0

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!

oguz ismail
  • 1
  • 16
  • 47
  • 69
till Kadabra
  • 478
  • 9
  • 21
  • 1
    [`to_datetime()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html) – 9769953 Oct 17 '21 at 11:33

1 Answers1

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)
SeaBean
  • 22,547
  • 3
  • 13
  • 25