pandas.CategoricalIndex.reorder_categories#
- CategoricalIndex.reorder_categories(*args, **kwargs)[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']
>>> 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')