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filipebodas
Helper I
Helper I

Divide based on a grouped column

Hello everyone,

 

I have a table with 5 columns [Date, ID, ZipCode, Numeric, Condition]

 

DateIDZipCodeNumberCondition
27/03/2020AMTS117525Yes
27/03/2020BMTS117525Yes
19/03/2020CMTS117525Yes
25/02/2020DMTS0No
20/01/2020EFCM120368Yes
13/03/2020FFCM120368Yes
17/03/2020GFCM0No
05/04/2020HMTS247831Yes
31/03/2020IMTS0No
08/04/2020JMTS247831Yes

 

The goal is to divide the Number with the total count of "Yes".

  • For ZipCode "MTS" I want to divide 117.525/3, being 3 the total number of Yes for that ZipCode and for that Number. The Number results from a condition based on the Date.
  • For ZipCode "FCM" I want to divide 120.368/2.
  • Again for MTS, 247.831/2
  • For the rows where the Condition = "No" the result should be 0.


How do i lock that count for each row? 

Edit: I add another ZipCode to clarify the goal

1 ACCEPTED SOLUTION
amitchandak
Super User
Super User

@filipebodas , New column

 

new column =divide( [Number], countx(filter(Table, [zip code] =earlier([Zip Code]) && [Condition]= "Yes" ), [ID] ) )

View solution in original post

5 REPLIES 5
tamerj1
Super User
Super User

@filipebodas 

Use this for a calculated column

Column1 =
VAR ZipCodeTable =
    CALCULATETABLE ( Table, ALLEXCEPT ( Table, Table[ZipCode] ) )
VAR YesTable =
    FILTER ( ZipCodeTable, Table[Condition] = "Yes" )
RETURN
    IF (
        Table[Condition],
        0,
        DIVIDE ( SUMX ( YesTable, Table[Number] ), COUNTROWS ( YesTable ) )
    )
tamerj1
Super User
Super User

Hi @filipebodas 

I guess you are using table visual by ZipCode. Then you can use

 

Measure1 =
DIVIDE (
    SUM ( Table[Number] ),
    COUNTROWS ( FILTER ( Table, Table[Condition] = "Yes" ) )
)

 

@tamerj1 I edit the question

amitchandak
Super User
Super User

@filipebodas , New column

 

new column =divide( [Number], countx(filter(Table, [zip code] =earlier([Zip Code]) && [Condition]= "Yes" ), [ID] ) )

When the condition is "No" the new column should be 0.

The output should be 39175 | 39175 | 39175 | 0 (these are rows). 

The zipcode can change along the table

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