) # doctest: +NORMALIZE_WHITESPACE
CategoricalIndex([3, 2, 1],
categories=[3, 2, 1], ordered=True, dtype='category')
If the mapping is not one-to-one an `Index` is returned:
>>> idx.map({'a': 'first', 'b': 'second', 'c': 'first'})
Index(['first', 'second', 'first'], dtype='object')
If a `dict` is used, all unmapped categories are mapped to None and
the result is an `Index`:
>>> idx.map({'a': 'first', 'b': 'second'})
Index(['first', 'second', None], dtype='object')
"""
return super().map(mapper)
@no_type_check
def all(self, *args, **kwargs) -> None:
raise TypeError("Cannot perform 'all' with this index type: %s" % type(self).__name__)
def _test() -> None:
import os
import doctest
import sys
from pyspark.sql import SparkSession
import pyspark.pandas.indexes.category
os.chdir(os.environ["SPARK_HOME"])
globs = pyspark.pandas.indexes.category.__dict__.copy()
globs["ps"] = pyspark.pandas
spark = (
SparkSession.builder.master("local[4]")
.appName("pyspark.pandas.indexes.category tests")
.getOrCreate()
)
(failure_count, test_count) = doctest.testmod(
pyspark.pandas.indexes.category,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()