Earn the coveted Fabric Analytics Engineer certification. 100% off your exam for a limited time only!
input :
impact, L1_Country ,L2_State ,L3_City
100 | IND | ||
40 | IND | KA | |
35 | IND | TN | |
25 | IND | MH | |
10 | IND | KA | BLR |
20 | IND | KA | HSR |
10 | IND | KA | KLR |
Output:
is this possible ?
Solved! Go to Solution.
Hi @amitchandak
Thanks for your response. this idea worked for me. I made few changes to your formula to restrict blanks at each level.
@Anonymous , Create a new table
Dim = filter(distinct(union(distinct(Table[L1_Country]), distinct(Table[L2_State]), distinct(Table[L3_City]))),[L1_Country] <> blank())
Join this table (only column- L1_Country) with all three columns
Assume L1_Country to L1_Country is active and others are inactive
Then create a meausre like this and use
sum(Table[impact]) + calculate(sum(Table[impact]), userelationship(dim[L1_Country], Table[L2_State]))
+ calculate(sum(Table[impact]), userelationship(dim[L1_Country], Table[L3_City]))
refer if needed
Hi @amitchandak
Thanks for your response. this idea worked for me. I made few changes to your formula to restrict blanks at each level.
User | Count |
---|---|
141 | |
113 | |
104 | |
78 | |
64 |
User | Count |
---|---|
136 | |
125 | |
107 | |
70 | |
61 |