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 DatetimeIndex.year attribute outputs an Index object containing the value of years present in the Datetime object.
Syntax: DatetimeIndex.year
Return: Index containing years.
Example #1: Use DatetimeIndex.year attribute to find the years present in the DatetimeIndex.
# importing pandas as pd import pandas as pd # Create the DatetimeIndex # Here the 'B' represents Business day frequency didx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='B', periods = 5, tz ='Asia/Calcutta') # Print the DatetimeIndex print(didx) |
Output :
Now we want to find all the year values present in the DatetimeIndex object.
# find all the years in the object didx.year |
Output :
As we can see in the output, the function has returned an Index object containing the year value of each entry in the DatetimeIndex object.
&n bsp;
Example #2: Use DatetimeIndex.year attribute to find the years present in the DatetimeIndex.
# importing pandas as pd import pandas as pd # Create the DatetimeIndex # Here the 'AS' represents Year start frequency didx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='AS', periods = 5, tz ='Asia/Calcutta') # Print the DatetimeIndex print(didx) |
Output :
Now we want to find all the year values present in the DatetimeIndex object.
# find all the years in the object didx.year |
Output :
As we can see in the output, the function has returned an Index object containing the year value of each entry in the DatetimeIndex object.
