convert numpy array of float64 values to datetime64 with python and pandas

Question

If I have a NumPy array of float64 values. I know these values represent dates in format of datetime64[ns]. I try to convert them with pandas. But I get an ValueError: Inferred frequency 86400N from passed values does not conform to passed frequency N

time = np.array([1420156200.0,1420242600.0,1420329000.0], dtype='float64')
pd.DatetimeIndex(time, freq='ns')

The time values must be '2015-01-01T23:50:00.000000000', '2015-01-02T23:50:00.000000000', '2015-01-03T23:50:00.000000000'. How can i archive this? Thanks!

to_datetime()

9769953
– 9769953

2021-10-17 11:33:56 +00:00
Commented Oct 17, 2021 at 11:33 — 9769953
– 9769953, Commented Oct 17, 2021 at 11:33

SeaBean · Accepted Answer · 2021-10-17 12:51:18Z

1

You can use pd.to_datetime() with unit s, as follows:

time = np.array([1420156200.0,1420242600.0,1420329000.0], dtype='float64')
pd.to_datetime(time, unit='s')

Result:

DatetimeIndex(['2015-01-01 23:50:00', '2015-01-02 23:50:00',
               '2015-01-03 23:50:00'],
              dtype='datetime64[ns]', freq=None)

answered Oct 17, 2021 at 12:51

SeaBean

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