Python | Pandas Series.tz_localize

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.tz_localize() function is used to localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. In order to localize the values in a timezone-naive Series, we can use Series.dt.tz_localize().

Syntax: Series.tz_localize(tz, axis=0, level=None, copy=True, ambiguous=’raise’, nonexistent=’raise’) Parameter : tz : string or pytz.timezone object axis : the axis to localize level : If axis is a MultiIndex, localize a specific level. Otherwise must be None copy : Also make a copy of the underlying data ambiguous : ‘infer’, bool-ndarray, ‘NaT’, default ‘raise’ nonexistent : str, default ‘raise’ Returns : Series or DataFrame

Example #1: Use Series.tz_localize() function to localize the time zone naive index of the given Series to the target time zone. 

Output : Now we will use Series.tz_localize() function to localize the given time zone naive index to time zone aware index. The target time zone is ‘US/Central’. 

Output : As we can see in the output, the Series.tz_localize() function has converted the given naive time zone index to a time aware index.   Example #2: Use Series.tz_localize() function to localize the time zone naive index of the given Series to the target time zone. 

Output : Now we will use Series.tz_localize() function to localize the given time zone naive index to time zone aware index. The target time zone is ‘Asia/Calcutta’. 

Output :