pandas.CategoricalIndex¶

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.

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'

Attributes

`codes`	The category codes of this categorical.
`categories`	The categories of this categorical.
`ordered`	Whether the categories have an ordered relationship.

Methods

`rename_categories`(args, *kwargs)	Rename categories.
`reorder_categories`(args, *kwargs)	Reorder categories as specified in new_categories.
`add_categories`(args, *kwargs)	Add new categories.
`remove_categories`(args, *kwargs)	Remove the specified categories.
`remove_unused_categories`(args, *kwargs)	Remove categories which are not used.
`set_categories`(args, *kwargs)	Set the categories to the specified new_categories.
`as_ordered`(args, *kwargs)	Set the Categorical to be ordered.
`as_unordered`(args, *kwargs)	Set the Categorical to be unordered.
`map`(mapper)	Map values using input correspondence (a dict, Series, or function).

pandas.Float64Index pandas.CategoricalIndex.codes