Joris Van den Bossche created ARROW-6749:
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Summary: [Python] Conversion of non-ns timestamp array to numpy
gives wrong values
Key: ARROW-6749
URL: https://issues.apache.org/jira/browse/ARROW-6749
Project: Apache Arrow
Issue Type: Bug
Components: Python
Reporter: Joris Van den Bossche
{code}
In [25]: np_arr = np.arange("2012-01-01", "2012-01-06", int(1e6)*60*60*24,
dtype="datetime64[us]")
In [26]: np_arr
Out[26]:
array(['2012-01-01T00:00:00.000000', '2012-01-02T00:00:00.000000',
'2012-01-03T00:00:00.000000', '2012-01-04T00:00:00.000000',
'2012-01-05T00:00:00.000000'], dtype='datetime64[us]')
In [27]: arr = pa.array(np_arr)
In [28]: arr
Out[28]:
<pyarrow.lib.TimestampArray object at 0x7f0b2ef07ee8>
[
2012-01-01 00:00:00.000000,
2012-01-02 00:00:00.000000,
2012-01-03 00:00:00.000000,
2012-01-04 00:00:00.000000,
2012-01-05 00:00:00.000000
]
In [29]: arr.type
Out[29]: TimestampType(timestamp[us])
In [30]: arr.to_numpy()
Out[30]:
array(['1970-01-16T08:09:36.000000000', '1970-01-16T08:11:02.400000000',
'1970-01-16T08:12:28.800000000', '1970-01-16T08:13:55.200000000',
'1970-01-16T08:15:21.600000000'], dtype='datetime64[ns]')
{code}
So it seems to simply interpret the integer microsecond values as nanoseconds
when converting to numpy.
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