from __future__ import annotations from typing import ( Any, Hashable, ) import warnings import numpy as np from pandas._libs import index as libindex from pandas._typing import ( Dtype, DtypeObj, npt, ) from pandas.util._decorators import ( cache_readonly, doc, ) from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.common import ( is_categorical_dtype, is_scalar, pandas_dtype, ) from pandas.core.dtypes.missing import ( is_valid_na_for_dtype, isna, notna, ) from pandas.core.arrays.categorical import ( Categorical, contains, ) from pandas.core.construction import extract_array import pandas.core.indexes.base as ibase from pandas.core.indexes.base import ( Index, maybe_extract_name, ) from pandas.core.indexes.extension import ( NDArrayBackedExtensionIndex, inherit_names, ) from pandas.io.formats.printing import pprint_thing _index_doc_kwargs: dict[str, str] = dict(ibase._index_doc_kwargs) _index_doc_kwargs.update({"target_klass": "CategoricalIndex"}) @inherit_names( [ "argsort", "tolist", "codes", "categories", "ordered", "_reverse_indexer", "searchsorted", "is_dtype_equal", "min", "max", ], Categorical, ) @inherit_names( [ "rename_categories", "reorder_categories", "add_categories", "remove_categories", "remove_unused_categories", "set_categories", "as_ordered", "as_unordered", ], Categorical, wrap=True, ) class CategoricalIndex(NDArrayBackedExtensionIndex): """ Index based on an underlying :class:`Categorical`. CategoricalIndex, like Categorical, can only take on a limited, and usually fixed, number of possible values (`categories`). Also, like Categorical, 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. Attributes ---------- codes categories ordered Methods ------- rename_categories reorder_categories add_categories remove_categories remove_unused_categories set_categories as_ordered as_unordered map Raises ------ ValueError If the categories do not validate. TypeError If an explicit ``ordered=True`` is given but no `categories` and the `values` are not sortable. See Also -------- Index : The base pandas Index type. Categorical : A categorical array. CategoricalDtype : Type for categorical data. Notes ----- See the `user guide `__ for more. Examples -------- >>> pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"]) 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"]) >>> pd.CategoricalIndex(c) 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 = pd.CategoricalIndex( ... ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"] ... ) >>> ci CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'], categories=['c', 'b', 'a'], ordered=True, dtype='category') >>> ci.min() 'c' """ _typ = "categoricalindex" _data_cls = Categorical @property def _can_hold_strings(self): return self.categories._can_hold_strings @cache_readonly def _should_fallback_to_positional(self) -> bool: return self.categories._should_fallback_to_positional codes: np.ndarray categories: Index ordered: bool | None _data: Categorical _values: Categorical @property def _engine_type(self) -> type[libindex.IndexEngine]: # self.codes can have dtype int8, int16, int32 or int64, so we need # to return the corresponding engine type (libindex.Int8Engine, etc.). return { np.int8: libindex.Int8Engine, np.int16: libindex.Int16Engine, np.int32: libindex.Int32Engine, np.int64: libindex.Int64Engine, }[self.codes.dtype.type] # -------------------------------------------------------------------- # Constructors def __new__( cls, data=None, categories=None, ordered=None, dtype: Dtype | None = None, copy: bool = False, name: Hashable = None, ) -> CategoricalIndex: name = maybe_extract_name(name, data, cls) if data is None: # GH#38944 warnings.warn( "Constructing a CategoricalIndex without passing data is " "deprecated and will raise in a future version. " "Use CategoricalIndex([], ...) instead.", FutureWarning, stacklevel=find_stack_level(), ) data = [] if is_scalar(data): raise cls._scalar_data_error(data) data = Categorical( data, categories=categories, ordered=ordered, dtype=dtype, copy=copy ) return cls._simple_new(data, name=name) # -------------------------------------------------------------------- def _is_dtype_compat(self, other) -> Categorical: """ *this is an internal non-public method* provide a comparison between the dtype of self and other (coercing if needed) Parameters ---------- other : Index Returns ------- Categorical Raises ------ TypeError if the dtypes are not compatible """ if is_categorical_dtype(other): other = extract_array(other) if