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.
import pandas as pd
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' ])
idx = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' , 'Day 5' ]
sr.index = idx
sr = pd.to_datetime(sr)
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.
result = sr.dt.time
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.
import pandas as pd
sr = pd.Series(pd.date_range( '2012-12-12 12:12' ,
periods = 5 , freq = 'H' ))
idx = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' , 'Day 5' ]
sr.index = idx
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.
result = sr.dt.time
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.