DatetimeIndex.is_leap_year Pandas DatetimeIndex.is_leap_year returns a boolean indicator if the date is in a leap year. Leap year — this is a year with 366 days (instead of 365), including February 29 as an intermediate day. Leap years — these are years in multiples of four, except for years that are multiples of 100, but not 400.

Syntax: DatetimeIndex.is_leap_year

Returns : numpy array containing logical values.

Example # 1: Use the DatetimeIndex.is_leap_year attribute to check if dates in a DatetimeIndex object leap year.

# import pandas as pd import pandas as pd   # Create DatetimeIndex didx = pd .DatetimeIndex ([ `2014-01-01` , ` 2008 -12-31` , `2017-03-31` , ` 2000-12-31` ])   # Print DatetimeIndex print (didx)

Output:

Now we want to find out if the dates contained in this DatetimeIndex are leap years or not.

# determine if dates are in leap years didx.is_leap_year

Output:
< br /> As we can see in the output, the function returned an empty array containing boolean values ​​for each record of the DatetimeIndex object.  True values ​​indicate that the corresponding date is in a leap year, and False values ​​indicate that the corresponding date is not in a leap year.

Example # 2: Use the DatetimeIndex.is_leap_year attribute to check if the dates in the DatetimeIndex are in a leap year.

# import pandas as pd import pandas as pd   # Create DatetimeIndex didx = pd.date_range ( "2008-12-30" periods = 5 , freq = `Q` )   # Print DatetimeIndex print (didx)

Output:

Now we want to find out if the dates contained in given DatetimeIndex object, leap year or not.

# determine if dates are in a leap year didx.is_leap_year

Output:

As we can see in the output, the function returned n a stub array containing the boolean values ​​for each entry in the DatetimeIndex object.  True values ​​indicate that the corresponding date is in a leap year, and False values ​​indicate that the corresponding date is not in a leap year.