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Hi!
Ive spent a few hours looking through similar posts but cant find a solution that's relevant to my issue.
I am trying to create a Matrix visual to show the percentage of blank cells in each column in a very large data set that needs to be grouped by city (SITE_CODE).
Here is a sample dataset. The actual dataset has over 250k rows and over 50 columns but is setup similarly with SITE_CODE containing no null values.
SITE_CODE | INVOICE_ID | MARKET | CUST_ID | ORDER_ID | Product |
12 | x | ||||
11 | INV | x | gdcc2323 | ||
12 | INV | x | 123321 | pen | |
13 | INV | ||||
13 | INV | xy | |||
12 | INV | xy | paper | ||
13 | INV | xy | 123322 | ||
13 | INV | ||||
12 | INV | x | ijs1172 | ||
11 | paper |
I have tried to unpivot the data and use the countblanks() formula but that doesn't work and I run into a resource limit issue. Similarly the " is blank " filter doesn't seem to be working at all when i create a matrix off the original dataset. Ideally I would like the resulting dataset to look something like this.
SITE_CODE - >>> | 11 | 12 | 13 | 14 | 15 |
INVOICE_ID | % | % | % | % | % |
MARKET | % | % | % | % | % |
CUST_ID | % | % | % | % | % |
ORDER_ID | % | % | % | % | % |
Product | % | % | % | % | % |
Any advice or help would be appreciated. In the future I would like to also be able to add the ability to calculate these percentages based on a specific error value to aid in this data cleansing process.
Thanks!
Solved! Go to Solution.
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