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sebastienke
Helper III
Helper III

How to use or replace CALCULATE when we have data in two different (but linked) tables ?

Hi, 

 

I have this measure which works perfectly. 

M: LxD_invoiced_month = CALCULATE(SUM(Joint_data[related amount]),Joint_data[To consider]=true())

 

Tables PNL (1) and Joint_Data (many) are linked with a cardinality (1:many).

 

In order to get the result that I wanted, I had to create the column related amount in Joint_Data  which is :

related amount = RELATED(PNL[Invoice_amount])

 

But I would like to take advantage of this cardinality and to not create a redundant column related amount and use directly Invoice_amount.

 

But the measures below don't work :

M: LxD_invoiced_month = CALCULATE(SUM(PNL[Invoice_amount]),Joint_data[To consider]=true())

 

M: LxD_invoiced_month = CALCULATE(SUM(RELATED(PNL[Invoice_amount])),Joint_data[To consider]=true())​

 

 

Someone to help me ?

Thank you.

 

 

1 ACCEPTED SOLUTION
amitchandak
Super User
Super User

@sebastienke , Try like

M: LxD_invoiced_month = CALCULATE(SUM(PNL[Invoice_amount]),filter(Joint_data,Joint_data[To consider]))

 

or

 

M: LxD_invoiced_month = CALCULATE(SUMX(Joint_data,RELATED(PNL[Invoice_amount])),filter(Joint_data,Joint_data[To consider]))



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2 REPLIES 2
amitchandak
Super User
Super User

@sebastienke , Try like

M: LxD_invoiced_month = CALCULATE(SUM(PNL[Invoice_amount]),filter(Joint_data,Joint_data[To consider]))

 

or

 

M: LxD_invoiced_month = CALCULATE(SUMX(Joint_data,RELATED(PNL[Invoice_amount])),filter(Joint_data,Joint_data[To consider]))



New Power BI Features
Datamarts: https://youtu.be/8tskWsJTEpg
Field Parameters : https://youtu.be/lqF3Wa1FllE?t=70
Did I answer your question? Mark my post as a solution! Appreciate your Kudos!! !!
Dashboard of My Blogs !! Connect on Linkedin !! Proud to be a Super User!
Want To Learn Power BI
Learn Power BI Beginners !! Advance Power BI Concepts !! Power BI For Tableau User !! Learn Power BI in Hindi !!
!! Subscribe to my youtube Channel !!

so obvious..... 🤔
Thanks a lot @amitchandak 

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