Find everything you need to get certified on Fabric—skills challenges, live sessions, exam prep, role guidance, and more. Get started
Hello everyone,
first of all, I study Definitive guide to dax vol2, reading blogs of sqlbi.com etc..so please do not respond that I should watch it
I still fighting witch filter context,
I have following easy use case based on following tables
schema is simple
And here starts my questions (not only one) if I want to write DAX measure which will count user per each city,
1) why I cannot do it with following formula?
Solved! Go to Solution.
Hi @Petr__ ,
Regarding your formula `User per City = CALCULATE(COUNT(User[ID_user]),Location[ID_location])`**:
The reason this formula does not produce the expected result is due to how the `CALCULATE` function works in conjunction with the `COUNT` function. The `CALCULATE` function changes the context in which the data is evaluated, and it expects a filter expression as its second argument. However, `Location[ID_location]` as used here does not serve as a filter expression but rather as a column reference, which is why it doesn't work as expected.
For more details, please refer: CALCULATE – DAX Guide
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @Petr__ ,
We can create a measure.
Measure = CALCULATE(DISTINCTCOUNT(User[Name]),CROSSFILTER(Sales[ID_user],User[ID_user],Both))
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @v-tangjie-msft
great, thanks for DAX !!!
may I ask you why is not working solution 1) there is data relation (fact table) where data can be meet and calculated
Hi @Petr__ ,
Regarding your formula `User per City = CALCULATE(COUNT(User[ID_user]),Location[ID_location])`**:
The reason this formula does not produce the expected result is due to how the `CALCULATE` function works in conjunction with the `COUNT` function. The `CALCULATE` function changes the context in which the data is evaluated, and it expects a filter expression as its second argument. However, `Location[ID_location]` as used here does not serve as a filter expression but rather as a column reference, which is why it doesn't work as expected.
For more details, please refer: CALCULATE – DAX Guide
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Filter context is best conquered with matrix visuals and with their cousins SUMMARIZE and SUMMARIZECOLUMNS.
Put your dimension users into rows of a matrix visual, dimension locations into colums, and count of Value (not sum!) in the values. That will give you a graphical idea of the solution to your question.
Hi @lbendlin
thanks for tip / best practicte !!! but unfortunatelly it doesn t reply on my questions 😞 can you please reply on them pls ? 🙂
I think you can figure it out yourself. Learn about star schema, and how to use TREATAS.
Hello @lbendlin ,
as I mentioned, I am learning and trying to understand logic of filter context with directions, trust me if I understand why is that I will not ask for help and where else to ask for help than forum? 🙂 so if you can explain me in high level, why cannot use solution one, even when data are match via Fact table I will really apreciate it (because I am trying to finding proper explanation for that) and unfortunatelly there is no one in my area who can give me explanation for that (btw still continue to explore blog in sqlbi.com etc..) so if you know explanation it will really help me 🙂
thank you
Here's the answer to question 1, both with implicit measures (in the matrix) and explicit measures (in the table). Note that this is the simplest implementation and it won't tell you which users were NOT in a city. for that you need to use the outer tables.
Check out the September 2024 Power BI update to learn about new features.
Learn from experts, get hands-on experience, and win awesome prizes.
User | Count |
---|---|
25 | |
20 | |
18 | |
18 | |
15 |
User | Count |
---|---|
36 | |
19 | |
19 | |
17 | |
11 |