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BUG: Fixed merging on tz-aware #25033

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Jan 30, 2019
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BUG: Fixed merging on tz-aware #25033

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58f2892
BUG: Fixed merging on tz-aware
TomAugspurger Jan 30, 2019
8bd020f
fix earlier
TomAugspurger Jan 30, 2019
43706fb
Unbox NaT for numpy
TomAugspurger Jan 30, 2019
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.24.1.rst
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Expand Up @@ -23,6 +23,7 @@ Fixed Regressions
- Bug in :meth:`DataFrame.itertuples` with ``records`` orient raising an ``AttributeError`` when the ``DataFrame`` contained more than 255 columns (:issue:`24939`)
- Bug in :meth:`DataFrame.itertuples` orient converting integer column names to strings prepended with an underscore (:issue:`24940`)
- Fixed regression in :class:`Index.intersection` incorrectly sorting the values by default (:issue:`24959`).
- Fixed regression in :func:`merge` when merging an empty ``DataFrame`` with multiple timezone-aware columns on one of the timezone-aware columns (:issue:`25014`).

.. _whatsnew_0241.enhancements:

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4 changes: 3 additions & 1 deletion pandas/core/internals/concat.py
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Expand Up @@ -335,8 +335,10 @@ def get_empty_dtype_and_na(join_units):
elif 'category' in upcast_classes:
return np.dtype(np.object_), np.nan
elif 'datetimetz' in upcast_classes:
# GH-25014. We use NaT instead of iNaT, since this eventually
# ends up in DatetimeArray.take, which does not allow iNaT.
dtype = upcast_classes['datetimetz']
return dtype[0], tslibs.iNaT
return dtype[0], tslibs.NaT
elif 'datetime' in upcast_classes:
return np.dtype('M8[ns]'), tslibs.iNaT
elif 'timedelta' in upcast_classes:
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18 changes: 18 additions & 0 deletions pandas/tests/reshape/merge/test_merge.py
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Expand Up @@ -616,6 +616,24 @@ def test_merge_on_datetime64tz(self):
assert result['value_x'].dtype == 'datetime64[ns, US/Eastern]'
assert result['value_y'].dtype == 'datetime64[ns, US/Eastern]'

def test_merge_on_datetime64tz_empty(self):
# https://github.com/pandas-dev/pandas/issues/25014
dtz = pd.DatetimeTZDtype(tz='UTC')
right = pd.DataFrame({'date': [pd.Timestamp('2018', tz=dtz.tz)],
'value': [4.0],
'date2': [pd.Timestamp('2019', tz=dtz.tz)]},
columns=['date', 'value', 'date2'])
left = right[:0]
result = left.merge(right, on='date')
expected = pd.DataFrame({
'value_x': pd.Series(dtype=float),
'date2_x': pd.Series(dtype=dtz),
'date': pd.Series(dtype=dtz),
'value_y': pd.Series(dtype=float),
'date2_y': pd.Series(dtype=dtz),
}, columns=['value_x', 'date2_x', 'date', 'value_y', 'date2_y'])
tm.assert_frame_equal(result, expected)

def test_merge_datetime64tz_with_dst_transition(self):
# GH 18885
df1 = pd.DataFrame(pd.date_range(
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