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asif082
Frequent Visitor

Merge Rows with data from three Table Urgent help please!

Hi Everyone,

 

I have 3 tables which producing below result.

 

 Current Outcome

Original Data.PNG

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Desire Result

 

what I need is I want to merge duplicate ID in two one row where it shows Full Time and Part Time in one row.

 

Desire Data.PNG

 

If any one knows how can I achive this please

 

4 REPLIES 4
v-danhe-msft
Microsoft Employee
Microsoft Employee

Hi @asif082,

Could you please tell me if your problem has been solved? If it is, could you please mark the helpful replies as Answered?

 

Regards,

Daniel He

Community Support Team _ Daniel He
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
MFelix
Super User
Super User

Hi @asif082,

 

On your Current Outcome do a grouping by then add all your columns to the Goup by option and on the Part Time select the option MAX.

 

This should give expected result.

 

Regards,

MFelix


Regards

Miguel Félix


Did I answer your question? Mark my post as a solution!

Proud to be a Super User!

Check out my blog: Power BI em Português



Hi,

Thanks for the reply, but the problem is all the names come from Employee Table.

 

so I have these 4 tables, Position, engagement, Grades and Employee

 

Enagement is where all the data is link via ID's from those three table. 

 

so the screen shot I have sent earlier is actually a Table visualization in power BI  and when I go in edit quries mode how I group by from different tables?

 

Thanks

 

Hi @asif082,

From your description, you could also follow the MFelix 's function to group your data by [ID] due to your row data table have no [Name] column, if it still could not solve your problem, could you please offer some sample data to have a test?

 

Regards,

Daniel He

Community Support Team _ Daniel He
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

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