Forum Discussion
How to sum up only different values in a column
- 4 years ago
Hi Arashi
Here's a measure that follows a similar pattern to others in this thread. It uses SUMMARIZE to build a table of unique Store and Extention values, then the SUMX aggregates the Extention values from there.
Max. de Extention = SUMX( SUMMARIZE('Table','Table'[Store],'Table'[Extention]), 'Table'[Extention] )Used in a matrix you get this
Hi, Arashi
I can't get 1225 from the data above, would you be able to consider sharing more data and the output you expect?
It makes it easier to give you a solution.
- Sample (dummy dataset) data as text, use the table tool in the editing bar
- Expected output from sample data
- Explanation in words of how to get from 1. to 2.
Best Regards,
Community Support Team _ Zeon Zheng
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hello,
I have attached in the table below the dummy set data.
The expected result is as below:
For each store I want to get the sum of the maximum value of the extension column for each type of zone.
The measure provided by daXtreme works perfectly in that sense.
However, I also want to get the aggregate of the type of store as the sum of each of the resultant value of stores based on previous measure.
| Category | Zone | Store | Extention | Type of Store | Units |
| Category5 | Warehouse | 1206 | 136,6 | E | 0 |
| Category5 | Warehouse | 2052 | 102,06 | C | 217 |
| Category5 | Warehouse | 2066 | 168,07 | E | 85 |
| Category5 | Warehouse | 2067 | 117 | C | 174 |
| Category5 | Warehouse | 8329 | 116 | C | 226 |
| Category5 | Warehouse | 8334 | 0 | B | 0 |
| Category5 | Warehouse | 8404 | 0 | D | 0 |
| Category4 | Warehouse | 8750 | 118,92 | A | 639 |
| Category4 | Warehouse | 13922 | 0 | B | 0 |
| Category4 | Warehouse | 14009 | 133,67 | D | 160 |
| Category4 | entrance | 1206 | 279,96 | E | 0 |
| Category4 | entrance | 2052 | 351,11 | C | 313 |
| Category4 | entrance | 2066 | 369,54 | E | 90 |
| Category4 | entrance | 2067 | 322 | C | 386 |
| Category4 | entrance | 8329 | 439 | C | 338 |
| Category4 | entrance | 8334 | 313,48 | B | 503 |
| Category4 | entrance | 8404 | 192,86 | D | 220 |
| Category3 | entrance | 8750 | 355,63 | A | 911 |
| Category3 | entrance | 13922 | 337,65 | B | 838 |
| Category3 | entrance | 14009 | 281,71 | D | 225 |
| Category3 | Warehouse | 1206 | 136,6 | E | 0 |
| Category3 | Warehouse | 2052 | 102,06 | C | 196 |
| Category3 | Warehouse | 2066 | 168,07 | E | 254 |
| Category3 | Warehouse | 2067 | 117 | C | 136 |
| Category3 | Warehouse | 8329 | 116 | C | 132 |
| Category3 | Warehouse | 8334 | 0 | B | 7 |
| Category2 | Warehouse | 8404 | 0 | D | 0 |
| Category2 | Warehouse | 8750 | 118,92 | A | 313 |
| Category2 | Warehouse | 13922 | 0 | B | 0 |
| Category2 | Warehouse | 14009 | 133,67 | D | 52 |
| Category2 | entrance | 1206 | 279,96 | E | 0 |
| Category2 | entrance | 2052 | 351,11 | C | 489 |
| Category2 | entrance | 2066 | 369,54 | E | 231 |
| Category2 | entrance | 2067 | 322 | C | 545 |
| Category2 | entrance | 8329 | 439 | C | 699 |
| Category2 | entrance | 8334 | 313,48 | B | 1319 |
| Category2 | entrance | 8404 | 192,86 | D | 346 |
| Category1 | entrance | 8750 | 355,63 | A | 1717 |
| Category1 | entrance | 13922 | 337,65 | B | 1860 |
| Category1 | entrance | 14009 | 281,71 | D | 366 |
Let me know if now it is clear or if you need any other explanation.
Thanks a lot
- PaulOlding4 years ago
Solution Sage
Hi Arashi
Here's a measure that follows a similar pattern to others in this thread. It uses SUMMARIZE to build a table of unique Store and Extention values, then the SUMX aggregates the Extention values from there.
Max. de Extention = SUMX( SUMMARIZE('Table','Table'[Store],'Table'[Extention]), 'Table'[Extention] )Used in a matrix you get this
- Arashi4 years agoFrequent Visitor
It works perfectly.
I have a lot to learn yet!
Thank you very much!