Joris Van den Bossche created ARROW-6305:
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Summary: [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
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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