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)
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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)
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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)
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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)
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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.