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Hello, all.
date | category | name unit | countUnit | tradePoint | Zone |
01.01.2019 | Bike | Bike1 | 356 | 1A | A |
02.01.2019 | Bike | Bike2 | 388 | 6C | C |
03.01.2019 | Bike | Bike3 | 176 | 9C | C |
04.01.2019 | Bike | Bike4 | 391 | 9C | C |
05.01.2019 | Bike | Bike5 | 443 | 3C | C |
06.01.2019 | Car | Car6 | 500 | 4A | A |
07.01.2019 | Car | Car7 | 452 | 9C | C |
08.01.2019 | Car | Car8 | 306 | 3C | C |
09.01.2019 | Car | Car9 | 295 | 7A | A |
10.01.2019 | Robots | Robots10 | 490 | 2B | B |
11.01.2019 | Robots | Robots11 | 466 | 9C | C |
12.01.2019 | Robots | Robots12 | 261 | 4A | A |
13.01.2019 | Robots | Robots13 | 488 | 2B | B |
14.01.2019 | Bike | Bike1 | 199 | 9C | C |
15.01.2019 | Bike | Bike2 | 397 | 6C | C |
16.01.2019 | Bike | Bike3 | 291 | 1A | A |
17.01.2019 | Bike | Bike4 | 496 | 6C | C |
18.01.2019 | Bike | Bike5 | 348 | 1A | A |
19.01.2019 | Car | Car6 | 247 | 7A | A |
20.01.2019 | Car | Car7 | 411 | 7A | A |
21.01.2019 | Car | Car8 | 378 | 8B | B |
22.01.2019 | Car | Car9 | 426 | 2B | B |
23.01.2019 | Robots | Robots10 | 296 | 8B | B |
24.01.2019 | Robots | Robots11 | 268 | 10A | A |
25.01.2019 | Robots | Robots12 | 461 | 5B | B |
26.01.2019 | Robots | Robots13 | 209 | 7A | A |
01.01.2019 | Bike | Bike1 | 275 | 7A | A |
02.01.2019 | Bike | Bike2 | 329 | 6C | C |
03.01.2019 | Bike | Bike3 | 173 | 10A | A |
06.01.2019 | Car | Car6 | 377 | 9C | C |
07.01.2019 | Car | Car7 | 399 | 10A | A |
08.01.2019 | Car | Car8 | 475 | 10A | A |
09.01.2019 | Car | Car9 | 271 | 6C | C |
10.01.2019 | Robots | Robots10 | 396 | 5B | B |
11.01.2019 | Robots | Robots11 | 431 | 5B | B |
12.01.2019 | Robots | Robots12 | 281 | 7A | A |
14.01.2019 | Bike | Bike1 | 491 | 3C | C |
15.01.2019 | Bike | Bike2 | 163 | 6C | C |
16.01.2019 | Bike | Bike3 | 250 | 10A | A |
17.01.2019 | Bike | Bike4 | 245 | 5B | B |
18.01.2019 | Bike | Bike5 | 294 | 3C | C |
19.01.2019 | Car | Car6 | 430 | 6C | C |
21.01.2019 | Car | Car8 | 304 | 1A | A |
22.01.2019 | Car | Car9 | 323 | 3C | C |
23.01.2019 | Robots | Robots10 | 285 | 8B | B |
24.01.2019 | Robots | Robots11 | 479 | 7A | A |
25.01.2019 | Robots | Robots12 | 404 | 7A | A |
I have an approximate such table with the values of products and categories that were sold in points. Points are part of a zone.
I calculate the average value of sales for the period by days. If you look at the point level, the calculation is correct.
But how do I calculate the average in the zone for these points?
Using the example of zone B, we have 3 points with average values of 468, 383, 480. The average for the zone will be 444 , but measure calculate = sumCount (6226) / distinctDate(15) = 415 is wrong
How do I calculate the average points for a zone , use function ISFILTERED () ?
And if it is possible calculate avarage for the total (maybe a function of Hasonevalue() )?
Solved! Go to Solution.
This problem is solved by :
Hi @Anonymous,
How could you get the AverageCount in your Matrix table? If I used the "Averahe" option, I get the different data from you:
Could you please offer me more information about it?
Regards,
Daniel He
Hi @v-danhe-msft ,
It is important that the mean value of the zone is calculated from the mean values of the points.
I have created a file that displays the calculations, please see .
The average value is calculated differently, for points it is sales divided by the number of dates, for a zone it is the average of the values of the average points.
And I can not use the Avarage function on a column, because the points are filtered by attribute in the "sum" measure itself.
This problem is solved by :