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BUG: concat sorting when sort=False for all dti #51683

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BUG: concat sorting when sort=False for all dti #51683

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5ae4d91
BUG: concat sorting when sort=False for all dti
phofl Feb 28, 2023
cf3b2b7
Fix pylint
phofl Feb 28, 2023
f56f172
Merge branch 'main' into 51210
phofl Mar 4, 2023
f4d3426
Merge remote-tracking branch 'upstream/main' into 51210
phofl Mar 7, 2023
f68bd9b
Merge branch 'main' into 51210
phofl Mar 14, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
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Expand Up @@ -208,6 +208,7 @@ Groupby/resample/rolling
Reshaping
^^^^^^^^^
- Bug in :meth:`DataFrame.stack` losing extension dtypes when columns is a :class:`MultiIndex` and frame contains mixed dtypes (:issue:`45740`)
- Bug in :func:`concat` sorting :class:`Index` when all passed objects have :class:`DatetimeIndex` indexes (:issue:`51210`)
- Bug in :meth:`DataFrame.transpose` inferring dtype for object column (:issue:`51546`)
- Bug in :meth:`Series.combine_first` converting ``int64`` dtype to ``float64`` and losing precision on very large integers (:issue:`51764`)
-
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1 change: 0 additions & 1 deletion pandas/core/indexes/api.py
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Expand Up @@ -280,7 +280,6 @@ def _find_common_index_dtype(inds):
raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")

if len(dtis) == len(indexes):
sort = True
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I wonder if the sort = False below also causes a similar issue

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Based on the comment it looks like this is necessary, but yes this probably causes trouble as well

result = indexes[0]

elif len(dtis) > 1:
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12 changes: 6 additions & 6 deletions pandas/tests/reshape/concat/test_datetimes.py
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Expand Up @@ -77,23 +77,23 @@ def test_concat_datetime_timezone(self):

exp_idx = DatetimeIndex(
[
"2010-12-31 15:00:00+00:00",
"2010-12-31 16:00:00+00:00",
"2010-12-31 17:00:00+00:00",
"2010-12-31 23:00:00+00:00",
"2011-01-01 00:00:00+00:00",
"2011-01-01 01:00:00+00:00",
"2010-12-31 15:00:00+00:00",
"2010-12-31 16:00:00+00:00",
"2010-12-31 17:00:00+00:00",
]
)

expected = DataFrame(
[
[np.nan, 1],
[np.nan, 2],
[np.nan, 3],
[1, np.nan],
[2, np.nan],
[3, np.nan],
[np.nan, 1],
[np.nan, 2],
[np.nan, 3],
],
index=exp_idx,
columns=["a", "b"],
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