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.weekofyear attribute outputs the ordinal value of the week of the year for each entries of the DatetimeIndex object.
Syntax: DatetimeIndex.weekofyear
Return: Index object
Example #1: Use DatetimeIndex.weekofyear attribute to find the ordinal value of the week for each entries in the DatetimeIndex object.
# importing pandas as pd import pandas as pd # Create the DatetimeIndex # Here 'W' represents Weekly frequency didx = pd.DatetimeIndex(start ='2000-01-10 06:30', freq ='W', periods = 3, tz ='Asia/Calcutta') # Print the DatetimeIndex print(didx) |
Output :
Now we want to find ordinal value of the week for each entries in the DatetimeIndex object.
# find the ordinal value of the week # for each entries present in the object didx.weekofyear |
Output :
As we can see in the output, the function has returned an Index object containing the ordinal values of the weeks present in each entry of the DatetimeIndex object.
Example #2: Use DatetimeIndex.weekofyear attribute to find the ordinal value of the week for each entries in the DatetimeIndex object.
# importing pandas as pd import pandas as pd # Create the DatetimeIndex # Here 'M' represents monthly frequency didx = pd.DatetimeIndex(start ='2014-08-01 10:05:45', freq ='M', periods = 5, tz ='Asia/Calcutta') # Print the DatetimeIndex print(didx) |
Output :
Now we want to find ordinal value of the week for each entries in the DatetimeIndex object.
# find the ordinal value of the week # for each entries present in the object didx.weekofyear |
Output :
As we can see in the output, the function has returned an Index object containing the ordinal values of the weeks present in each entry of the DatetimeIndex object.
