Count Distinct Percent of Parent and Count Distinct Percent of Total

Count Distinct Percent of Parent is the Count Distinct expressed as a percentage share of the Count Distinct as the parent node level. While Count Distinct Percent of Total is the Count Distinct expressed as a percentage share of the Count Distinct
in the total data table.

Sample dataset:

Auto Key	Row Count	CustomerID	Product Downloaded	Product Family	Product Vendor	Timestamp
1	1	c1	Prod X	Fam A	Boogle	03/04/2022
2	1	c1	Prod Y	Fam A	Boogle	02/04/2022
3	1	c1	Prod Z	Fam B	Boogle	29/04/2022
4	1	c2	Prod X	Fam A	Boogle	04/04/2022
5	1	c2	Prod Y	Fam A	Boogle	04/04/2022
6	1	c2	Prod Z	Fam B	Boogle	18/04/2022
7	1	c3	Prod X	Fam A	Boogle	10/04/2022
8	1	c3	Prod Y	Fam A	Boogle	11/04/2022
9	1	c3	Prod Z	Fam B	Boogle	01/04/2022
10	1	c4	Prod Z	Fam B	Boogle	01/04/2022
11	1	c91	Prod X	Fam A	Boogle	15/03/2022
12	1	c91	Prod X	Fam A	Boogle	15/04/2022
13	1	c91	Prod X	Fam A	Boogle	15/05/2022
14	1	c91	Prod X	Fam A	Boogle	15/06/2022
15	1	c91	Prod X	Fam A	Boogle	16/06/2022
16	1	c92	Prod Y	Fam A	Boogle	01/05/2022
17	1	c92	Prod Z	Fam B	Boogle	01/05/2022
18	1	c92	Prod Y	Fam A	Boogle	06/05/2022
19	1	c92	Prod Z	Fam B	Boogle	03/06/2022
20	1	c92	Prod Z	Fam B	Boogle	06/06/2022

This dataset contains information about software product downloads.

Count	Details
1	Product vendor
2	Product families
3	Products
6	Customers
3	Month and Year time periods
20	Unique download occasions

Below is the defined breakdown in a Table visualization:

The added columns have the following aggregations:

Column	Aggregate
Row Count	Sum
CustomerID	Count Distinct
CustomerID	Count Distinct Percent of Parent
CustomerID	Count Distinct Percent of Total

By counting the rows in the dataset, we can count how many different download occasions we have.

By doing Count Distinct on CustomerID, we find out how many unique customers there are, regardless of how many times each customer did a download.

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