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.
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