pandas.CategoricalIndex.set_categories#

CategoricalIndex.set_categories(*args,**kwargs)[source]#

Set the categories to the specified new categories.

new_categories can include new categories (which will result inunused categories) or remove old categories (which results in valuesset toNaN). Ifrename=True, the categories will simply be renamed(less or more items than in old categories will result in values set toNaN 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 thanperforming the individual steps via the more specialised methods.

On the other hand this methods does not do checks (e.g., whether theold categories are included in the new categories on a reorder), whichcan result in surprising changes, for example when using special stringdtypes, which does not considers a S1 string equal to a single charpython string.

Parameters:

new_categoriesIndex-like

The categories in new order.

orderedbool, default False

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 renameof the old categories or as reordered categories.

Returns:

Categorical with reordered categories.

Raises:

ValueError

If new_categories does not validate as 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.

Examples

Forpandas.Series:

>>>raw_cat=pd.Categorical(['a','b','c','A'],...categories=['a','b','c'],ordered=True)>>>ser=pd.Series(raw_cat)>>>ser0   a1   b2   c3   NaNdtype: categoryCategories (3, object): ['a' < 'b' < 'c']

>>>ser.cat.set_categories(['A','B','C'],rename=True)0   A1   B2   C3   NaNdtype: categoryCategories (3, object): ['A' < 'B' < 'C']

Forpandas.CategoricalIndex:

>>>ci=pd.CategoricalIndex(['a','b','c','A'],...categories=['a','b','c'],ordered=True)>>>ciCategoricalIndex(['a', 'b', 'c', nan], categories=['a', 'b', 'c'],                 ordered=True, dtype='category')

>>>ci.set_categories(['A','b','c'])CategoricalIndex([nan, 'b', 'c', nan], categories=['A', 'b', 'c'],                 ordered=True, dtype='category')>>>ci.set_categories(['A','b','c'],rename=True)CategoricalIndex(['A', 'b', 'c', nan], categories=['A', 'b', 'c'],                 ordered=True, dtype='category')