Cantrecallmyusername
Board Regular
- Joined
- May 24, 2021
- Messages
- 50
- Office Version
- 365
- Platform
- Windows
Hi,
I have a file which has ~10K rows of data.
I want to create a Pivot table removing duplicated values based on the name column this gives me ~2k but I want to capture the data that is derived in a column that contains a tag.
The problem is that when the tag is generated there are many instances where the name has 1:many generated which I loose when removing duplicates on the name.
The pivot looks like this with no changes made;
When I do a simple remove dupes based on a name value which is what is deriving the count in both pivots I get
Is there a way to represent the data that will capture my unique count on name but generate the tag count accurately - currently both versions here are incorrect to a degree
Ideally I want to have a count of how many assets are in a container (unique values) and how many tags at the different levels are outputted per container.
Is there a way to approach this is the data generation that may help?
Any help very much appreciated.
I have a file which has ~10K rows of data.
I want to create a Pivot table removing duplicated values based on the name column this gives me ~2k but I want to capture the data that is derived in a column that contains a tag.
The problem is that when the tag is generated there are many instances where the name has 1:many generated which I loose when removing duplicates on the name.
The pivot looks like this with no changes made;
When I do a simple remove dupes based on a name value which is what is deriving the count in both pivots I get
Is there a way to represent the data that will capture my unique count on name but generate the tag count accurately - currently both versions here are incorrect to a degree
Ideally I want to have a count of how many assets are in a container (unique values) and how many tags at the different levels are outputted per container.
Is there a way to approach this is the data generation that may help?
Any help very much appreciated.