Allow for dict-like categorical renaming

Author: alanbato

doc/source/categorical.rst

@@ -206,6 +206,10 @@ by using the :func:`Categorical.rename_categories` method:
     s.cat.categories = ["Group %s" % g for g in s.cat.categories]
     s
     s.cat.rename_categories([1,2,3])
+    s
+    # You can also pass a dict-like object to map the renaming
+    s.cat.rename_categories({1: 'x', 2: 'y', 3: 'z'})
+    s
 
 .. note::
 

doc/source/whatsnew/v0.21.0.txt

@@ -114,7 +114,8 @@ Other Enhancements
 - :func:`pd.read_sas()` now recognizes much more of the most frequently used date (datetime) formats in SAS7BDAT files (:issue:`15871`).
 - :func:`DataFrame.items` and :func:`Series.items` is now present in both Python 2 and 3 and is lazy in all cases (:issue:`13918`, :issue:`17213`)
 - :func:`Styler.where` has been implemented. It is as a convenience for :func:`Styler.applymap` and enables simple DataFrame styling on the Jupyter notebook (:issue:`17474`).
-
+- :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:`MultiIndex.is_monotonic_decreasing` has been implemented.  Previously returned ``False`` in all cases. (:issue:`16554`)
 
 
 .. _whatsnew_0210.api_breaking:
@@ -525,6 +526,8 @@ Plotting
 ^^^^^^^^
 - Bug in plotting methods using ``secondary_y`` and ``fontsize`` not setting secondary axis font size (:issue:`12565`)
 - Bug when plotting ``timedelta`` and ``datetime`` dtypes on y-axis (:issue:`16953`)
+- Line plots no longer assume monotonic x data when calculating xlims, they show the entire lines now even for unsorted x data. (:issue:`11310`)(:issue:`11471`)
+- With matplotlib 2.0.0 and above, calculation of x limits for line plots is left to matplotlib, so that its new default settings are applied. (:issue:`15495`)
 - Bug in ``Series.plot.bar`` or ``DataFramee.plot.bar`` with ``y`` not respecting user-passed ``color`` (:issue:`16822`)
 
 

pandas/core/categorical.py

@@ -25,7 +25,8 @@
     is_categorical_dtype,
     is_integer_dtype, is_bool,
     is_list_like, is_sequence,
-    is_scalar)
+    is_scalar,
+    is_dict_like)
 from pandas.core.common import is_null_slice, _maybe_box_datetimelike
 
 from pandas.core.algorithms import factorize, take_1d, unique1d
@@ -824,7 +825,11 @@ def rename_categories(self, new_categories, inplace=False):
         """
         inplace = validate_bool_kwarg(inplace, 'inplace')
         cat = self if inplace else self.copy()
-        cat.categories = new_categories
+        if is_dict_like(new_categories):
+            cat.categories = [new_categories.get(item, item)
+                              for item in cat.categories]
+        else:
+            cat.categories = new_categories
         if not inplace:
             return cat
 

pandas/tests/test_categorical.py

@@ -983,6 +983,35 @@ def test_rename_categories(self):
         with pytest.raises(ValueError):
             cat.rename_categories([1, 2])
 
+    def test_rename_categories_dict(self):
+        # GH 17336
+        cat = pd.Categorical(['a', 'b', 'c', 'd'])
+        res = cat.rename_categories({'a': 4, 'b': 3, 'c': 2, 'd': 1})
+        expected = Index([4, 3, 2, 1])
+        tm.assert_index_equal(res.categories, expected)
+        # Test for inplace
+        res = cat.rename_categories({'a': 4, 'b': 3, 'c': 2, 'd': 1},
+                                    inplace=True)
+        assert res is None
+
+        tm.assert_index_equal(cat.categories, expected)
+        # Test for dicts of smaller length
+        cat = pd.Categorical(['a', 'b', 'c', 'd'])
+        res = cat.rename_categories({'a': 1, 'c': 3})
+        expected = Index([1, 'b', 3, 'd'])
+        tm.assert_index_equal(res.categories, expected)
+        # Test for dicts with bigger length
+        cat = pd.Categorical(['a', 'b', 'c', 'd'])
+        res = cat.rename_categories({'a': 1, 'b': 2, 'c': 3,
+                                     'd': 4, 'e': 5, 'f': 6})
+        expected = Index([1, 2, 3, 4])
+        tm.assert_index_equal(res.categories, expected)
+        # Test for dicts with no items from old categories
+        cat = pd.Categorical(['a', 'b', 'c', 'd'])
+        res = cat.rename_categories({'f': 1, 'g': 3})
+        expected = Index(['a', 'b', 'c', 'd'])
+        tm.assert_index_equal(res.categories, expected)
+
     @pytest.mark.parametrize('codes, old, new, expected', [
         ([0, 1], ['a', 'b'], ['a', 'b'], [0, 1]),
         ([0, 1], ['b', 'a'], ['b', 'a'], [0, 1]),