![]() Slicing the table based on the date and period selected and adding the sales amount grouped by Salesperson and product division. ![]() In the following steps, we try to understand the above DAX code in multiple phases. Similarly, the last line of code is changed to obtain the middle 60% and the bottom 20%. The measure’s output will be the number of salespersons who contributed to the top 20% of the total sales amount. We must sort the salespersons based on their sales amount before we compare the totals. To make it easy to understand the requirement, I mentioned that salespersons are in the order of the amount they sold in the original data, this is not the case. If ID 1 and 2 together made a sale amount of $20 or greater than $20, then the number of salespersons contributed for the top 20% of the total sales amount is 2. If the sales amount of ID 1 is $20, then the number of salespersons contributed for the top 20% of the total sale amount is 1. Let’s assume the total sale amount to be $100. ![]() Consider ten salespersons with ID 1 to 10, ID 1 having the highest sales, and 10 having the lowest deals. To make it more transparent, let us take an example. The customer is interested in visualising a pie chart that displays the number of salespersons who contributed to the top 20%, middle 60%, and bottom 20% of the total Sales Amount for the chosen month and time period (MTD, YTD, etc.). Thank you for taking the time to read this blog. In this blog, we will try to understand how table variables are correlated to the piping functions in R and Python. We build these numbers using a query language called DAX. Although they play a significant role in creating the first impression, businesses are always keen on the numbers or KPIs they convey. Visualisations make a Power BI report look great. Industry Trends and News, Insights Roundup, Products & Solutions, Technical How to Implement Table Variables in Power BI with Dax Function
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