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.year attribute return an integer value representing the year the given period lies in.
Syntax : Period.year
Parameters : None
Return : year
Example #1: Use Period.year attribute to find the year the period lies in for the given Period object.
# importing pandas as pd import pandas as pd # Create the Period object prd = pd.Period(freq ='S', year = 2000, month = 2, day = 21, hour = 8, minute = 21) # Print the Period object print(prd) |
Output :
Now we will use the Period.year attribute to find the value of year
# return the year value prd.year |
Output :
As we can see in the output, the Period.year attribute has returned 2000 indicating that the given period lies in the year of 2000.
Example #2: Use Period.year attribute to find the year the period lies in for the given Period object.
# 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.year attribute to find the value of year
# return the year prd.year |
Output :
As we can see in the output, the Period.year attribute has returned 2006 indicating that the given period lies in the year of 2006.