not other._categories_match_up_to_permutation(self): raise TypeError( "categories must match existing categories when appending" ) elif other._is_multi: # preempt raising NotImplementedError in isna call raise TypeError("MultiIndex is not dtype-compatible with CategoricalIndex") else: values = other cat = Categorical(other, dtype=self.dtype) other = CategoricalIndex(cat) if not other.isin(values).all(): raise TypeError( "cannot append a non-category item to a CategoricalIndex" ) other = other._values if not ((other == values) | (isna(other) & isna(values))).all(): # GH#37667 see test_equals_non_category raise TypeError( "categories must match existing categories when appending" ) return other @doc(Index.astype) def astype(self, dtype: Dtype, copy: bool = True) -> Index: from pandas.core.api import NumericIndex dtype = pandas_dtype(dtype) categories = self.categories # the super method always returns Int64Index, UInt64Index and Float64Index # but if the categories are a NumericIndex with dtype float32, we want to # return an index with the same dtype as self.categories. if categories._is_backward_compat_public_numeric_index: assert isinstance(categories, NumericIndex) # mypy complaint fix try: categories._validate_dtype(dtype) except ValueError: pass else: new_values = self._data.astype(dtype, copy=copy) # pass copy=False because any copying has been done in the # _data.astype call above return categories._constructor(new_values, name=self.name, copy=False) return super().astype(dtype, copy=copy) def equals(self, other: object) -> bool: """ Determine if two CategoricalIndex objects contain the same elements. Returns ------- bool If two CategoricalIndex objects have equal elements True, otherwise False. """ if self.is_(other): return True if not isinstance(other, Index): return False try: other = self._is_dtype_compat(other) except (TypeError, ValueError): return False return self._data.equals(other) # -------------------------------------------------------------------- # Rendering Methods @property def _formatter_func(self): return self.categories._formatter_func def _format_attrs(self): """ Return a list of tuples of the (attr,formatted_value) """ attrs: list[tuple[str, str | int | bool | None]] attrs = [ ( "categories", "[" + ", ".join(self._data._repr_categories()) + "]", ), ("ordered", self.ordered), ] extra = super()._format_attrs() return attrs + extra def _format_with_header(self, header: list[str], na_rep: str) -> list[str]: result = [ pprint_thing(x, escape_chars=("\t", "\r", "\n")) if notna(x) else na_rep for x in self._values ] return header + result # -------------------------------------------------------------------- @property def inferred_type(self) -> str: return "categorical" @doc(Index.__contains__) def __contains__(self, key: Any) -> bool: # if key is a NaN, check if any NaN is in self. if is_valid_na_for_dtype(key, self.categories.dtype): return self.hasnans return contains(self, key, container=self._engine) # TODO(2.0): remove reindex once non-unique deprecation is enforced def reindex( self, target, method=None, level=None, limit=None, tolerance=None ) -> tuple[Index, npt.NDArray[np.intp] | None]: """ Create index with target's values (move/add/delete values as necessary) Returns ------- new_index : pd.Index Resulting index indexer : np.ndarray[np.intp] or None Indices of output values in original index """ if method is not None: raise NotImplementedError( "argument method is not implemented for CategoricalIndex.reindex" ) if level is not None: raise NotImplementedError( "argument level is not implemented for CategoricalIndex.reindex" ) if limit is not None: raise NotImplementedError( "argument limit is not implemented for CategoricalIndex.reindex" ) target = ibase.ensure_index(target) if self.equals(target): indexer = None missing = np.array([], dtype=np.intp) else: indexer, missing = self.get_indexer_non_unique(target) if not self.is_unique: # GH#42568 warnings.warn( "reindexing with a non-unique Index is deprecated and will " "raise in a future version.", FutureWarning, stacklevel=find_stack_level(), ) new_target: Index if len(self) and indexer is not None: new_target = self.take(indexer) else: new_target = target # filling in missing if needed if len(missing): cats = self.categories.get_indexer(target) if not isinstance(target, CategoricalIndex) or (cats == -1).any(): new_target, indexer, _ = super()._reindex_non_unique(target) else: # error: "Index" has no attribute "codes" codes = new_target.codes.copy() # type: