cudf.core.column.categorical.CategoricalAccessor.set_categories#

CategoricalAccessor.set_categories(new_categories: Any, ordered: bool = False, rename: bool = False, inplace: bool = False) → SeriesOrIndex | None#

Set the categories to the specified new_categories.

new_categories can include new categories (which will result in unused categories) or remove old categories (which results in values set to null). If rename==True, the categories will simple be renamed (less or more items than in old categories will result in values set to null or in unused categories respectively).

This method can be used to perform more than one action of adding, removing, and reordering simultaneously and is therefore faster than performing the individual steps via the more specialised methods.

On the other hand this methods does not do checks (e.g., whether the old categories are included in the new categories on a reorder), which can result in surprising changes.

Parameters:

new_categorieslist-like: The categories in new order.
orderedbool, default None: Whether or not the categorical is treated as a ordered categorical. If not given, do not change the ordered information.
renamebool, default False: Whether or not the new_categories should be considered as a rename of the old categories or as reordered categories.
inplacebool, default False: Whether or not to reorder the categories in-place or return a copy of this categorical with reordered categories.

Deprecated since version 23.04: The inplace parameter is is deprecated and will be removed in a future version of cudf. Setting categories will always return a new Categorical object.

Returns:

cat: Categorical with reordered categories or None if inplace.

Examples

>>> import cudf
>>> s = cudf.Series([1, 1, 2, 10, 2, 10], dtype='category')
>>> s
0     1
1     1
2     2
3    10
4     2
5    10
dtype: category
Categories (3, int64): [1, 2, 10]
>>> s.cat.set_categories([1, 10])
0       1
1       1
2    <NA>
3      10
4    <NA>
5      10
dtype: category
Categories (2, int64): [1, 10]
>>> s.cat.set_categories([1, 10], inplace=True)
>>> s
0       1
1       1
2    <NA>
3      10
4    <NA>
5      10
dtype: category
Categories (2, int64): [1, 10]