BUG: Fix to_dict() problem when using datetime DataFrame #11247

doc/source/whatsnew/v0.17.1.txt

@@ -96,3 +96,4 @@ Bug Fixes
 - Bugs in ``to_excel`` with duplicate columns (:issue:`11007`, :issue:`10982`, :issue:`10970`)
 - Fixed a bug that prevented the construction of an empty series of dtype
   ``datetime64[ns, tz]`` (:issue:`11245`).
+- Bug in ``DataFrame.to_dict()`` produces an datetime object instead of Timestamp when only datetime is present in data (:issue:`11327`)

pandas/core/frame.py

@@ -802,11 +802,12 @@ def to_dict(self, orient='dict'):
         elif orient.lower().startswith('sp'):
             return {'index': self.index.tolist(),
                     'columns': self.columns.tolist(),
-                    'data': self.values.tolist()}
+                    'data': lib.map_infer(self.values.ravel(), _maybe_box_datetimelike)
+                    .reshape(self.values.shape).tolist()}
         elif orient.lower().startswith('s'):
-            return dict((k, v) for k, v in compat.iteritems(self))
+            return dict((k, _maybe_box_datetimelike(v)) for k, v in compat.iteritems(self))
         elif orient.lower().startswith('r'):
-            return [dict((k, v) for k, v in zip(self.columns, row))
+            return [dict((k, _maybe_box_datetimelike(v)) for k, v in zip(self.columns, row))
                     for row in self.values]
         elif orient.lower().startswith('i'):
             return dict((k, v.to_dict()) for k, v in self.iterrows())

pandas/tests/test_frame.py

@@ -4728,6 +4728,55 @@ def test_to_dict(self):
             for k2, v2 in compat.iteritems(v):
                 self.assertEqual(v2, recons_data[k2][k])
 
+    def test_to_dict_timestamp(self):
+        # GH11247
+        tsmp = Timestamp('20130101')
+        test_data = DataFrame({'A': [tsmp, tsmp], 'B': [tsmp, tsmp]})
+        test_data_mixed = DataFrame({'A': [tsmp, tsmp], 'B': [1, 2]})
+
+        expected_records = [{'A': tsmp, 'B': tsmp},
+                            {'A': tsmp, 'B': tsmp}]
+        expected_records_mixed = [{'A': tsmp, 'B': 1},
+                            {'A': tsmp, 'B': 2}]
+
+        tm.assert_almost_equal(test_data.to_dict(
+            orient='records'), expected_records)
+        tm.assert_almost_equal(test_data_mixed.to_dict(
+            orient='records'), expected_records_mixed)
+
+        expected_series = {
+            'A': Series([tsmp, tsmp]),
+            'B': Series([tsmp, tsmp]),
+        }
+        expected_series_mixed = {
+            'A': Series([tsmp, tsmp]),
+            'B': Series([1, 2]),
+        }
+
+        tm.assert_almost_equal(test_data.to_dict(
+            orient='series'), expected_series)
+        tm.assert_almost_equal(test_data_mixed.to_dict(
+            orient='series'), expected_series_mixed)
+
+        expected_split = {
+            'index': [0, 1],
+            'data': [[tsmp, tsmp],
+                     [tsmp, tsmp]],
+            'columns': ['A', 'B']
+        }
+        expected_split_mixed = {
+            'index': [0, 1],
+            'data': [[tsmp, 1],
+                     [tsmp, 2]],
+            'columns': ['A', 'B']
+        }
+
+        tm.assert_almost_equal(test_data.to_dict(
+            orient='split'), expected_split)
+        tm.assert_almost_equal(test_data_mixed.to_dict(
+            orient='split'), expected_split_mixed)
+
+
     def test_to_dict_invalid_orient(self):
         df = DataFrame({'A':[0, 1]})
         self.assertRaises(ValueError, df.to_dict, orient='xinvalid')