This is best Fabric, Power BI, SQL and AI community event. How do we know? The last event sold out! Save €200 with code FABCMTY200.
Register nowA new Data Days event is coming soon! This time we’re going bigger than ever. Fabric, Power BI, SQL, AI and more. Don't miss out.
HI All,
Issue:
I have 6 columns with items listed, some duplicates over different rows. I am needing to count the items across the 6 columns, display them in a single table, then count them
Example of Data:
ID | Item 1 | Item 2 | Item 3 | Item 4 | Item 5 | Item 6 |
01 | Household Essentials | Vehicle & Transport | ||||
| 01 | Furniture | Repair & Maintenance | ||||
| 01 | Baby Furniture & Items | Repair & Maintenance | ||||
| 02 | Education & Employment | |||||
| 02 | Technology | |||||
| 02 | Tablet / Laptop | |||||
| 03 | Vehicle & Transport | Vehicle & Transport | ||||
| 03 | Vehicle Registration | Vehicle Registration | ||||
| 03 | Car Registration | CTP |
Desired outcome:
| Items | Count |
| Household Essentials | 1 |
| Car Registration | 1 |
| Vehicle & Transport | 3 |
| Tablet / Laptop | 1 |
| Technology | 1 |
| Education & Employment | 1 |
| Baby Furniture & Items | 1 |
| Furniture | 1 |
| Repair & Maintenance | 2 |
| CTP | 1 |
Background:
I have a large spreadsheet that has items in 4 columns that were separated by a dash. This column was split and rows duplicated so that there is multiple rows with the same ID, however now one Item 1 on each row. the same for Item 2.
Any help would be amazing thanks
Solved! Go to Solution.
Hi @JMMSSAU , Before counting, I'd suggest first unpivoting the 6 item columns into a single column ,this makes the count much simpler and cleaner to work with.
In Power Query:
1. Select all 6 item columns (Item 1 through Item 6)
2. Right-click → Unpivot Columns
3. This gives you a single Value column with all items stacked
4. Remove the Attribute column (which just says Item 1, Item 2 etc.)
5. Filter out any blank/null rows
6. Then group by the Value column and count rows
Once everything is in one column, a simple **Group By** gives you exactly the Items + Count table you're after — no complex cross-column logic needed.
Hope that helps!
Thanks!
Natarajan Manivasagan
If you found this helpful, please consider giving it a Kudos and marking it as the accepted solution — it goes a long way in helping others facing the same issue.
🏆 Best Solution for Enterprise BI — 2026 Microsoft Fabric Semantic Link Developer Experience Challenge
👉 Microsoft announcement · View the winning notebook
For more Power BI tips and discussions, let's connect on LinkedIn.
Cheers!
That's brilliant thanks.
Hi @JMMSSAU , Before counting, I'd suggest first unpivoting the 6 item columns into a single column ,this makes the count much simpler and cleaner to work with.
In Power Query:
1. Select all 6 item columns (Item 1 through Item 6)
2. Right-click → Unpivot Columns
3. This gives you a single Value column with all items stacked
4. Remove the Attribute column (which just says Item 1, Item 2 etc.)
5. Filter out any blank/null rows
6. Then group by the Value column and count rows
Once everything is in one column, a simple **Group By** gives you exactly the Items + Count table you're after — no complex cross-column logic needed.
Hope that helps!
Thanks!
Natarajan Manivasagan
If you found this helpful, please consider giving it a Kudos and marking it as the accepted solution — it goes a long way in helping others facing the same issue.
🏆 Best Solution for Enterprise BI — 2026 Microsoft Fabric Semantic Link Developer Experience Challenge
👉 Microsoft announcement · View the winning notebook
For more Power BI tips and discussions, let's connect on LinkedIn.
Cheers!
Check out the May 2026 Power BI update to learn about new features.
Sign up to receive a private message when registration opens and key events begin.
If you have recently started exploring Fabric, we'd love to hear how it's going. Your feedback can help with product improvements.
| User | Count |
|---|---|
| 31 | |
| 26 | |
| 23 | |
| 22 | |
| 13 |
| User | Count |
|---|---|
| 61 | |
| 47 | |
| 27 | |
| 23 | |
| 18 |