514 lines
16 KiB
Python
514 lines
16 KiB
Python
from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Any,
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Literal,
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cast,
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)
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import numpy as np
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from pandas._libs import index as libindex
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from pandas.util._decorators import (
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cache_readonly,
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doc,
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)
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from pandas.core.dtypes.common import is_scalar
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from pandas.core.dtypes.concat import concat_compat
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from pandas.core.dtypes.dtypes import CategoricalDtype
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from pandas.core.dtypes.missing import (
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is_valid_na_for_dtype,
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isna,
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)
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from pandas.core.arrays.categorical import (
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Categorical,
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contains,
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)
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from pandas.core.construction import extract_array
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from pandas.core.indexes.base import (
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Index,
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maybe_extract_name,
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)
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from pandas.core.indexes.extension import (
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NDArrayBackedExtensionIndex,
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inherit_names,
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)
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if TYPE_CHECKING:
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from collections.abc import Hashable
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from pandas._typing import (
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Dtype,
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DtypeObj,
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Self,
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npt,
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)
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@inherit_names(
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[
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"argsort",
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"tolist",
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"codes",
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"categories",
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"ordered",
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"_reverse_indexer",
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"searchsorted",
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"min",
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"max",
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],
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Categorical,
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)
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@inherit_names(
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[
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"rename_categories",
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"reorder_categories",
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"add_categories",
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"remove_categories",
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"remove_unused_categories",
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"set_categories",
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"as_ordered",
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"as_unordered",
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],
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Categorical,
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wrap=True,
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)
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class CategoricalIndex(NDArrayBackedExtensionIndex):
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"""
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Index based on an underlying :class:`Categorical`.
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CategoricalIndex, like Categorical, can only take on a limited,
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and usually fixed, number of possible values (`categories`). Also,
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like Categorical, it might have an order, but numerical operations
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(additions, divisions, ...) are not possible.
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Parameters
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----------
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data : array-like (1-dimensional)
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The values of the categorical. If `categories` are given, values not in
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`categories` will be replaced with NaN.
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categories : index-like, optional
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The categories for the categorical. Items need to be unique.
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If the categories are not given here (and also not in `dtype`), they
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will be inferred from the `data`.
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ordered : bool, optional
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Whether or not this categorical is treated as an ordered
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categorical. If not given here or in `dtype`, the resulting
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categorical will be unordered.
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dtype : CategoricalDtype or "category", optional
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If :class:`CategoricalDtype`, cannot be used together with
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`categories` or `ordered`.
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copy : bool, default False
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Make a copy of input ndarray.
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name : object, optional
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Name to be stored in the index.
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Attributes
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----------
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codes
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categories
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ordered
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Methods
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-------
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rename_categories
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reorder_categories
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add_categories
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remove_categories
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remove_unused_categories
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set_categories
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as_ordered
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as_unordered
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map
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Raises
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------
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ValueError
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If the categories do not validate.
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TypeError
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If an explicit ``ordered=True`` is given but no `categories` and the
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`values` are not sortable.
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See Also
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--------
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Index : The base pandas Index type.
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Categorical : A categorical array.
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CategoricalDtype : Type for categorical data.
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Notes
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-----
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See the `user guide
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<https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#categoricalindex>`__
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for more.
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Examples
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--------
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>>> pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"])
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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``CategoricalIndex`` can also be instantiated from a ``Categorical``:
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>>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
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>>> pd.CategoricalIndex(c)
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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Ordered ``CategoricalIndex`` can have a min and max value.
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>>> ci = pd.CategoricalIndex(
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... ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"]
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... )
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>>> ci
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['c', 'b', 'a'], ordered=True, dtype='category')
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>>> ci.min()
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'c'
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"""
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_typ = "categoricalindex"
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_data_cls = Categorical
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@property
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def _can_hold_strings(self):
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return self.categories._can_hold_strings
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@cache_readonly
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def _should_fallback_to_positional(self) -> bool:
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return self.categories._should_fallback_to_positional
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codes: np.ndarray
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categories: Index
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ordered: bool | None
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_data: Categorical
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_values: Categorical
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@property
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def _engine_type(self) -> type[libindex.IndexEngine]:
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# self.codes can have dtype int8, int16, int32 or int64, so we need
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# to return the corresponding engine type (libindex.Int8Engine, etc.).
