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Frequent Visitor

Count/CountA counting wrong category

This is such a simple problem that I have spent HOURS trying to solve with no avail.

I have a table with plenty of categories, say A, B, C, D, etc. I have a measure that Counts values in column A, and filters them by name into a table. I have another identical measure for column B, but for some reason, it just returns identical values as the column A measure. It makes no sense and I'm ripping my hair out over here.

My table looks like this:

Name  |  Count of A  |  Count of B

Name1          3                      3
Name2          7                      7
Name3          4                      4          

and so on when A and B in reality have very different values. I don't understand what's happening. Please help me <3.

1 ACCEPTED SOLUTION

Accepted Solutions
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Community Support
Community Support

Hi, @gtripple 

 

Based on your description, I assume that you want to filter by player and get win% for each player. I created data to reproduce your scenario. The pbix file is attached in the end.

Table:

a1.png

 

Names(a calculated table):

Names = 
DISTINCT(
    UNION(
        DISTINCT('Table'[Winner's Name]),
        DISTINCT('Table'[Loser's Name])
    )
)

 

You may create a measure as below.

Win % = 
var result = 
DIVIDE(
    COUNTROWS(
        FILTER(
            ALL('Table'),
            [Winner's Name]=SELECTEDVALUE(Names[Name])
        )
    ),
    COUNTROWS(
        FILTER(
            ALL('Table'),
            OR(
                [Winner's Name]=SELECTEDVALUE(Names[Name]),
                [Loser's Name]=SELECTEDVALUE(Names[Name])
            )
        )
    )
)
return
IF(
    ISBLANK(result),
    0,
    result
)

 

Result:

a2.png

 

Best Regards

Allan

 

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

8 REPLIES 8
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Super User II
Super User II

Please post a sample data and your formulas.






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Hello @gtripple ,

 

Try ignoring any filters applied on the column, on which you are calculating Measure 'B'. You can achieve this in your DAX, by using ALL( ) function in your filter context of DAX.

 

Or you can share your masked DAX, so that I can get more clarity on your issue.

Highlighted

I can't provide data because it's sensitive. However, I have isolated the problem. 

My dataset is essentially [game_id] [winner's name] [loser's name] .

The reason I am having issues is that when I put [Count of Wins] and [Count of Losses] into a table, I don't have a common "name" column to filter them both into. 

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Pls see above explanation ❤️

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@gtripple , Do you have team or something like that to group /filter if not what is expected?



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I do not. I just want a way to filter by player and get someone's win%, etc

Highlighted

Hello @gtripple ,

Is your data in this format ?

Data.PNG

Please give some sample masked data, so that we get better idea. You can also give the sample output which you expect.

Highlighted
Community Support
Community Support

Hi, @gtripple 

 

Based on your description, I assume that you want to filter by player and get win% for each player. I created data to reproduce your scenario. The pbix file is attached in the end.

Table:

a1.png

 

Names(a calculated table):

Names = 
DISTINCT(
    UNION(
        DISTINCT('Table'[Winner's Name]),
        DISTINCT('Table'[Loser's Name])
    )
)

 

You may create a measure as below.

Win % = 
var result = 
DIVIDE(
    COUNTROWS(
        FILTER(
            ALL('Table'),
            [Winner's Name]=SELECTEDVALUE(Names[Name])
        )
    ),
    COUNTROWS(
        FILTER(
            ALL('Table'),
            OR(
                [Winner's Name]=SELECTEDVALUE(Names[Name]),
                [Loser's Name]=SELECTEDVALUE(Names[Name])
            )
        )
    )
)
return
IF(
    ISBLANK(result),
    0,
    result
)

 

Result:

a2.png

 

Best Regards

Allan

 

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

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