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Abhi_2022's avatar
Abhi_2022
Frequent Visitor
3 years ago

Using if condition in New measure in a table visual

Hi,
There are 18 columns and each columns has at max 5 categorical values. After selecting a particular zip code I want to show only those columns and their values which are of certain categorical value. So, basically I want to filter or put condition and show only those columns  in the table visualization that satifies the condition. I guess I need to create a measure for this but not sure how to approach it. if anyone could guide it will be helpful.
Thanks!

2 Replies

  • v-yalanwu-msft's avatar
    v-yalanwu-msft
    Icon for Community Support rankCommunity Support

    Hi, Abhi_2022 ;

    I have some confusion about your description, can you take a simple scene or example, and hope to output the results? With logic.

    please provide some sample data in your tables (exclude sensitive data) with Text format and your expected result with backend logic and special examples. It is better if you can share a simplified pbix file. You can refer the following link to upload the file to the community. Thank you.

    How to upload PBI in Community
    Best Regards,
    Community Support Team _ Yalan Wu
    If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

    • Abhi_2022's avatar
      Abhi_2022
      Frequent Visitor

      Hi v-yalanwu-msft 
      Thanks for the response.

      Following is the sample of the table visual I get after selecting a particular region on map or slicer. Now, I don't want all 18 columns to be displayed. I just want those columns which contain values "Very High", "Relatively High" and "Relatively Moderate". 
      These columns may differ depending on the region I select. For example, in Florida there is no risk of Avalanches at all. So the value for AVLN_RISKR will be NA_NR_ID (NULL basically). Similarly, there are some other risks that are not relevant to Florida, and they will show "NA_NR_ID" values or "Very Low". Therefore, I want to show only the columns relevant to that region and not all 18 columns.  Please let me know if you have any questions.

      zipAVLN_RISKRCFLD_RISKRCWAV_RISKRDRGT_RISKRERQK_RISKRHAIL_RISKRHRCN_RISKRHWAV_RISKRISTM_RISKRLNDS_RISKRLTNG_RISKRRFLD_RISKRSWND_RISKRTRND_RISKRTSUN_RISKRVLCN_RISKRWFIR_RISKRWNTW_RISKR
      12836Relatively HighNR_NA_IDRelatively LowNR_NA_IDRelatively LowRelatively LowRelatively LowRelatively HighRelatively ModerateRelatively HighRelatively HighRelatively HighRelatively LowRelatively LowVery HighNR_NA_IDVery LowRelatively Moderate
      12853Very HighNR_NA_IDRelatively ModerateNR_NA_IDRelatively LowVery LowVery LowRelatively ModerateRelatively LowRelatively ModerateRelatively HighRelatively ModerateRelatively LowRelatively LowVery HighNR_NA_IDVery LowRelatively Low
      12861Relatively HighNR_NA_IDRelatively LowNR_NA_IDRelatively LowRelatively LowRelatively LowRelatively HighRelatively LowRelatively HighRelatively HighRelatively HighRelatively LowRelatively LowVery HighNR_NA_IDVery LowRelatively Moderate
      12911Relatively HighNR_NA_IDRelatively LowNR_NA_IDRelatively LowRelatively LowVery LowNR_NA_IDRelatively ModerateRelatively ModerateRelatively LowRelatively ModerateRelatively LowVery LowVery HighNR_NA_IDVery LowRelatively Low
      12912Relatively HighNR_NA_IDRelatively LowNR_NA_IDRelatively LowVery LowVery LowNR_NA_IDRelatively ModerateRelatively ModerateRelatively LowRelatively ModerateRelatively LowVery LowVery HighNR_NA_IDVery LowRelatively Moderate
      12913Relatively HighNR_NA_IDRelatively ModerateNR_NA_IDRelatively LowVery LowVery LowNR_NA_IDRelatively ModerateRelatively ModerateRelatively LowRelatively ModerateRelatively LowVery LowVery HighNR_NA_IDVery LowRelatively Moderate
      12972Very HighNR_NA_IDRelatively LowNR_NA_IDRelatively LowRelatively LowVery LowNR_NA_IDRelatively ModerateRelatively ModerateRelatively LowRelatively ModerateRelatively LowVery LowVery HighNR_NA_IDVery LowRelatively Moderate
      12983Relatively HighNR_NA_IDRelatively ModerateNR_NA_IDRelatively LowVery LowVery LowNR_NA_IDRelatively ModerateRelatively ModerateRelatively LowRelatively ModerateRelatively LowVery LowVery HighNR_NA_IDRelatively LowRelatively Moderate
      12989Relatively HighNR_NA_IDRelatively LowNR_NA_IDRelatively LowVery LowVery LowNR_NA_IDRelatively ModerateRelatively HighRelatively LowRelatively ModerateRelatively LowVery LowVery HighNR_NA_IDVery LowRelatively Moderate
      16652Very HighNR_NA_IDRelatively LowNR_NA_IDVery LowVery LowRelatively LowNR_NA_IDVery LowRelatively ModerateRelatively ModerateRelatively LowRelatively ModerateRelatively LowVery HighNR_NA_IDVery LowRelatively Moderate
      16666Relatively HighNR_NA_IDRelatively ModerateNR_NA_IDVery LowVery LowRelatively LowNR_NA_IDVery LowRelatively ModerateRelatively ModerateRelatively ModerateRelatively LowRelatively LowVery HighNR_NA_IDVery LowRelatively Moderate
      16686Very HighNR_NA_IDRelatively ModerateNR_NA_IDVery LowVery LowRelatively LowNR_NA_IDVery LowRelatively ModerateRelatively ModerateRelatively ModerateRelatively LowRelatively LowVery HighNR_NA_IDRelatively ModerateRelatively Moderate