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return {
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np.int8: libindex.Int8Engine,
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np.int16: libindex.Int16Engine,
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np.int32: libindex.Int32Engine,
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np.int64: libindex.Int64Engine,
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}[self.codes.dtype.type]
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# --------------------------------------------------------------------
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# Constructors
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def __new__(
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cls,
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data=None,
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categories=None,
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ordered=None,
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dtype: Dtype | None = None,
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copy: bool = False,
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name: Hashable | None = None,
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) -> Self:
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name = maybe_extract_name(name, data, cls)
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if is_scalar(data):
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# GH#38944 include None here, which pre-2.0 subbed in []
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cls._raise_scalar_data_error(data)
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data = Categorical(
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data, categories=categories, ordered=ordered, dtype=dtype, copy=copy
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)
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return cls._simple_new(data, name=name)
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# --------------------------------------------------------------------
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def _is_dtype_compat(self, other: Index) -> Categorical:
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"""
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*this is an internal non-public method*
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provide a comparison between the dtype of self and other (coercing if
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needed)
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Parameters
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----------
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other : Index
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Returns
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-------
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Categorical
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Raises
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------
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TypeError if the dtypes are not compatible
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"""
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if isinstance(other.dtype, CategoricalDtype):
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cat = extract_array(other)
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cat = cast(Categorical, cat)
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if not cat._categories_match_up_to_permutation(self._values):
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raise TypeError(
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"categories must match existing categories when appending"
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)
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elif other._is_multi:
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# preempt raising NotImplementedError in isna call
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raise TypeError("MultiIndex is not dtype-compatible with CategoricalIndex")
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else:
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values = other
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cat = Categorical(other, dtype=self.dtype)
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other = CategoricalIndex(cat)
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if not other.isin(values).all():
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raise TypeError(
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"cannot append a non-category item to a CategoricalIndex"
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)
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cat = other._values
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if not ((cat == values) | (isna(cat) & isna(values))).all():
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# GH#37667 see test_equals_non_category
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raise TypeError(
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"categories must match existing categories when appending"
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)
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return cat
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def equals(self, other: object) -> bool:
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"""
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Determine if two CategoricalIndex objects contain the same elements.
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Returns
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-------
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bool
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``True`` if two :class:`pandas.CategoricalIndex` objects have equal
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elements, ``False`` otherwise.
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Examples
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--------
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>>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'])
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>>> ci2 = pd.CategoricalIndex(pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c']))
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>>> ci.equals(ci2)
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True
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The order of elements matters.
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>>> ci3 = pd.CategoricalIndex(['c', 'b', 'a', 'a', 'b', 'c'])
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>>> ci.equals(ci3)
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False
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The orderedness also matters.
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>>> ci4 = ci.as_ordered()
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>>> ci.equals(ci4)
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False
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The categories matter, but the order of the categories matters only when
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``ordered=True``.
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>>> ci5 = ci.set_categories(['a', 'b', 'c', 'd'])
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>>> ci.equals(ci5)
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False
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>>> ci6 = ci.set_categories(['b', 'c', 'a'])
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>>> ci.equals(ci6)
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True
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>>> ci_ordered = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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... ordered=True)
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>>> ci2_ordered = ci_ordered.set_categories(['b', 'c', 'a'])
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>>> ci_ordered.equals(ci2_ordered)
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False
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"""
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if self.is_(other):
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return True
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if not isinstance(other, Index):
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return False
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try:
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other = self._is_dtype_compat(other)
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except (TypeError, ValueError):
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return False
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return self._data.equals(other)
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# --------------------------------------------------------------------
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# Rendering Methods
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@property
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def _formatter_func(self):
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return self.categories._formatter_func
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def _format_attrs(self):
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"""
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Return a list of tuples of the (attr,formatted_value)
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"""
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attrs: list[tuple[str, str | int | bool | None]]
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attrs = [
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(
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"categories",
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f"[{', '.join(self._data._repr_categories())}]",
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),
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("ordered", self.ordered),
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]
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extra = super()._format_attrs()
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return attrs + extra
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# --------------------------------------------------------------------
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@property
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def inferred_type(self) -> str:
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return "categorical"
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@doc(Index.__contains__)
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def __contains__(self, key: Any) -> bool:
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# if key is a NaN, check if any NaN is in self.
