rename_categories(self, new_categories, inplace=<no_default>)
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.
Whether or not to rename the categories inplace or return a copy of this categorical with renamed categories.
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
Categorical with removed categories or None if inplace=True
.
Rename categories.
add_categories
Add new categories.
remove_categories
Remove the specified categories.
remove_unused_categories
Remove categories which are not used.
reorder_categories
Reorder categories.
set_categories
Set the categories to the specified ones.
>>> c = pd.Categorical(['a', 'a', 'b'])
... c.rename_categories([0, 1]) [0, 0, 1] Categories (2, int64): [0, 1]
For dict-like new_categories
, extra keys are ignored and categories not in the dictionary are passed through
>>> c.rename_categories({'a': 'A', 'c': 'C'}) ['A', 'A', 'b'] Categories (2, object): ['A', 'b']
You may also provide a callable to create the new categories
This example is valid syntax, but we were not able to check execution>>> c.rename_categories(lambda x: x.upper()) ['A', 'A', 'B'] Categories (2, object): ['A', 'B']See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.arrays.categorical.Categorical.set_categories
pandas.core.arrays.categorical.Categorical.remove_categories
pandas.core.arrays.categorical.Categorical.add_categories
pandas.core.arrays.categorical.Categorical.remove_unused_categories
pandas.core.arrays.categorical.Categorical.reorder_categories
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