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https://issues.apache.org/jira/browse/ARROW-3899?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wes McKinney resolved ARROW-3899.
---------------------------------
Resolution: Fixed
Assignee: Wes McKinney
Fix Version/s: (was: 0.13.0)
0.12.0
This was resolved in 0.12. On master now
{code}
In [4]: arr = pa.array([date(2001, 1, 1), None, date(2001, 1, 2)])
In [5]: arr
Out[5]:
<pyarrow.lib.Date32Array object at 0x7f033ceb7ae8>
[
11323,
null,
11324
]
In [6]: arr.to_pandas()
Out[6]:
array([datetime.date(2001, 1, 1), None, datetime.date(2001, 1, 2)],
dtype=object)
{code}
> [Python] Table.to_pandas converts Arrow date32[day] to pandas datetime64[ns]
> ----------------------------------------------------------------------------
>
> Key: ARROW-3899
> URL: https://issues.apache.org/jira/browse/ARROW-3899
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Wes McKinney
> Assignee: Wes McKinney
> Priority: Major
> Fix For: 0.12.0
>
>
> This issue was raised here:
> https://github.com/wesm/feather/issues/359
> I explored this minimally against Arrow master:
> https://gist.github.com/wesm/2ebe0ca2461d1ecfba6185777238ad1f
> While it's pretty memory-wasteful, it might be better to preserve the intent
> of the data type when converting to pandas data structures. It also allows
> the data to round trip successfully
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