Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.tz attribute return the timezone if any, else it return None.
Syntax: Series.dt.tz
Parameter : None
Returns : timezone
Example #1: Use Series.dt.tz attribute to find the timezone of the underlying datetime based data in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['2012-12-31', '2019-1-1 12:30', '2008-02-2 10:30', '2010-1-1 09:25', '2019-12-31 00:00']) # Creating the index idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'] # set the index sr.index = idx # Convert the underlying data to datetime sr = pd.to_datetime(sr) # Print the series print(sr) |
Output :
Now we will use Series.dt.tz attribute to find the timezone of the datetime data in the given series object.
# find the timezone result = sr.dt.tz # print the result print(result) |
Output :
As we can see in the output, the Series.dt.tz attribute has returned None indicating the timezone for the given datetime data is not known.
Example #2 : Use Series.dt.tz attribute to find the timezone of the underlying datetime based data in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(pd.date_range('2012-12-31 00:00', periods = 5, freq = 'D', tz = 'US / Central')) # Creating the index idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'] # set the index sr.index = idx # Print the series print(sr) |
Output :
Now we will use Series.dt.tz attribute to find the timezone of the datetime data in the given series object.
# find the timezone result = sr.dt.tz # print the result print(result) |
Output :
As we can see in the output, the Series.dt.tz attribute has successfully returned the timezone of the underlying datetime based data in the given Series object.
