Python | Pandas Series.dt.tz
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.
