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pandas.CategoricalIndex — pandas 2.3.1 documentation

class pandas.CategoricalIndex(data=None, categories=None, ordered=None, dtype=None, copy=False, name=None)[source]#

Index based on an underlying 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:

dataarray-like (1-dimensional)

The values of the categorical. If categories are given, values not in categories will be replaced with NaN.

categoriesindex-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.

orderedbool, 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.

dtypeCategoricalDtype or “category”, optional

If CategoricalDtype, cannot be used together with categories or ordered.

copybool, default False

Make a copy of input ndarray.

nameobject, optional

Name to be stored in the index.

Attributes

codes

The category codes of this categorical index.

categories

The categories of this categorical.

ordered

Whether the categories have an ordered relationship.

Methods

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.

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'

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