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 Period.is_leap_year attribute checks for the given Period object, if the year in the object is a leap year or not. It return True if the following year is a leap year else it return False
Syntax : Period.is_leap_year
Parameters : None
Return : boolean
Example #1: Use Period.is_leap_year attribute to check if the date present in the given Period object is a leap year or not.
# importing pandas as pd import pandas as pd # Create the Period object prd = pd.Period(freq ='H', year = 2000, month = 2, day = 21, hour = 8) # Print the Period object print(prd) |
Output :
Now we will use the Period.is_leap_year attribute to check if the date present in the prd object is a leap year or not.
# check for leap year prd.is_leap_year |
Output :
As we can see in the output, the Period.is_leap_year attribute has returned True indicating that the following year in the prd object is a leap year.
Example #2: Use Period.is_leap_year attribute to check if the date present in the given Period object is a leap year or not.
# importing pandas as pd import pandas as pd # Create the Period object prd = pd.Period(freq ='T', year = 2006, month = 10, hour = 15, minute = 49) # Print the Period object print(prd) |
Output :
Now we will use the Period.is_leap_year attribute to check if the date present in the prd object is a leap year or not.
# check for leap year prd.is_leap_year |
Output :
As we can see in the output, the Period.is_leap_year attribute has returned False indicating that the following year in the prd object is not a leap year.
