Pandas Series: dt.tz_convert() function

Last update on September 19 2022 12:31:32 (UTC/GMT +8 hours)

Series.dt.tz_convert() function

The tz_convert() function is used to convert tz-aware Datetime Array/Index from one time zone to another.

Syntax:

Series.dt.tz_convert(self, *args, **kwargs)

Parameter:

Name	Description	Type / Default Value	Required / Optional
tz	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.	str, pytz.timezone, dateutil.tz.tzfile or None	Required

Returns: Array or Index

Raises: TypeError
If Datetime Array/Index is tz-naive.

Example - With the tz parameter, you can change the DatetimeIndex to other time zones:

Python-Pandas Code:

import numpy as np
import pandas as pd
dti = pd.date_range(start='2019-08-01 09:00',
                    freq='H', periods=3, tz='Europe/Berlin')
dti

Output:

DatetimeIndex(['2019-08-01 09:00:00+02:00', '2019-08-01 10:00:00+02:00',
               '2019-08-01 11:00:00+02:00'],
              dtype='datetime64[ns, Europe/Berlin]', freq='H')

Python-Pandas Code:

import numpy as np
import pandas as pd
dti = pd.date_range(start='2019-08-01 09:00',
                    freq='H', periods=3, tz='Europe/Berlin')
dti.tz_convert('US/Central')

Output:

DatetimeIndex(['2019-08-01 02:00:00-05:00', '2019-08-01 03:00:00-05:00',
               '2019-08-01 04:00:00-05:00'],
              dtype='datetime64[ns, US/Central]', freq='H')

Example - With the tz=None, we can remove the timezone (after converting to UTC if necessary):

Python-Pandas Code:

import numpy as np
import pandas as pd
dti = pd.date_range(start='2019-08-01 09:00', freq='H',
                    periods=3, tz='Europe/Berlin')
dti

Output:

DatetimeIndex(['2019-08-01 09:00:00+02:00', '2019-08-01 10:00:00+02:00',
               '2019-08-01 11:00:00+02:00'],
              dtype='datetime64[ns, Europe/Berlin]', freq='H')

Python-Pandas Code:

import numpy as np
import pandas as pd
dti = pd.date_range(start='2019-08-01 09:00', freq='H',
                    periods=3, tz='Europe/Berlin')
dti.tz_convert(None)

Output:

DatetimeIndex(['2019-08-01 07:00:00', '2019-08-01 08:00:00',
               '2019-08-01 09:00:00'],
              dtype='datetime64[ns]', freq='H')

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