pandas.Series.dt.tz_convert#

Series.dt.tz_convert(*args, **kwargs)[source]#

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

Parameters:

tzstr, zoneinfo.ZoneInfo, 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.

Returns:

Array or Index: Datetme Array/Index with target tz.

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"
... )

>>> 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')

>>> 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')

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"
... )

>>> 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')

>>> 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')