Python | Pandas Series.dt.second
Last Updated :
20 Mar, 2019
Series.dt
can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.second
attribute return a numpy array containing the second of the datetime in the underlying data of the given series object.
Syntax: Series.dt.second
Parameter : None
Returns : numpy array
Example #1: Use Series.dt.second
attribute to return the seconds of the datetime in the underlying data of the given Series object.
import pandas as pd
sr = pd.Series([ '2012-10-21 09:30:45' , '2019-7-18 12:30:21' , '2008-02-2 10:30:38' ,
'2010-4-22 09:25:19' , '2019-11-8 02:22:44' ])
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.second
attribute to return the seconds of the datetime in the underlying data of the given Series object.
result = sr.dt.second
print (result)
|
Output :
As we can see in the output, the Series.dt.second
attribute has successfully accessed and returned the second of the datetime in the underlying data of the given series object.
Example #2 : Use Series.dt.second
attribute to return the seconds of the datetime in 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.second
attribute to return the seconds of the datetime in the underlying data of the given Series object.
result = sr.dt.second
print (result)
|
Output :
As we can see in the output, the Series.dt.second
attribute has successfully accessed and returned the second of the datetime in the underlying data of the given series object.
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