Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.tz_convert() function convert tz-aware Datetime Array/Index from one time zone to another.
Syntax: Series.dt.tz_convert(*args, **kwargs)
Parameter :
tz : Time zone to convert timestamps to.
Returns : same type as self
Example #1: Use Series.dt.tz_convert() function to convert the timezone of the timestamps 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_convert() function to convert the timestamps in the given series object to ‘Europe/Berlin’.
# convert to 'Europe / Berlin' result = sr.dt.tz_convert(tz = 'Europe / Berlin') # print the result print(result) |
Output :
As we can see in the output, the Series.dt.tz_convert() function has successfully converted the timezone of the timestamps in the given series object to the target timezone.
Example #2 : Use Series.dt.tz_convert() function to convert the timezone of the timestamps 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_convert() function to convert the timestamps in the given series object to ‘Asia/Calcutta’.
# convert to 'Asia / Calcutta' result = sr.dt.tz_convert(tz = 'Asia / Calcutta') # print the result print(result) |
Output :
As we can see in the output, the Series.dt.tz_convert() function has successfully converted the timezone of the timestamps in the given series object to the target timezone.
