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BUG: NaT instead of error for timestamp concat with None #53042

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BUG: NaT instead of error for timestamp concat with None #53042

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d75762d
BUG: NaT instead of error for timestamp
srkds May 2, 2023
e17bd97
Merge remote-tracking branch 'upstream/main' into bug/52093/concat_no…
srkds May 3, 2023
8804de4
Merge remote-tracking branch 'upstream/main' into bug/52093/concat_no…
srkds May 8, 2023
c1c5675
Timestamp
srkds May 8, 2023
634e522
Merge remote-tracking branch 'upstream/main' into bug/52093/concat_no…
srkds May 9, 2023
4171a7c
whatsnew entry
srkds May 9, 2023
11db8f1
Resolved merge conflict: accepted both changes
srkds Jun 26, 2023
416f4a8
added actual bug test case
srkds Jun 26, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -309,6 +309,7 @@ Categorical
Datetimelike
^^^^^^^^^^^^
- :meth:`DatetimeIndex.map` with ``na_action="ignore"`` now works as expected. (:issue:`51644`)
- Bug in :func:`concat` raises ``AttributeError`` when concate ``None`` dtype DataFrame with ``timestamp`` dtype DataFrame. (:issue:`52093`)
- Bug in :func:`date_range` when ``freq`` was a :class:`DateOffset` with ``nanoseconds`` (:issue:`46877`)
- Bug in :meth:`Timestamp.round` with values close to the implementation bounds returning incorrect results instead of raising ``OutOfBoundsDatetime`` (:issue:`51494`)
- Bug in :meth:`arrays.DatetimeArray.map` and :meth:`DatetimeIndex.map`, where the supplied callable operated array-wise instead of element-wise (:issue:`51977`)
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3 changes: 2 additions & 1 deletion pandas/core/internals/managers.py
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Expand Up @@ -26,6 +26,7 @@
BlockPlacement,
BlockValuesRefs,
)
from pandas._libs.tslibs import Timestamp
from pandas.errors import PerformanceWarning
from pandas.util._decorators import cache_readonly
from pandas.util._exceptions import find_stack_level
Expand Down Expand Up @@ -2405,7 +2406,7 @@ def _preprocess_slice_or_indexer(
def make_na_array(dtype: DtypeObj, shape: Shape, fill_value) -> ArrayLike:
if isinstance(dtype, DatetimeTZDtype):
# NB: exclude e.g. pyarrow[dt64tz] dtypes
i8values = np.full(shape, fill_value._value)
i8values = np.full(shape, Timestamp(fill_value)._value)
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i think this may mess up if your dtype has a different unit than the fill_value. can you add a test where this is the case? also needs a test for the original bug

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also needs a test for the original bug

My bad! I forgot to add that. Now added that👍
Thanks for the reminding.

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i think this may mess up if your dtype has a different unit than the fill_value. can you add a test where this is the case?

Are you talking about something like this? where the value will be timestamp but its dtype will be different?

df = pd.DataFrame([{'A':None}])
df_2 = pd.DataFrame([{'A': pd.to_datetime("1990-12-20 00:00:00+00:00")}], dtype="int32")
cd = pd.concat([df, df_2])

sorry! if I did not get it correctly. Could you please explain it in detail?

return DatetimeArray(i8values, dtype=dtype)

elif is_1d_only_ea_dtype(dtype):
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