pandas.Series.cat.categories#

Series.cat.categories[source]#

The categories of this categorical.

Setting assigns new values to each category (effectively a rename of each individual category).

The assigned value has to be a list-like object. All items must be unique and the number of items in the new categories must be the same as the number of items in the old categories.

Raises:

ValueError: If the new categories do not validate as categories or if the number of new categories is unequal the number of old categories

See also

rename_categories: Rename categories.
reorder_categories: Reorder categories.
add_categories: Add new categories.
remove_categories: Remove the specified categories.
remove_unused_categories: Remove categories which are not used.
set_categories: Set the categories to the specified ones.

Examples

For pandas.Series:

>>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
>>> ser.cat.categories
Index(['a', 'b', 'c'], dtype='object')

>>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], categories=['b', 'c', 'd'])
>>> ser = pd.Series(raw_cat)
>>> ser.cat.categories
Index(['b', 'c', 'd'], dtype='object')

For pandas.Categorical:

>>> cat = pd.Categorical(['a', 'b'], ordered=True)
>>> cat.categories
Index(['a', 'b'], dtype='object')

For pandas.CategoricalIndex:

>>> ci = pd.CategoricalIndex(['a', 'c', 'b', 'a', 'c', 'b'])
>>> ci.categories
Index(['a', 'b', 'c'], dtype='object')

>>> ci = pd.CategoricalIndex(['a', 'c'], categories=['c', 'b', 'a'])
>>> ci.categories
Index(['c', 'b', 'a'], dtype='object')