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Module « pandas »

Classe « Categorical »

Informations générales

Héritage

builtins.object
    ABC
        BaseStringArrayMethods
            ObjectStringArrayMixin
    builtins.object
        DirNamesMixin
            PandasObject
    builtins.object
        ExtensionArray
            NDArrayBackedExtensionArray
                Categorical

Définition

class Categorical(NDArrayBackedExtensionArray, PandasObject, ObjectStringArrayMixin):

Description [extrait de Categorical.__doc__]

    Represent a categorical variable in classic R / S-plus fashion.

    `Categoricals` can only take on only a limited, and usually fixed, number
    of possible values (`categories`). In contrast to statistical categorical
    variables, a `Categorical` might have an order, but numerical operations
    (additions, divisions, ...) are not possible.

    All values of the `Categorical` are either in `categories` or `np.nan`.
    Assigning values outside of `categories` will raise a `ValueError`. Order
    is defined by the order of the `categories`, not lexical order of the
    values.

    Parameters
    ----------
    values : list-like
        The values of the categorical. If categories are given, values not in
        categories will be replaced with NaN.
    categories : Index-like (unique), optional
        The unique categories for this categorical. If not given, the
        categories are assumed to be the unique values of `values` (sorted, if
        possible, otherwise in the order in which they appear).
    ordered : bool, default False
        Whether or not this categorical is treated as a ordered categorical.
        If True, the resulting categorical will be ordered.
        An ordered categorical respects, when sorted, the order of its
        `categories` attribute (which in turn is the `categories` argument, if
        provided).
    dtype : CategoricalDtype
        An instance of ``CategoricalDtype`` to use for this categorical.

    Attributes
    ----------
    categories : Index
        The categories of this categorical
    codes : ndarray
        The codes (integer positions, which point to the categories) of this
        categorical, read only.
    ordered : bool
        Whether or not this Categorical is ordered.
    dtype : CategoricalDtype
        The instance of ``CategoricalDtype`` storing the ``categories``
        and ``ordered``.

    Methods
    -------
    from_codes
    __array__

    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
    --------
    CategoricalDtype : Type for categorical data.
    CategoricalIndex : An Index with an underlying ``Categorical``.

    Notes
    -----
    See the `user guide
    <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>`_
    for more.

    Examples
    --------
    >>> pd.Categorical([1, 2, 3, 1, 2, 3])
    [1, 2, 3, 1, 2, 3]
    Categories (3, int64): [1, 2, 3]

    >>> pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'])
    ['a', 'b', 'c', 'a', 'b', 'c']
    Categories (3, object): ['a', 'b', 'c']

    Missing values are not included as a category.

    >>> c = pd.Categorical([1, 2, 3, 1, 2, 3, np.nan])
    >>> c
    [1, 2, 3, 1, 2, 3, NaN]
    Categories (3, int64): [1, 2, 3]

    However, their presence is indicated in the `codes` attribute
    by code `-1`.

    >>> c.codes
    array([ 0,  1,  2,  0,  1,  2, -1], dtype=int8)

    Ordered `Categoricals` can be sorted according to the custom order
    of the categories and can have a min and max value.

    >>> c = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'], ordered=True,
    ...                    categories=['c', 'b', 'a'])
    >>> c
    ['a', 'b', 'c', 'a', 'b', 'c']
    Categories (3, object): ['c' < 'b' < 'a']
    >>> c.min()
    'c'
    

Constructeur(s)

Signature du constructeur Description
__init__(self, values, categories=None, ordered=None, dtype=None, fastpath=False)

Liste des attributs statiques

Nom de l'attribut Valeur
itemsize<pandas._libs.properties.CachedProperty object at 0x7f504c4d4880>
ndim<pandas._libs.properties.CachedProperty object at 0x7f504c4ffbc0>
size<pandas._libs.properties.CachedProperty object at 0x7f504c4ffec0>

Attributs statiques hérités de la classe NDArrayBackedExtensionArray

nbytes

Liste des propriétés

Nom de la propriétéDescription
categories
codes
dtype
nbytes
ordered
shape
T

Propriétés héritées de la classe ExtensionArray

ndim, size

Liste des opérateurs

Signature de l'opérateur Description
__contains__(self, key) -> bool
__getitem__(self, key)

Opérateurs hérités de la classe NDArrayBackedExtensionArray

__setitem__

Opérateurs hérités de la classe ExtensionArray

__eq__, __ne__

Opérateurs hérités de la classe object

__ge__, __gt__, __le__, __lt__

Liste des méthodes

Toutes les méthodes Méthodes d'instance Méthodes statiques Méthodes dépréciées
Signature de la méthodeDescription
__array__(self, dtype=None) -> numpy.ndarray
__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
__iter__(self)
__repr__(self) -> str
__setstate__(self, state) Necessary for making this object picklable [extrait de __setstate__.__doc__]
add_categories(self, new_categories, inplace=False)
argsort(self, ascending=True, kind='quicksort', **kwargs)
as_ordered(self, inplace=False)
as_unordered(self, inplace=False)
astype(self, dtype: Union[ForwardRef('ExtensionDtype'), str, numpy.dtype, Type[Union[str, float, int, complex, bool, object]]], copy: bool = True) -> ~ArrayLike
check_for_ordered(self, op) assert that we are ordered [extrait de check_for_ordered.__doc__]
describe(self)
equals(self, other: object) -> bool
fillna(self, value=None, method=None, limit=None)
from_codes(codes, categories=None, ordered=None, dtype=None)
is_dtype_equal(self, other) -> bool
isin(self, values) -> numpy.ndarray
isna(self)
isnull(self)
map(self, mapper)
max(self, *, skipna=True, **kwargs)
memory_usage(self, deep: bool = False) -> int
min(self, *, skipna=True, **kwargs)
mode(self, dropna=True)
notna(self)
notnull(self)
remove_categories(self, removals, inplace=False)
remove_unused_categories(self, inplace=<object object at 0x7f5051439e10>)
rename_categories(self, new_categories, inplace=False)
reorder_categories(self, new_categories, ordered=None, inplace=False)
replace(self, to_replace, value, inplace: bool = False)
set_categories(self, new_categories, ordered=None, rename=False, inplace=False)
set_ordered(self, value, inplace=False)
sort_values(self, inplace: bool = False, ascending: bool = True, na_position: str = 'last')
take_nd(self, indexer, allow_fill: bool = False, fill_value=None)
to_dense(self)
to_list(self) -> List[Union[str, int, float, bool, ForwardRef('Period'), ForwardRef('Timestamp'), ForwardRef('Timedelta'), ForwardRef('Interval')]]
tolist(self) -> List[Union[str, int, float, bool, ForwardRef('Period'), ForwardRef('Timestamp'), ForwardRef('Timedelta'), ForwardRef('Interval')]]
unique(self)
value_counts(self, dropna=True)
view(self, dtype=None)

Méthodes héritées de la classe ObjectStringArrayMixin

__init_subclass__, __len__, __subclasshook__

Méthodes héritées de la classe PandasObject

__sizeof__

Méthodes héritées de la classe DirNamesMixin

__dir__

Méthodes héritées de la classe NDArrayBackedExtensionArray

copy, putmask, ravel, repeat, reshape, searchsorted, shift, take, where

Méthodes héritées de la classe ExtensionArray

__hash__, argmax, argmin, dropna, factorize, to_numpy, transpose

Méthodes héritées de la classe object

__delattr__, __format__, __getattribute__, __reduce__, __reduce_ex__, __setattr__, __str__