[BUG-FIX] DataFrame created with tzinfo cannot use to_dict(orient="records") (#18416)

Closes #18372

Author: bolkedebruin

doc/source/whatsnew/v0.21.1.txt

@@ -63,7 +63,7 @@ Conversion
 - Bug in :class:`TimedeltaIndex` subtraction could incorrectly overflow when ``NaT`` is present (:issue:`17791`)
 - Bug in :class:`DatetimeIndex` subtracting datetimelike from DatetimeIndex could fail to overflow (:issue:`18020`)
 - Bug in :meth:`IntervalIndex.copy` when copying and ``IntervalIndex`` with non-default ``closed`` (:issue:`18339`)
--
+- Bug in :func:`DataFrame.to_dict` where columns of datetime that are tz-aware were not converted to required arrays when used with ``orient='records'``, raising``TypeError` (:issue:`18372`)
 -
 -
 

pandas/core/frame.py

@@ -993,7 +993,7 @@ def to_dict(self, orient='dict', into=dict):
                           for k, v in compat.iteritems(self))
         elif orient.lower().startswith('r'):
             return [into_c((k, _maybe_box_datetimelike(v))
-                           for k, v in zip(self.columns, row))
+                           for k, v in zip(self.columns, np.atleast_1d(row)))
                     for row in self.values]
         elif orient.lower().startswith('i'):
             return into_c((k, v.to_dict(into)) for k, v in self.iterrows())

pandas/tests/frame/test_convert_to.py

@@ -1,6 +1,9 @@
 # -*- coding: utf-8 -*-
 
+from datetime import datetime
+
 import pytest
+import pytz
 import collections
 import numpy as np
 
@@ -249,3 +252,18 @@ def test_to_dict_box_scalars(self):
 
         result = DataFrame(d).to_dict(orient='records')
         assert isinstance(result[0]['a'], (int, long))
+
+    def test_frame_to_dict_tz(self):
+        # GH18372 When converting to dict with orient='records' columns of
+        # datetime that are tz-aware were not converted to required arrays
+        data = [(datetime(2017, 11, 18, 21, 53, 0, 219225, tzinfo=pytz.utc),),
+                (datetime(2017, 11, 18, 22, 6, 30, 61810, tzinfo=pytz.utc,),)]
+        df = DataFrame(list(data), columns=["d", ])
+
+        result = df.to_dict(orient='records')
+        expected = [
+            {'d': Timestamp('2017-11-18 21:53:00.219225+0000', tz=pytz.utc)},
+            {'d': Timestamp('2017-11-18 22:06:30.061810+0000', tz=pytz.utc)},
+        ]
+        tm.assert_dict_equal(result[0], expected[0])
+        tm.assert_dict_equal(result[1], expected[1])