10000 Series[datetime64[ns, tz]].__setitem__ fails to coerce for ndarray value · Issue #23932 · pandas-dev/pandas · GitHub
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Series[datetime64[ns, tz]].__setitem__ fails to coerce for ndarray value #23932

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TomAugspurger opened this issue Nov 26, 2018 · 2 comments · Fixed by #24024
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Series[datetime64[ns, tz]].__setitem__ fails to coerce for ndarray value #23932

TomAugspurger opened this issue Nov 26, 2018 · 2 comments · Fixed by #24024
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Datetime Datetime data dtype Dtype Conversions Unexpected or buggy dtype conversions ExtensionArray Extending pandas with custom dtypes or arrays. Indexing Related to indexing on series/frames, not to indexes themselves

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@TomAugspurger
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TomAugspurger commented Nov 26, 2018 •
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What's the expected output for

In [9]: a = pd.Series(pd.DatetimeIndex(['2000', '2000'], tz="UTC"))

In [10]: a.loc[:] = np.array([pd.Timestamp('2000', tz="UTC")] * 2)

On master, we coerce self to object and then set.

In [11]: a
Out[11]:
0    2000-01-01 00:00:00+00:00
1    2000-01-01 00:00:00+00:00
dtype: object

Should we instead coerce value, rather than self?
@jbrockmendel
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We should certainly at least try

@TomAugspurger TomAugspurger added Datetime Datetime data dtype Indexing Related to indexing on series/frames, not to indexes themselves Dtype Conversions Unexpected or buggy dtype conversions ExtensionArray Extending pandas with custom dtypes or arrays. labels Nov 27, 2018
@TomAugspurger
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TomAugspurger commented Nov 27, 2018 •
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FYI, I have this working on my all-in-one branch (hoping to have a PR tomorrow).

In [4]: a.loc[:] = np.array([pd.Timestamp('2000', tz="UTC")] * 2)

In [5]: a
Out[5]:
0    2000-01-01 00:00:00+00:00
1    2000-01-01 00:00:00+00:00
dtype: datetime64[ns, UTC]

@TomAugspurger TomAugspurger mentioned this issue Dec 12, 2018
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Datetime Datetime data dtype Dtype Conversions Unexpected or buggy dtype conversions ExtensionArray Extending pandas with custom dtypes or arrays. Indexing Related to indexing on series/frames, not to indexes themselves
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REF: DatetimeLikeArray TomAugspurger/pandas
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