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class pandas.Categorical(values, categories=None, ordered=None, dtype=None, fastpath=False)[source]

Represents 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.


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.

ordered : boolean, (default False)

Whether or not this categorical is treated as a ordered categorical. If not given, the resulting categorical will not be ordered.

dtype : CategoricalDtype

An instance of CategoricalDtype to use for this categorical

New in version 0.21.0.



If the categories do not validate.


If an explicit ordered=True is given but no categories and the values are not sortable.

See also

Type for categorical data
An Index with an underlying Categorical


See the user guide for more.


>>> 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]

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()
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