Deprecate numeric_only parameter in Categorical.min and max

Author: makbigc

doc/source/whatsnew/v0.25.1.rst

@@ -16,6 +16,11 @@ For example, on MacOS installing Python with `pyenv` may lead to an incomplete P
 
 .. _whatsnew_0251.bug_fixes:
 
+Other deprecations
+^^^^^^^^^^^^^^^^^^
+
+- The parameter ``numeric_only`` of :meth:`Categorical.min` and :meth:`Categorical.max` is deprecated and replaced with ``skipna`` (:issue:`25303`)
+
 Bug fixes
 ~~~~~~~~~
 

pandas/core/arrays/categorical.py

@@ -2196,7 +2196,8 @@ def _reduce(self, name, axis=0, **kwargs):
             raise TypeError(msg.format(op=name))
         return func(**kwargs)
 
-    def min(self, numeric_only=None, **kwargs):
+    @deprecate_kwarg(old_arg_name="numeric_only", new_arg_name="skipna")
+    def min(self, skipna=True, **kwargs):
         """
         The minimum value of the object.
 
@@ -2212,17 +2213,18 @@ def min(self, numeric_only=None, **kwargs):
         min : the minimum of this `Categorical`
         """
         self.check_for_ordered("min")
-        if numeric_only:
-            good = self._codes != -1
-            pointer = self._codes[good].min(**kwargs)
+        good = self._codes != -1
+        if good.any():
+            if skipna:
+                pointer = self._codes[good].min(**kwargs)
+            else:
+                return np.nan
         else:
             pointer = self._codes.min(**kwargs)
-        if pointer == -1:
-            return np.nan
-        else:
-            return self.categories[pointer]
+        return self.categories[pointer]
 
-    def max(self, numeric_only=None, **kwargs):
+    @deprecate_kwarg(old_arg_name="numeric_only", new_arg_name="skipna")
+    def max(self, skipna=True, **kwargs):
         """
         The maximum value of the object.
 
@@ -2238,15 +2240,15 @@ def max(self, numeric_only=None, **kwargs):
         max : the maximum of this `Categorical`
         """
         self.check_for_ordered("max")
-        if numeric_only:
-            good = self._codes != -1
-            pointer = self._codes[good].max(**kwargs)
+        good = self._codes != -1
+        if good.any():
+            if skipna:
+                pointer = self._codes[good].max(**kwargs)
+            else:
+                return np.nan
         else:
             pointer = self._codes.max(**kwargs)
-        if pointer == -1:
-            return np.nan
-        else:
-            return self.categories[pointer]
+        return self.categories[pointer]
 
     def mode(self, dropna=True):
         """

pandas/core/series.py

@@ -3980,9 +3980,7 @@ def _reduce(
             self._get_axis_number(axis)
 
         if isinstance(delegate, Categorical):
-            # TODO deprecate numeric_only argument for Categorical and use
-            # skipna as well, see GH25303
-            return delegate._reduce(name, numeric_only=numeric_only, **kwds)
+            return delegate._reduce(name, skipna=skipna, **kwds)
         elif isinstance(delegate, ExtensionArray):
             # dispatch to ExtensionArray interface
             return delegate._reduce(name, skipna=skipna, **kwds)

pandas/tests/arrays/categorical/test_analytics.py

@@ -38,29 +38,38 @@ def test_min_max(self):
         cat = Categorical(
             [np.nan, "b", "c", np.nan], categories=["d", "c", "b", "a"], ordered=True
         )
-        _min = cat.min()
-        _max = cat.max()
+        _min = cat.min(skipna=False)
+        _max = cat.max(skipna=False)
         assert np.isnan(_min)
-        assert _max == "b"
+        assert np.isnan(_max)
 
-        _min = cat.min(numeric_only=True)
+        _min = cat.min()
         assert _min == "c"
-        _max = cat.max(numeric_only=True)
+        _max = cat.max()
         assert _max == "b"
 
         cat = Categorical(
             [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
         )
-        _min = cat.min()
-        _max = cat.max()
+        _min = cat.min(skipna=False)
+        _max = cat.max(skipna=False)
         assert np.isnan(_min)
-        assert _max == 1
+        assert np.isnan(_max)
 
-        _min = cat.min(numeric_only=True)
+        _min = cat.min()
         assert _min == 2
-        _max = cat.max(numeric_only=True)
+        _max = cat.max()
         assert _max == 1
 
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_deprecate_numeric_only_min_max(self, method):
+        # GH 25303
+        cat = Categorical(
+            [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
+        )
+        with tm.assert_produces_warning(expected_warning=FutureWarning):
+            getattr(cat, method)(numeric_only=False)
+
     @pytest.mark.parametrize(
         "values,categories,exp_mode",
         [

pandas/tests/reductions/test_reductions.py

@@ -1028,7 +1028,7 @@ def test_min_max(self):
         )
         _min = cat.min()
         _max = cat.max()
-        assert np.isnan(_min)
+        assert _min == "c"
         assert _max == "b"
 
         cat = Series(
@@ -1038,30 +1038,24 @@ def test_min_max(self):
         )
         _min = cat.min()
         _max = cat.max()
-        assert np.isnan(_min)
+        assert _min == 2
         assert _max == 1
 
-    def test_min_max_numeric_only(self):
-        # TODO deprecate numeric_only argument for Categorical and use
-        # skipna as well, see GH25303
+    def test_min_max_skipna(self):
+        # GH 25303
         cat = Series(
             Categorical(["a", "b", np.nan, "a"], categories=["b", "a"], ordered=True)
         )
 
         _min = cat.min()
         _max = cat.max()
-        assert np.isnan(_min)
-        assert _max == "a"
-
-        _min = cat.min(numeric_only=True)
-        _max = cat.max(numeric_only=True)
         assert _min == "b"
         assert _max == "a"
 
-        _min = cat.min(numeric_only=False)
-        _max = cat.max(numeric_only=False)
+        _min = cat.min(skipna=False)
+        _max = cat.max(skipna=False)
         assert np.isnan(_min)
-        assert _max == "a"
+        assert np.isnan(_max)
 
 
 class TestSeriesMode: