Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.time attribute return a numpy array of python datetime.time objects.
Syntax: Series.dt.time
Parameter : None
Returns : numpy array
Example #1: Use Series.dt.time attribute to return the time property of the underlying data of the given Series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['2012-10-21 09:30', '2019-7-18 12:30', '2008-02-2 10:30', '2010-4-22 09:25', '2019-11-8 02:22']) # 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.time attribute to return the time property of the underlying data of the given Series object.
# return the time result = sr.dt.time # print the result print(result) |
Output :
As we can see in the output, the Series.dt.time attribute has successfully accessed and returned the time property of the underlying data in the given series object.
Example #2 : Use Series.dt.time attribute to return the time property of the underlying data of the given Series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(pd.date_range('2012-12-12 12:12', periods = 5, freq = 'H')) # 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.time attribute to return the time property of the underlying data of the given Series object.
# return the time result = sr.dt.time # print the result print(result) |
Output :
As we can see in the output, the Series.dt.time attribute has successfully accessed and returned the time property of the underlying data in the given series object.