ignore[attr-defined] codes[indexer == -1] = cats[missing] cat = self._data._from_backing_data(codes) new_target = type(self)._simple_new(cat, name=self.name) # we always want to return an Index type here # to be consistent with .reindex for other index types (e.g. they don't # coerce based on the actual values, only on the dtype) # unless we had an initial Categorical to begin with # in which case we are going to conform to the passed Categorical if is_categorical_dtype(target): cat = Categorical(new_target, dtype=target.dtype) new_target = type(self)._simple_new(cat, name=self.name) else: # e.g. test_reindex_with_categoricalindex, test_reindex_duplicate_target new_target_array = np.asarray(new_target) new_target = Index._with_infer(new_target_array, name=self.name) return new_target, indexer # -------------------------------------------------------------------- # Indexing Methods def _maybe_cast_indexer(self, key) -> int: # GH#41933: we have to do this instead of self._data._validate_scalar # because this will correctly get partial-indexing on Interval categories try: return self._data._unbox_scalar(key) except KeyError: if is_valid_na_for_dtype(key, self.categories.dtype): return -1 raise def _maybe_cast_listlike_indexer(self, values) -> CategoricalIndex: if isinstance(values, CategoricalIndex): values = values._data if isinstance(values, Categorical): # Indexing on codes is more efficient if categories are the same, # so we can apply some optimizations based on the degree of # dtype-matching. cat = self._data._encode_with_my_categories(values) codes = cat._codes else: codes = self.categories.get_indexer(values) codes = codes.astype(self.codes.dtype, copy=False) cat = self._data._from_backing_data(codes) return type(self)._simple_new(cat) # -------------------------------------------------------------------- def _is_comparable_dtype(self, dtype: DtypeObj) -> bool: return self.categories._is_comparable_dtype(dtype) def take_nd(self, *args, **kwargs) -> CategoricalIndex: """Alias for `take`""" warnings.warn( "CategoricalIndex.take_nd is deprecated, use CategoricalIndex.take " "instead.", FutureWarning, stacklevel=find_stack_level(), ) return self.take(*args, **kwargs) def map(self, mapper): """ Map values using input an input mapping 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 :class:`~pandas.CategoricalIndex` which has the same order property as the original, otherwise an :class:`~pandas.Index` is returned. If a `dict` or :class:`~pandas.Series` is used any unmapped category is mapped to `NaN`. Note that if this happens an :class:`~pandas.Index` will be returned. Parameters ---------- mapper : function, dict, or Series Mapping correspondence. Returns ------- pandas.CategoricalIndex or pandas.Index Mapped index. See Also -------- Index.map : Apply a mapping correspondence on an :class:`~pandas.Index`. Series.map : Apply a mapping correspondence on a :class:`~pandas.Series`. Series.apply : Apply more complex functions on a :class:`~pandas.Series`. Examples -------- >>> idx = pd.CategoricalIndex(['a', 'b', 'c']) >>> idx CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'], ordered=False, dtype='category') >>> idx.map(lambda x: x.upper()) CategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'], ordered=False, dtype='category') >>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'}) 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 = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True) >>> idx CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'], ordered=True, dtype='category') >>> idx.map({'a': 3, 'b': 2, 'c': 1}) CategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True, dtype='category') If the mapping is not one-to-one an :class:`~pandas.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 `NaN` and the result is an :class:`~pandas.Index`: >>> idx.map({'a': 'first', 'b': 'second'}) Index(['first', 'second', nan], dtype='object') """ mapped = self._values.map(mapper) return Index(mapped, name=self.name) def _concat(self, to_concat: list[Index], name: Hashable) -> Index: # if calling index is category, don't check dtype of others try: cat = Categorical._concat_same_type( [self._is_dtype_compat(c) for c in to_concat] ) except TypeError: # not all to_concat elements are among our categories (or NA) from pandas.core.dtypes.concat import concat_compat res = concat_compat([x._values for x in to_concat]) return Index(res, name=name) else: return type(self)._simple_new(cat, name=name)