# pandas.Categorical

class `pandas.``Categorical`(values, categories=None, ordered=None, dtype=None, fastpath=False)

  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.

  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.

  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.

  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

  `pandas.api.types.CategoricalDtype`
    Type for categorical data
  `CategoricalIndex`
    An Index with an underlying `Categorical`

  Notes

  See the user guide 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]

  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'

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