Power Query is a powerful tool in Microsoft Excel that enables users to import, transform, and manipulate data efficiently. One common challenge many users face is maintaining blank rows in their datasets, especially when the original worksheet lacks data in certain areas. This article will explore how to achieve this in Power Query, ensuring that your final output retains those important blank rows for easier data analysis.
The Problem Scenario
You have a dataset in Excel that requires cleaning and transformation via Power Query. However, upon loading the data into Power Query, you notice that any blank rows from the original dataset are omitted. This results in a dataset that is more compact but less informative if those blank rows hold significance for your data structure.
Original Code Example:
let
Source = Excel.CurrentWorkbook(){[Name="YourTableName"]}[Content],
RemovedEmpty = Table.SelectRows(Source, each List.NonNullCount(Record.FieldValues(_)) > 0)
in
RemovedEmpty
In this example, the original code is set to remove empty rows by utilizing List.NonNullCount. This line of code effectively filters out rows that do not have any data, which is why blank rows are not visible in the final output.
Solution: Keeping Blank Rows
To keep blank rows when importing data into Power Query, you can modify your approach. The goal is to use the original dataset to create an index and then join it with the modified data that does not include blanks. Here’s how you can do it step-by-step:
-
Load Your Data: First, load your dataset into Power Query as you normally would.
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Add an Index Column: While in Power Query, add an index column to your original data. This can be done by navigating to the "Add Column" tab and selecting "Index Column."
-
Remove Empty Rows: Proceed to remove empty rows as usual using the
Table.SelectRowsfunction. -
Merge Queries: After removing the empty rows, merge this table with the original dataset using the index column. This will bring back the blank rows.
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Final Cleanup: After merging, you may need to clean up the dataset again to adjust any other unwanted columns or values.
Updated Code Example:
let
// Load your original data
Source = Excel.CurrentWorkbook(){[Name="YourTableName"]}[Content],
// Add an index column
AddIndex = Table.AddIndexColumn(Source, "Index", 0, 1, Int64.Type),
// Remove empty rows
RemovedEmpty = Table.SelectRows(AddIndex, each List.NonNullCount(Record.FieldValues(_)) > 0),
// Merge back with the original dataset based on Index
MergedData = Table.Join(AddIndex, "Index", RemovedEmpty, "Index", JoinKind.FullOuter),
// Remove Index column if not needed
FinalTable = Table.RemoveColumns(MergedData, {"Index"})
in
FinalTable
Practical Examples
Use Case Scenario:
Imagine you are tracking employee attendance over a month, where some days may have no entries. Keeping these blank rows can help visualize attendance trends without misrepresenting data. If you were to omit them, analysis would only reflect days with entries, thus leading to skewed results.
Added Value:
By keeping blank rows, users can gain insights into the regularity and occurrence of data over time. Additionally, they can improve their workflow, especially when sharing reports with stakeholders who need to understand the context of data absence.
Conclusion
Maintaining blank rows in Power Query when there’s no data from the original worksheet is essential for certain analyses and presentations. By utilizing an index and merging techniques, you can ensure that your dataset retains its structural integrity, making it easier for stakeholders to comprehend the data context.
Useful Resources
By implementing these steps and practices, you will enhance your data processing capabilities within Excel, making your reports more informative and user-friendly.
