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raulogv
Frequent Visitor

Flag rows based in content of another column

Maybe a noob question.

 

I have a table like the one below

 

Sales document         Block

1                                 A

1                                 B

1                                 A

2                                 A

2                                 B

3                                 B

 

I need to add a column that flags all rows of that sales document that contain block A. 

 

Sales document        Block        Contain block A?

1                                A                Yes

1                                B                Yes

1                                A                Yes

2                                A                Yes

2                                B                Yes

3                                B

 

 

Thanks in advance

1 ACCEPTED SOLUTION
JarroVGIT
Resident Rockstar
Resident Rockstar

Hi @raulogv ,

You can try this calculated column:

CalcColumn = 
VAR _curSalesDoc = 'Table'[Sales Document]
VAR _rowsWithA = FILTER('Table', 'Table'[Sales Document] = _curSalesDoc && 'Table'[Block] = "A")
RETURN
IF(COUNTROWS(_rowsWithA) > 0, "Yes")

Result is this:

image.png

Let me know if this works for you 🙂

 

Kind regards

Djerro123

-------------------------------

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View solution in original post

2 REPLIES 2
JarroVGIT
Resident Rockstar
Resident Rockstar

Hi @raulogv ,

You can try this calculated column:

CalcColumn = 
VAR _curSalesDoc = 'Table'[Sales Document]
VAR _rowsWithA = FILTER('Table', 'Table'[Sales Document] = _curSalesDoc && 'Table'[Block] = "A")
RETURN
IF(COUNTROWS(_rowsWithA) > 0, "Yes")

Result is this:

image.png

Let me know if this works for you 🙂

 

Kind regards

Djerro123

-------------------------------

If this answered your question, please mark it as the Solution. This also helps others to find what they are looking for.

Keep those thumbs up coming! 🙂





Did I answer your question? Mark my post as a solution!

Proud to be a Super User!




It worked, thank you so much

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