Object dtype for empty describe (#27184)

Author: TomAugspurger

pandas/core/generic.py

@@ -9652,6 +9652,7 @@ def describe_categorical_1d(data):
             objcounts = data.value_counts()
             count_unique = len(objcounts[objcounts != 0])
             result = [data.count(), count_unique]
+            dtype = None
             if result[1] > 0:
                 top, freq = objcounts.index[0], objcounts.iloc[0]
 
@@ -9676,9 +9677,10 @@ def describe_categorical_1d(data):
             # to maintain output shape consistency
             else:
                 names += ['top', 'freq']
-                result += [None, None]
+                result += [np.nan, np.nan]
+                dtype = 'object'
 
-            return pd.Series(result, index=names, name=data.name)
+            return pd.Series(result, index=names, name=data.name, dtype=dtype)
 
         def describe_1d(data):
             if is_bool_dtype(data):

pandas/tests/frame/test_analytics.py

@@ -525,6 +525,17 @@ def test_bool_describe_in_mixed_frame(self):
                              index=['count', 'unique', 'top', 'freq'])
         tm.assert_frame_equal(result, expected)
 
+    def test_describe_empty_object(self):
+        # https://github.com/pandas-dev/pandas/issues/27183
+        df = pd.DataFrame({"A": [None, None]}, dtype=object)
+        result = df.describe()
+        expected = pd.DataFrame({"A": [0, 0, np.nan, np.nan]}, dtype=object,
+                                index=['count', 'unique', 'top', 'freq'])
+        tm.assert_frame_equal(result, expected)
+
+        result = df.iloc[:0].describe()
+        tm.assert_frame_equal(result, expected)
+
     def test_describe_bool_frame(self):
         # GH 13891
         df = pd.DataFrame({
@@ -590,13 +601,17 @@ def test_describe_categorical(self):
 
     def test_describe_empty_categorical_column(self):
         # GH 26397
-        # Ensure the index of an an empty categoric DataFrame column
+        # Ensure the index of an an empty categorical DataFrame column
         # also contains (count, unique, top, freq)
         df = pd.DataFrame({"empty_col": Categorical([])})
         result = df.describe()
-        expected = DataFrame({'empty_col': [0, 0, None, None]},
-                             index=['count', 'unique', 'top', 'freq'])
+        expected = DataFrame({'empty_col': [0, 0, np.nan, np.nan]},
+                             index=['count', 'unique', 'top', 'freq'],
+                             dtype='object')
         tm.assert_frame_equal(result, expected)
+        # ensure NaN, not None
+        assert np.isnan(result.iloc[2, 0])
+        assert np.isnan(result.iloc[3, 0])
 
     def test_describe_categorical_columns(self):
         # GH 11558

pandas/tests/series/test_analytics.py

@@ -42,6 +42,20 @@ def test_describe(self):
                           index=['count', 'unique', 'top', 'freq'])
         tm.assert_series_equal(result, expected)
 
+    def test_describe_empty_object(self):
+        # https://github.com/pandas-dev/pandas/issues/27183
+        s = pd.Series([None, None], dtype=object)
+        result = s.describe()
+        expected = pd.Series([0, 0, np.nan, np.nan], dtype=object,
+                             index=['count', 'unique', 'top', 'freq'])
+        tm.assert_series_equal(result, expected)
+
+        result = s[:0].describe()
+        tm.assert_series_equal(result, expected)
+        # ensure NaN, not None
+        assert np.isnan(result.iloc[2])
+        assert np.isnan(result.iloc[3])
+
     def test_describe_with_tz(self, tz_naive_fixture):
         # GH 21332
         tz = tz_naive_fixture