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 Timestamp.is_leap_year attribute return a boolean value. It return True if the date in the given Timestamp object is a leap year else it return False.
Syntax : Timestamp.is_leap_year
Parameters : None
Return : boolean
Example #1: Use Timestamp.is_leap_year attribute to check if the date in the given Timestamp object is a leap year or not.
# importing pandas as pd import pandas as pd   # Create the Timestamp object ts = pd.Timestamp(2016, 2, 15, 12)   # Print the Timestamp object print(ts) |
Output :
Now we will use the Timestamp.is_leap_year attribute to find out if the date in the ts object is a leap year or not.
# check for leap year ts.is_leap_year |
Output :
As we can see in the output, the Timestamp.is_leap_year attribute has returned True indicating the date in the given Timestamp object is a leap year.
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Example #2: Use Timestamp.is_leap_year attribute to check if the date in the given Timestamp object is a leap year or not.
# importing pandas as pd import pandas as pd   # Create the Timestamp object ts = pd.Timestamp(year = 2009, month = 10, day = 21,                      hour = 4, tz = 'Europe/Berlin')   # Print the Timestamp object print(ts) |
Output :
Now we will use the Timestamp.is_leap_year attribute to find out if the date in the ts object is a leap year or not.
# check for leap year ts.is_leap_year |
Output :
As we can see in the output, the Timestamp.is_leap_year attribute has returned False indicating the date in the given Timestamp object is not a leap year.
