pandas.Series.tz_convert#

Series.tz_convert(tz, axis=0, level=None, copy=<no_default>)[source]#

Convert tz-aware axis to target time zone.

Parameters:

tzstr or tzinfo object or None: Target time zone. Passing None will convert to UTC and remove the timezone information.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to convert
levelint, str, default None: If axis is a MultiIndex, convert a specific level. Otherwise must be None.
copybool, default False: Also make a copy of the underlying data.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Deprecated since version 3.0.0.

Returns:

Series/DataFrame: Object with time zone converted axis.

Raises:

TypeError: If the axis is tz-naive.

See also

DataFrame.tz_localize: Localize tz-naive index of DataFrame to target time zone.
Series.tz_localize: Localize tz-naive index of Series to target time zone.

Examples

Change to another time zone:

>>> s = pd.Series(
...     [1],
...     index=pd.DatetimeIndex(["2018-09-15 01:30:00+02:00"]),
... )
>>> s.tz_convert("Asia/Shanghai")
2018-09-15 07:30:00+08:00    1
dtype: int64

Pass None to convert to UTC and get a tz-naive index:

>>> s = pd.Series([1], index=pd.DatetimeIndex(["2018-09-15 01:30:00+02:00"]))
>>> s.tz_convert(None)
2018-09-14 23:30:00    1
dtype: int64