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Anonymous
Not applicable

Power BI SUMIFS on another table

Hello everybody

 

I have a maybe easy question but I dont find the way to do it easily : 

 

Table1 : 

 

ABC
Peter10Daughter
Franck20Son
Mark30Daughter

 

Table2 :

 

DEF
PeterPeter_Son100
PeterPeter_Daughter450
FranckFranck_Son600

 

I woud like : a sumifs + concatenate => SUMIFS(Table2[ColonneF], Column_E = (Column_A & Column_C) ) )  onthe table1

 

I dont have a direct unique key between the two tables that is why the join is a little bit complicated.

 

Kind regards, thanks !

1 ACCEPTED SOLUTION

Hi @Anonymous ,

You can create a calculated column as below in Table1:

Column = 
CALCULATE (
    SUM ( 'Table2'[F] ),
    FILTER ( 'Table2', 'Table2'[E] = 'Table1'[A] & "_" & 'Table1'[C] )
)

yingyinr_0-1661248478973.png

Best Regards

Community Support Team _ Rena
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

View solution in original post

3 REPLIES 3
AUaero
Responsive Resident
Responsive Resident

Create a calculated column on Table1 to create a join to Table2:

AUaero_2-1660847051832.png

 

 

AUaero_1-1660847023092.png

 

Anonymous
Not applicable

Thank you, I thought about it but I would like to do it in DAX.

 

I dont find the equivalent of MAX for Double, you know when you make column_values = MAX(colum_values_2) 

 

I am looking the equivalent for a String column like

 

CALCULATE( SUM(table_2[column_F],

table_2[column_D] = table_1[column_A])

 

or 

 

table_2[column_E] = table_1[column_A] & "_" & table_1[column_C]))

 

without joining the two tables, just a kind of sumIF

 

Kind regards

Hi @Anonymous ,

You can create a calculated column as below in Table1:

Column = 
CALCULATE (
    SUM ( 'Table2'[F] ),
    FILTER ( 'Table2', 'Table2'[E] = 'Table1'[A] & "_" & 'Table1'[C] )
)

yingyinr_0-1661248478973.png

Best Regards

Community Support Team _ Rena
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

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