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https://issues.apache.org/jira/browse/ARROW-6305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joris Van den Bossche closed ARROW-6305.
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Resolution: Duplicate
> [Python] scalar pd.NaT incorrectly parsed in conversion from Python
> -------------------------------------------------------------------
>
> Key: ARROW-6305
> URL: https://issues.apache.org/jira/browse/ARROW-6305
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Reporter: Joris Van den Bossche
> Priority: Major
>
> When converting from scalar values, using {{pd.NaT}} (the missing value
> indicator that pandas uses for datetime64 data) results in an incorrect
> timestamp:
> {code}
> In [6]: pa.array([pd.Timestamp("2012-01-01"), pd.NaT])
> Out[6]:
> <pyarrow.lib.TimestampArray object at 0x7f46c8368780>
> [
> 2012-01-01 00:00:00.000000,
> 0001-01-01 00:00:00.000000
> ]
> {code}
> where {{pd.NaT}} is converted to "0001-01-01", which is strange, as that does
> not even correspond with the integer value of pd.NaT.
> Numpy's version ({{np.datetime64('NaT')}}) is correctly handled. Which also
> means that a pandas Series holding pd.NaT is handled correctly (as when
> converting to numpy it is using numpy's NaT).
> Related to ARROW-842.
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