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if is_valid_na_for_dtype(key, self.categories.dtype):
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return self.hasnans
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return contains(self, key, container=self._engine)
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def reindex(
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self, target, method=None, level=None, limit: int | None = None, tolerance=None
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) -> tuple[Index, npt.NDArray[np.intp] | None]:
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"""
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Create index with target's values (move/add/delete values as necessary)
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Returns
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-------
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new_index : pd.Index
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Resulting index
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indexer : np.ndarray[np.intp] or None
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Indices of output values in original index
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"""
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if method is not None:
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raise NotImplementedError(
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"argument method is not implemented for CategoricalIndex.reindex"
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)
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if level is not None:
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raise NotImplementedError(
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"argument level is not implemented for CategoricalIndex.reindex"
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)
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if limit is not None:
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raise NotImplementedError(
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"argument limit is not implemented for CategoricalIndex.reindex"
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)
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return super().reindex(target)
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# --------------------------------------------------------------------
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# Indexing Methods
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def _maybe_cast_indexer(self, key) -> int:
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# GH#41933: we have to do this instead of self._data._validate_scalar
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# because this will correctly get partial-indexing on Interval categories
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try:
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return self._data._unbox_scalar(key)
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except KeyError:
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if is_valid_na_for_dtype(key, self.categories.dtype):
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return -1
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raise
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def _maybe_cast_listlike_indexer(self, values) -> CategoricalIndex:
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if isinstance(values, CategoricalIndex):
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values = values._data
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if isinstance(values, Categorical):
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# Indexing on codes is more efficient if categories are the same,
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# so we can apply some optimizations based on the degree of
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# dtype-matching.
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cat = self._data._encode_with_my_categories(values)
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codes = cat._codes
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else:
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codes = self.categories.get_indexer(values)
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codes = codes.astype(self.codes.dtype, copy=False)
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cat = self._data._from_backing_data(codes)
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return type(self)._simple_new(cat)
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# --------------------------------------------------------------------
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def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
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return self.categories._is_comparable_dtype(dtype)
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def map(self, mapper, na_action: Literal["ignore"] | None = None):
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"""
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Map values using input an input mapping or function.
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Maps the values (their categories, not the codes) of the index to new
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categories. If the mapping correspondence is one-to-one the result is a
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:class:`~pandas.CategoricalIndex` which has the same order property as
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the original, otherwise an :class:`~pandas.Index` is returned.
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If a `dict` or :class:`~pandas.Series` is used any unmapped category is
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mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
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will be returned.
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Parameters
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----------
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mapper : function, dict, or Series
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Mapping correspondence.
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Returns
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-------
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pandas.CategoricalIndex or pandas.Index
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Mapped index.
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See Also
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--------
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Index.map : Apply a mapping correspondence on an
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:class:`~pandas.Index`.
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Series.map : Apply a mapping correspondence on a
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:class:`~pandas.Series`.
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Series.apply : Apply more complex functions on a
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:class:`~pandas.Series`.
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Examples
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--------
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>>> idx = pd.CategoricalIndex(['a', 'b', 'c'])
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>>> idx
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CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
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ordered=False, dtype='category')
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>>> idx.map(lambda x: x.upper())
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CategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'],
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ordered=False, dtype='category')
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>>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'})
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CategoricalIndex(['first', 'second', 'third'], categories=['first',
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'second', 'third'], ordered=False, dtype='category')
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If the mapping is one-to-one the ordering of the categories is
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preserved:
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>>> idx = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True)
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>>> idx
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CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
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ordered=True, dtype='category')
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>>> idx.map({'a': 3, 'b': 2, 'c': 1})
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CategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True,
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dtype='category')
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If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
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>>> idx.map({'a': 'first', 'b': 'second', 'c': 'first'})
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Index(['first', 'second', 'first'], dtype='object')
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If a `dict` is used, all unmapped categories are mapped to `NaN` and
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the result is an :class:`~pandas.Index`:
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>>> idx.map({'a': 'first', 'b': 'second'})
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Index(['first', 'second', nan], dtype='object')
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"""
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mapped = self._values.map(mapper, na_action=na_action)
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return Index(mapped, name=self.name)
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def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
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# if calling index is category, don't check dtype of others
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try:
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cat = Categorical._concat_same_type(
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[self._is_dtype_compat(c) for c in to_concat]
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)
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except TypeError:
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# not all to_concat elements are among our categories (or NA)
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res = concat_compat([x._values for x in to_concat])
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return Index(res, name=name)
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else:
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return type(self)._simple_new(cat, name=name)
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