How to apply filter on a specific column and not on all other columns?

Hi,

I have different columns in a table. I want to apply a filter which only applies to a specific column . It shouldn’t affect any other columns in the table visual. Say we have a column 1 and column 2. If I add a filter then the numbers in column 1 shouldn’t change but column 2 in the table should change. How do I do this?

Hi @liya101

I guess that’s not possible.

City / Product / Value
C123 / P123 / 8
C456 / P123 / 7
C789 / P456 / 1

If you filter to show only P123 you will exclude C789 automatically.

Is that what you mean? To show C789 even if you exclude P456?

BR

Hi,
If there are columns , category, no of people, no of converted customers ,
Category A 200 , 150,
Category B 300, 200
Category C 350, 250
And if I am putting a filter to show the converted customers who made a purchase of more than a specific amount, but I want to see the first column, number of people unchanged
For category A, out of 200 people , if 50 people made a purchase more than the amount, then it should show
Category A, 200, 50. Can this be done using parameters and calculated field?

You can calculate the fact “people made a purchase more than the amount” (e.g. 50 for Category A) but as soon as you apply a filter you will lose something.

Hello @liya101, to follow-up with what @ErikG was saying, when you alter the values that will be returned for a specific column, the entire row will be removed from the visual as that value has been filtered out. Now, what you can do, is try to use functions like sumOver(), avgOver(), etc. in QuickSight to display values based on a certain partition to try and include certain values in a table after a filter. Utilize the PRE_AGG or PRE_FILTER calculation levels, there is a little more capability. You can also try showing certain values in a KPI by your table visual that are not filtered to show some more information.

I’ll mark this as the solution, but if you have any further questions on this topic, I will do my best to guide you. Thank you!