pyspark.pandas.Series.cat.rename_categories¶

cat.rename_categories(new_categories: Union[list, dict, Callable]) → Optional[ps.Series]¶

Rename categories.

Parameters

new_categorieslist-like, dict-like or callable

New categories which will replace old categories.

list-like: all items must be unique and the number of items in the new categories must match the existing number of categories.
dict-like: specifies a mapping from old categories to new. Categories not contained in the mapping are passed through and extra categories in the mapping are ignored.
callable : a callable that is called on all items in the old categories and whose return values comprise the new categories.

Returns

catSeries or None: Categorical with removed categories or None if inplace=True.

Raises

ValueError: If new categories are list-like and do not have the same number of items than the current categories or do not validate as categories

See also

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

>>> s = ps.Series(["a", "a", "b"], dtype="category")
>>> s.cat.rename_categories([0, 1])  
0    0
1    0
2    1
dtype: category
Categories (2, int64): [0, 1]

For dict-like new_categories, extra keys are ignored and categories not in the dictionary are passed through

>>> s.cat.rename_categories({'a': 'A', 'c': 'C'})  
0    A
1    A
2    b
dtype: category
Categories (2, object): ['A', 'b']

You may also provide a callable to create the new categories

>>> s.cat.rename_categories(lambda x: x.upper())  
0    A
1    A
2    B
dtype: category
Categories (2, object): ['A', 'B']

pyspark.pandas.Series.cat.codes pyspark.pandas.Series.cat.reorder_categories