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I´m (more or less) an absolut PBI newbie and need some help.
I have a list with amounts and deadlines (in the past and upcoming) and calculated with DATEDIFF the corresponding due days (a few thousand values between 400 and -120). Now I would like to sort them into different groups/columns according to the due date.
I tried to rebuild in Excel how it should look like in the end (I have green part and trying to build the grey one):
In theory and Excel it is very easy .... but not in PBI without the neccessary PBI knowledge. 😉
I already tried to create a "conditional column" but I can not select the "due days" as it is a calculated column and not part of the Power Query Editor.
Solved! Go to Solution.
Thanks for your support!
Meanwhile I adjusted the data table a little bit and integrated xthe calculation of the due days with a measures. This step enables the possibility to insert the groups by "conditional column". Maybe not the professional way but for me the most logical one. 😉
Thanks for your support!
Meanwhile I adjusted the data table a little bit and integrated xthe calculation of the due days with a measures. This step enables the possibility to insert the groups by "conditional column". Maybe not the professional way but for me the most logical one. 😉
Hi @BlueSkyX
You can consider to create a calculated column with siwtch() funcion.
e.g
Type =
SWITCH (
TRUE (),
[Due Days] >= 1
&& [Due Days] <= 5, "Due 1-5",
[Due Days] >= 6
&& [Due Days] <= 10, "Due 6-10",
[Due Days] >= 11, "Due >11"
)
Then you can put them into a matrix visual
e.g
Best Regards!
Yolo Zhu
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @BlueSkyX
If Due Days is a column you can use bins, please refer to the linked tutorial :
https://tic.li/c/51714/3Dm72QxG6J
If it is a measure you can use workarounds of the segmentation:
https://www.youtube.com/watch?v=0KBR-aEVaxk
If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly
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