# **How to Transform Data within a Cell Using Power Query: A Step-by-Step Guide**
Harnessing the power nestled within each cell of your data tables in Power BI can redefine the way you interpret and display your data. The key lies in understanding how to adeptly use Power Query to pass tables within a cell to a custom function. This guide will walk you through a streamlined solution that involves unpivoting your columns, renaming them, and then pivoting again to combine distinct values effectively.
## **The Scenario and Solution**
Imagine you have a dataset where multiple attributes of a company are packed into individual cells. To analyze or visualize this data in a more meaningful way, you need to unpack these attributes and treat them individually. This is where the presented solution shines, presenting a simpler approach to unlock the data's potential.
### **Step-by-Step Process**
1. **Unpivot Other Columns**
First, we'll start with our source table, referred to in our query as `Table1`. We aim to unpivot columns other than "Company ID" and "Company". Unpivoting transforms columns into rows, effectively turning our packed attributes into a list form.
```powershell
let
Source = Table1,
#"Unpivoted Other Columns" = Table.UnpivotOtherColumns(Source, {"Company ID", "Company"}, "Attribute", "Value")
in
#"Unpivoted Other Columns"
```
2. **Renaming Attributes**
After unpivoting the columns, the next order of business is renaming the attributes for clarity. This involves tweaking attribute names to make them more informative.
```powershell
#"Renamed Attributes" = Table.TransformColumns(#"Unpivoted Other Columns", {{"Attribute", each "s" & Text.BetweenDelimiters(_, "_", "_"), type text}})
```
3. **Pivoting the Column**
The final step in our data transformation is to pivot the column. This means transforming our listed attributes back into columns, but now, each cell under these columns will contain distinct values combined as needed.
```powershell
#"Pivoted Column" = Table.Pivot(#"Renamed Attributes", List.Distinct(#"Renamed Attributes"[Attribute]), "Attribute", "Value", each Text.Combine(List.Distinct(_),", "))
```
### **Additional Tweaks**
While the above steps create a robust baseline, you might encounter instances where further refinement is necessary, such as removing blank values, sorting, and custom value separators.
- **Removing Empty Values**
```powershell
#"Removed Empty Values" = Table.SelectRows(#"Unpivoted Other Columns", each [Value] <> null and [Value] <> "")
```
- **Sorting Attributes**
```powershell
#"Sorted Attributes" = Table.Sort(#"Renamed Attributes",{{"Attribute", Order.Ascending}})
```
- **Custom Separators for Values**
```powershell
#"Pivoted Column" = Table.Pivot(#"Sorted Attributes", List.Distinct(#"Sorted Attributes"[Attribute]), "Attribute", "Value", each Text.Combine(List.Sort(List.Distinct(_)),"#(lf)"))
```
## **Conclusion**
The power of Power Query in Power BI cannot be overstated. Its ability to manipulate and transform data in such flexible and powerful ways opens up a plethora of opportunities for data analysis and report generation. Through the steps outlined above, you can effectively pass tables within a cell to a custom function, enhancing your data's clarity and utility.
Merely understanding these techniques is not the end but the beginning. Tools like [Flowpoint.ai](https://flowpoint.ai) can further assist by identifying all technical errors impacting conversion rates on your site and directly generating recommendations to fix them. Thus, complementing your data manipulation skills with powerful analytics tools can pivot your data analysis journey from good to exceptional.
Remember, every dataset has a story to tell, and with tools like Power Query and platforms like Flowpoint, you're well-equipped to narrate these stories compellingly.