Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas PeriodIndex.is_leap_year attribute return an array of boolean values corresponding to each element in the PeriodIndex object. It return True if the given year is a leap year else it return False if it is not a leap year.
Syntax : PeriodIndex.is_leap_year
Parameters : None
Return : array of boolean values
Example #1: Use PeriodIndex.is_leap_year attribute to check for each element in the given PeriodIndex object, whether it is a leap year or not.
# importing pandas as pd import pandas as pd # Create the PeriodIndex object pidx = pd.PeriodIndex(start ='2003-12-21 08:45 ', end ='2009-12-21 11:55', freq ='Y') # Print the PeriodIndex object print(pidx) |
Output :
Now we will use the PeriodIndex.is_leap_year attribute to check if the given year is a leap year or not.
# check for leap year pidx.is_leap_year |
Output :
As we can see in the output, the PeriodIndex.is_leap_year attribute has returned an array containing boolean values. True indicates the given year was a leap year and False indicates the given year was not a leap year.
Example #2: Use PeriodIndex.is_leap_year attribute to check for each element in the given PeriodIndex object, whether it is a leap year or not.
# importing pandas as pd import pandas as pd # Create the PeriodIndex object pidx = pd.PeriodIndex(start ='2016-02-1', end ='2016-02-06', freq ='D') # Print the PeriodIndex object print(pidx) |
Output :
Now we will use the PeriodIndex.is_leap_year attribute to check if the given year is a leap year or not.
# check for leap year pidx.is_leap_year |
Output :
As we can see in the output, the PeriodIndex.is_leap_year attribute has returned an array containing boolean values. True indicates the given year was a leap year and False indicates the given year was not a leap year.
