pandas.Categorical.reorder_categories#

Categorical.reorder_categories(new_categories, ordered=None)[source]#

Reorder categories as specified in new_categories.

new_categories need to include all old categories and no new category items.

Parameters:

new_categoriesIndex-like: The categories in new order.
orderedbool, optional: Whether or not the categorical is treated as a ordered categorical. If not given, do not change the ordered information.

Returns:

Categorical: Categorical with reordered categories.

Raises:

ValueError: If the new categories do not contain all old category items or any new ones

See also

rename_categories: Rename 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 = ser.cat.reorder_categories(["c", "b", "a"], ordered=True)
>>> ser
0   a
1   b
2   c
3   a
dtype: category
Categories (3, object): ['c' < 'b' < 'a']

>>> ser.sort_values()
2   c
1   b
0   a
3   a
dtype: category
Categories (3, object): ['c' < 'b' < 'a']

For pandas.CategoricalIndex:

>>> ci = pd.CategoricalIndex(["a", "b", "c", "a"])
>>> ci
CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c'],
                 ordered=False, dtype='category')
>>> ci.reorder_categories(["c", "b", "a"], ordered=True)
CategoricalIndex(['a', 'b', 'c', 'a'], categories=['c', 'b', 'a'],
                 ordered=True, dtype='category')