Address comments and confirm timezone tests

Author: mroeschke

doc/source/whatsnew/v0.21.0.txt

@@ -303,13 +303,13 @@ length 2+ levels, so a :class:`MultiIndex` is always returned from all of the
 UTC Localization with Series
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Previously, :func:`to_datetime` did not localize datetime ``Series`` data as when ``utc=True`` was passed. Now, :func:`to_datetime` will correctly localize `Series` with a `datetime64[ns, UTC]` data type. (:issue:`6415`).
+Previously, :func:`to_datetime` did not localize datetime ``Series`` data when ``utc=True`` was passed. Now, :func:`to_datetime` will correctly localize `Series` with a `datetime64[ns, UTC]` data type to be consistent with how list-like and Index data are handled. (:issue:`6415`).
 
   Previous Behavior
 
   .. ipython:: python
 
-     s = Series(['20130101 00:00:00'] * 10)
+     s = Series(['20130101 00:00:00'] * 3)
 
   .. code-block:: python
 
@@ -318,13 +318,6 @@ Previously, :func:`to_datetime` did not localize datetime ``Series`` data as whe
      0   2013-01-01
      1   2013-01-01
      2   2013-01-01
-     3   2013-01-01
-     4   2013-01-01
-     5   2013-01-01
-     6   2013-01-01
-     7   2013-01-01
-     8   2013-01-01
-     9   2013-01-01
      dtype: datetime64[ns]
 
   New Behavior

pandas/core/tools/datetimes.py

@@ -516,7 +516,7 @@ def _convert_listlike(arg, box, format, name=None, tz=tz):
         result = arg
     elif isinstance(arg, ABCSeries):
         from pandas import Series
-        values = _convert_listlike(arg, True, format)
+        values = _convert_listlike(arg._values, True, format)
         result = Series(values, index=arg.index, name=arg.name)
     elif isinstance(arg, (ABCDataFrame, MutableMapping)):
         result = _assemble_from_unit_mappings(arg, errors=errors)

pandas/io/sql.py

@@ -818,6 +818,7 @@ def _harmonize_columns(self, parse_dates=None):
                 df_col = self.frame[col_name]
                 # the type the dataframe column should have
                 col_type = self._get_dtype(sql_col.type)
+
                 if (col_type is datetime or col_type is date or
                         col_type is DatetimeTZDtype):
                     if col_type is DatetimeTZDtype:

pandas/tests/indexes/datetimes/test_tools.py

@@ -272,11 +272,13 @@ def test_to_datetime_utc_is_true(self):
 
     @pytest.mark.parametrize("init_constructor, end_constructor, test_method",
         [(Index, DatetimeIndex, tm.assert_index_equal),
+         (list, DatetimeIndex, tm.assert_index_equal),
+         (np.array, DatetimeIndex, tm.assert_index_equal),
          (Series, Series, tm.assert_series_equal)])
-    def test_to_datetime_utc_true_with_series(self, 
-                                              init_constructor, 
-                                              end_constructor, 
-                                              test_method):
+    def test_to_datetime_utc_true_with_constructors(self, 
+                                                    init_constructor, 
+                                                    end_constructor, 
+                                                    test_method):
         # GH 6415: UTC=True with Series
         data = ['20100102 121314', '20100102 121315']
         expected_data = [pd.Timestamp('2010-01-02 12:13:14', tz='utc'),

pandas/tests/io/test_sql.py

@@ -1277,6 +1277,13 @@ def check(col):
                 # "2000-06-01 07:00:00"
                 assert col[1] == Timestamp('2000-06-01 07:00:00', tz='UTC')
 
+                # Double check that the Series has been localized correctly
+                # GH 6415
+                expected_data = [Timestamp('2000-01-01 08:00:00', tz='UTC'),
+                                 Timestamp('2000-06-01 07:00:00', tz='UTC')]
+                expected = Series(expected_data)
+                tm.assert_series_equal(col, expected)
+
             else:
                 raise AssertionError("DateCol loaded with incorrect type "
                                      "-> {0}".format(col.dtype))
@@ -1388,8 +1395,8 @@ def test_datetime_date(self):
         df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"])
         df.to_sql('test_date', self.conn, index=False)
         res = read_sql_table('test_date', self.conn)
-        expected = res['a']
-        result = to_datetime(df['a'])
+        result = res['a']
+        expected = to_datetime(df['a'])
         # comes back as datetime64
         tm.assert_series_equal(result, expected)