Rename categories with Series

doc/source/whatsnew/v0.21.0.txt

@@ -239,6 +239,36 @@ Now, to find prices per store/product, we can simply do:
       .pipe(lambda grp: grp.Revenue.sum()/grp.Quantity.sum())
       .unstack().round(2))
 
+
+.. _whatsnew_0210.enhancements.reanme_categories:
+
+``Categorical.rename_categories`` accepts a dict-like
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:meth:`~Series.cat.rename_categories` now accepts a dict-like argument for
+``new_categories``. The previous categories are lookup up in the dictionary's
+keys and replaced if found. The behavior of missing and extra keys is the same
+as in :meth:`DataFrame.rename`.
+
+.. ipython:: python
+
+   c = pd.Categorical(['a', 'a', 'b'])
+   c.rename_categories({"a": "eh", "b": "bee"})
+
+.. warning::
+
+   To assist with upgrading pandas, ``rename_categories`` treats ``Series`` as
+   list-like. Typically, they are considered to be dict-like, and in a future
+   version of pandas ``rename_categories`` will change to treat them as
+   dict-like.
+
+  .. ipython:: python
+     :okwarning:
+
+     c.rename_categories(pd.Series([0, 1], index=['a', 'c']))
+
+  Follow the warning message's recommendations.
+
 See the :ref:`documentation <groupby.pipe>` for more.
 
 .. _whatsnew_0210.enhancements.other:
@@ -267,7 +297,6 @@ Other Enhancements
 - :func:`DataFrame.items` and :func:`Series.items` are now present in both Python 2 and 3 and is lazy in all cases. (:issue:`13918`, :issue:`17213`)
 - :func:`Styler.where` has been implemented as a convenience for :func:`Styler.applymap`. (:issue:`17474`)
 - :func:`MultiIndex.is_monotonic_decreasing` has been implemented.  Previously returned ``False`` in all cases. (:issue:`16554`)
-- :func:`Categorical.rename_categories` now accepts a dict-like argument as ``new_categories`` and only updates the categories found in that dict. (:issue:`17336`)
 - :func:`read_excel` raises ``ImportError`` with a better message if ``xlrd`` is not installed. (:issue:`17613`)
 - :func:`read_json` now accepts a ``chunksize`` parameter that can be used when ``lines=True``. If ``chunksize`` is passed, read_json now returns an iterator which reads in ``chunksize`` lines with each iteration. (:issue:`17048`)
 - :meth:`DataFrame.assign` will preserve the original order of ``**kwargs`` for Python 3.6+ users instead of sorting the column names. (:issue:`14207`)

pandas/core/categorical.py

@@ -866,11 +866,6 @@ def set_categories(self, new_categories, ordered=None, rename=False,
     def rename_categories(self, new_categories, inplace=False):
         """ Renames categories.
 
-        The new categories can be either a list-like dict-like object.
-        If it is list-like, all items must be unique and the number of items
-        in the new categories must be the same as the number of items in the
-        old categories.
-
         Raises
         ------
         ValueError
@@ -879,15 +874,30 @@ def rename_categories(self, new_categories, inplace=False):
 
         Parameters
         ----------
-        new_categories : Index-like or dict-like (>=0.21.0)
-           The renamed categories.
+        new_categories : list-like or dict-like
+
+           * 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. *New in verison 0.21.0*.
+
+           .. warning::
+
+              Currently, Series are considered list like. In a future version
+              of pandas they'll be considered dict-like.
+
         inplace : boolean (default: False)
            Whether or not to rename the categories inplace or return a copy of
            this categorical with renamed categories.
 
         Returns
         -------
-        cat : Categorical with renamed categories added or None if inplace.
+        cat : Categorical or None
+           With ``inplace=False``, the new categorical is returned.
+           With ``inplace=True``, there is no return value.
 
         See also
         --------
@@ -896,10 +906,33 @@ def rename_categories(self, new_categories, inplace=False):
         remove_categories
         remove_unused_categories
         set_categories
+
+        Examples
+        --------
+        >>> c = 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]
         """
         inplace = validate_bool_kwarg(inplace, 'inplace')
         cat = self if inplace else self.copy()
 
+        if isinstance(new_categories, ABCSeries):
+            msg = ("Treating Series 'new_categories' as a list-like and using "
+                   "the values. In a future version, 'rename_categories' will "
+                   "treat Series like a dictionary.\n"
+                   "For dict-like, use 'new_categories.to_dict()'\n"
+                   "For list-like, use 'new_categories.values'.")
+            warn(msg, FutureWarning, stacklevel=2)
+            new_categories = list(new_categories)
+
         if is_dict_like(new_categories):
             cat.categories = [new_categories.get(item, item)
                               for item in cat.categories]

pandas/tests/test_categorical.py

@@ -1203,6 +1203,18 @@ def test_rename_categories(self):
         with pytest.raises(ValueError):
             cat.rename_categories([1, 2])
 
+    def test_rename_categories_series(self):
+        # https://github.com/pandas-dev/pandas/issues/17981
+        c = pd.Categorical(['a', 'b'])
+        xpr = "Treating Series 'new_categories' as a list-like "
+        with tm.assert_produces_warning(FutureWarning) as rec:
+            result = c.rename_categories(pd.Series([0, 1]))
+
+        assert len(rec) == 1
+        assert xpr in str(rec[0].message)
+        expected = pd.Categorical([0, 1])
+        tm.assert_categorical_equal(result, expected)
+
     def test_rename_categories_dict(self):
         # GH 17336
         cat = pd.Categorical(['a', 'b', 'c', 'd'])