Python | Pandas Series.dt.is_leap_year
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.is_leap_year
attribute return a boolean indicator if the date belongs to a leap year.
Syntax: Series.dt.is_leap_year
Parameter : None
Returns : numpy array
Example #1: Use Series.dt.is_leap_year
attribute to check if the dates in the underlying data of the given series object belongs to a leap year.
import pandas as pd
sr = pd.Series([ '2012-12-31' , '2019-1-1 12:30' , '2008-02-2 10:30' ,
'2010-1-1 09:25' , '2019-12-31 00:00' ])
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.is_leap_year
attribute to check if the dates in the given series object belongs to a leap year.
result = sr.dt.is_leap_year
print (result)
|
Output :
As we can see in the output, the Series.dt.is_leap_year
attribute has successfully accessed and returned boolean values indicating whether the dates in the given series object belongs to a leap year.
Example #2 : Use Series.dt.is_leap_year
attribute to check if the dates in the underlying data of the given series object belongs to a leap year.
import pandas as pd
sr = pd.Series(pd.date_range( '2012-12-31 00:00' , periods = 5 , freq = 'D' ))
idx = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' , 'Day 5' ]
sr.index = idx
print (sr)
|
Output :
Now we will use Series.dt.is_leap_year
attribute to check if the dates in the given series object belongs to a leap year.
result = sr.dt.is_leap_year
print (result)
|
Output :
As we can see in the output, the Series.dt.is_leap_year
attribute has successfully accessed and returned boolean values indicating whether the dates in the given series object belongs to a leap year.
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