BUG: Concat with tz-aware and timedelta raises AttributeError

Author: sinhrks

doc/source/whatsnew/v0.18.1.txt

@@ -125,10 +125,13 @@ Bug Fixes
 
 
 
-
+- Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`)
 
 
 
 
 
 - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`)
+
+
+

pandas/core/common.py

@@ -2713,7 +2713,7 @@ def is_nonempty(x):
     # these are mandated to handle empties as well
     if 'datetime' in typs or 'datetimetz' in typs or 'timedelta' in typs:
         from pandas.tseries.common import _concat_compat
-        return _concat_compat(to_concat, axis=axis)
+        return _concat_compat(to_concat, axis=axis, typs=typs)
 
     elif 'sparse' in typs:
         from pandas.sparse.array import _concat_compat

pandas/tools/tests/test_merge.py

@@ -1161,6 +1161,20 @@ def test_concat_tz_series(self):
         result = pd.concat([first, second])
         self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]')
 
+    def test_concat_tz_series_with_datetimelike(self):
+        # GH 12620
+        # tz and timedelta
+        x = [pd.Timestamp('2011-01-01', tz='US/Eastern'),
+             pd.Timestamp('2011-02-01', tz='US/Eastern')]
+        y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')]
+        result = concat([pd.Series(x), pd.Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, pd.Series(x + y, dtype='object'))
+
+        # tz and period
+        y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')]
+        result = concat([pd.Series(x), pd.Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, pd.Series(x + y, dtype='object'))
+
     def test_concat_period_series(self):
         x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D'))
         y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D'))

pandas/tseries/common.py

@@ -238,7 +238,7 @@ class CombinedDatetimelikeProperties(DatetimeProperties, TimedeltaProperties):
     __doc__ = DatetimeProperties.__doc__
 
 
-def _concat_compat(to_concat, axis=0):
+def _concat_compat(to_concat, axis=0, typs=None):
     """
     provide concatenation of an datetimelike array of arrays each of which is a
     single M8[ns], datetimet64[ns, tz] or m8[ns] dtype
@@ -272,38 +272,33 @@ def convert_to_pydatetime(x, axis):
 
         return x
 
-    typs = get_dtype_kinds(to_concat)
+    if typs is None:
+        typs = get_dtype_kinds(to_concat)
 
-    # datetimetz
-    if 'datetimetz' in typs:
-
-        # if to_concat have 'datetime' or 'object'
-        # then we need to coerce to object
-        if 'datetime' in typs or 'object' in typs:
-            to_concat = [convert_to_pydatetime(x, axis) for x in to_concat]
-            return np.concatenate(to_concat, axis=axis)
+    # must be single dtype
+    if len(typs) == 1:
 
-        # we require ALL of the same tz for datetimetz
-        tzs = set([getattr(x, 'tz', None) for x in to_concat]) - set([None])
-        if len(tzs) == 1:
-            return DatetimeIndex(np.concatenate([x.tz_localize(None).asi8
-                                                 for x in to_concat]),
-                                 tz=list(tzs)[0])
+        if 'datetimetz' in typs:
+            # datetime with no tz should be stored as "datetime" in typs,
+            # thus no need to care
 
-    # single dtype
-    if len(typs) == 1:
+            # we require ALL of the same tz for datetimetz
+            tzs = set([x.tz for x in to_concat])
+            if len(tzs) == 1:
+                return DatetimeIndex(np.concatenate([x.tz_localize(None).asi8
+                                                     for x in to_concat]),
+                                     tz=list(tzs)[0])
 
-        if not len(typs - set(['datetime'])):
+        elif 'datetime' in typs:
             new_values = np.concatenate([x.view(np.int64) for x in to_concat],
                                         axis=axis)
             return new_values.view(_NS_DTYPE)
 
-        elif not len(typs - set(['timedelta'])):
+        elif 'timedelta' in typs:
             new_values = np.concatenate([x.view(np.int64) for x in to_concat],
                                         axis=axis)
             return new_values.view(_TD_DTYPE)
 
     # need to coerce to object
     to_concat = [convert_to_pydatetime(x, axis) for x in to_concat]
-
     return np.concatenate(to_concat, axis=axis)