address review

Author: mroeschke

doc/source/whatsnew/v0.23.0.txt

@@ -896,7 +896,7 @@ Timezones
 - Bug in :func:`Timestamp.tz_localize` where localizing a timestamp near the minimum or maximum valid values could overflow and return a timestamp with an incorrect nanosecond value (:issue:`12677`)
 - Bug when iterating over :class:`DatetimeIndex` that was localized with fixed timezone offset that rounded nanosecond precision to microseconds (:issue:`19603`)
 - Bug in :func:`DataFrame.diff` that raised an ``IndexError`` with tz-aware values (:issue:`18578`)
-- Bug in :func:`melt` that coverted tz-aware dtypes to tz-naive (:issue:`15785`)
+- Bug in :func:`melt` that converted tz-aware dtypes to tz-naive (:issue:`15785`)
 
 Offsets
 ^^^^^^^

pandas/core/reshape/melt.py

@@ -74,7 +74,6 @@ def melt(frame, id_vars=None, value_vars=None, var_name=None,
     for col in id_vars:
         id_data = frame.pop(col)
         if is_extension_type(id_data):
-            # Preserve pandas dtype by not converting to a numpy array
             id_data = concat([id_data] * K, ignore_index=True)
         else:
             id_data = np.tile(id_data.values, K)

pandas/tests/reshape/test_melt.py

@@ -214,22 +214,16 @@ def test_multiindex(self):
 
     @pytest.mark.parametrize("col", [
         pd.Series(pd.date_range('2010', periods=5, tz='US/Pacific')),
-        pd.Series(["a", "b", "c", "a", "d"], dtype="category")])
-    @pytest.mark.parametrize("pandas_dtype_value", [True, False])
-    def test_pandas_dtypes_id_var(self, col, pandas_dtype_value):
+        pd.Series(["a", "b", "c", "a", "d"], dtype="category"),
+        pd.Series([0, 1, 0, 0, 0])])
+    def test_pandas_dtypes(self, col):
         # GH 15785
-        # Pandas dtype in the id
         df = DataFrame({'klass': range(5),
                         'col': col,
-                        'attr1': [1, 0, 0, 0, 0]})
-        if pandas_dtype_value:
-            # Pandas dtype in the value as well
-            df['attr2'] = col
-            expected_value = pd.concat([pd.Series([1, 0, 0, 0, 0]), col],
-                                       ignore_index=True)
-        else:
-            df['attr2'] = [0, 1, 0, 0, 0]
-            expected_value = [1, 0, 0, 0, 0] + [0, 1, 0, 0, 0]
+                        'attr1': [1, 0, 0, 0, 0],
+                        'attr2': col})
+        expected_value = pd.concat([pd.Series([1, 0, 0, 0, 0]), col],
+                                   ignore_index=True)
         result = melt(df, id_vars=['klass', 'col'], var_name='attribute',
                       value_name='value')
         expected = DataFrame({0: list(range(5)) * 2,