modin.pandas.Series.dt.tz_convert¶

Series.dt.tz_convert[source]¶

Convert tz-aware Datetime Array/Index from one time zone to another.

Parameters:

tz (str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None) – Time zone for time. Corresponding timestamps would be converted to this time zone of the Datetime Array/Index. A tz of None will convert to UTC and remove the timezone information.

Return type:

Array or Index

Raises:

  • TypeError – If Datetime Array/Index is tz-naive.

  • See also –

  • DatetimeIndex.tz – A timezone that has a variable offset from UTC.

  • DatetimeIndex.tz_localize – Localize tz-naive DatetimeIndex to a given time zone, or remove timezone from a tz-aware DatetimeIndex.

Examples

With the tz parameter, we can change the DatetimeIndex to other time zones:

>>> dti = pd.date_range(start='2014-08-01 09:00',
...                     freq='h', periods=3, tz='Europe/Berlin')
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>>> dti  
DatetimeIndex(['2014-08-01 09:00:00+02:00',
               '2014-08-01 10:00:00+02:00',
               '2014-08-01 11:00:00+02:00'],
              dtype='datetime64[ns, Europe/Berlin]', freq='h')
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>>> dti.tz_convert('US/Central')  
DatetimeIndex(['2014-08-01 02:00:00-05:00',
               '2014-08-01 03:00:00-05:00',
               '2014-08-01 04:00:00-05:00'],
              dtype='datetime64[ns, US/Central]', freq='h')
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With the tz=None, we can remove the timezone (after converting to UTC if necessary):

>>> dti = pd.date_range(start='2014-08-01 09:00', freq='h',
...                     periods=3, tz='Europe/Berlin')
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>>> dti  
DatetimeIndex(['2014-08-01 09:00:00+02:00',
               '2014-08-01 10:00:00+02:00',
               '2014-08-01 11:00:00+02:00'],
              dtype='datetime64[ns, Europe/Berlin]', freq='h')
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>>> dti.tz_convert(None)  
DatetimeIndex(['2014-08-01 07:00:00',
               '2014-08-01 08:00:00',
               '2014-08-01 09:00:00'],
              dtype='datetime64[ns]', freq='h')
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