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Python | Pandas PeriodIndex.week

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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 PeriodIndex.week attribute return an Index object containing the ordinal value of week of each period element for the given PeriodIndex object.

Syntax : PeriodIndex.week

Parameters : None

Return : Index object

Example #1: Use PeriodIndex.week attribute to find out the ordinal value of week of each period element contained in the given PeriodIndex object.




# importing pandas as pd
import pandas as pd
  
# Create the PeriodIndex object
pidx = pd.PeriodIndex(start ='2004-11-11 02:45:21 '
               end ='2004-11-21 8:45:29', freq ='D')
  
# Print the PeriodIndex object
print(pidx)


Output :

Now we will use the PeriodIndex.week attribute to find out the week’s ordinal value in the year for the given PeriodIndex object.




# return the week's ordinal value
pidx.week


Output :

As we can see in the output, the PeriodIndex.week attribute has returned an Index object containing the ordinal value of the week for each period element contained in the given PeriodIndex object.
 
Example #2: Use PeriodIndex.week attribute to find out the ordinal value of week of each period element contained in the given PeriodIndex object.




# importing pandas as pd
import pandas as pd
  
# Create the PeriodIndex object
pidx = pd.PeriodIndex(start ='2016-2-12 11:12:02'
           end ='2016-09-12 11:32:12', freq ='M')
  
# Print the PeriodIndex object
print(pidx)


Output :

Now we will use the PeriodIndex.week attribute to find out the week’s ordinal value in the year for the given PeriodIndex object.




# return the week's ordinal value
pidx.week


Output :

As we can see in the output, the PeriodIndex.week attribute has returned an Index object containing the ordinal value of the week for each period element contained in the given PeriodIndex object.



Last Updated : 06 Jan, 2019
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