Mastering Power BI: How to Create and Optimize Calculated Columns from the Same Table
Creating calculated columns in Power BI is a foundational skill for any data analyst or business intelligence professional. The ability to add and manipulate these columns allows for deeper insights and more dynamic reports. While there are many ways to approach this task, this article focuses on a specific method using Power Query and M code to create calculated columns from the same table, optimizing your Power BI projects for better performance and clarity.
Why You Should Optimize Calculated Columns in Power BI
Optimizing calculated columns is crucial for several reasons:
- Improved Performance: Optimized calculations ensure your Power BI reports load faster, providing a better user experience.
- Enhanced Data Insights: With precise calculations, you can unearth more relevant insights, leading to more informed decision-making.
- Maintenance and Scalability: Optimized calculations are easier to maintain and update, making your Power BI projects more scalable.
How to Make Calculated Columns from the Same Table
Here's a step-by-step guide to creating calculated columns from the same table using Power Query and M code.
Step 1: Starting with Your Table
Assume you have a primary table in your Power BI project (let's call it Table1). This table is the base from which you'll create additional calculated columns.
Step 2: Creating a Reference Table
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Reference Your Initial Table: Right-click on Table1 and select "Reference" to create a new table (Table2) based on Table1. This process doesn't duplicate the data but creates a reference to the original data.
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Transform Table2: Apply transformations to Table2, such as sorting and adding indexes. The transformation steps include sorting rows by specific columns and adding an index column that serves as a unique identifier for each row.
Here's an example of the M code for these transformations:
let
Source = Table1,
#"Sorted Rows" = Table.Sort(Source, {{"Id", Order.Ascending}, {"title", Order.Ascending}, {"Parent Id", Order.Ascending}}),
#"Added Index" = Table.AddIndexColumn(#"Sorted Rows", "Index", 1, 1),
#"Grouped Rows" = Table.Group(#"Added Index", {"Id"}, {{"MinIndex", each List.Min([Index]), type number}, {"MaxIndex", each List.Max([Index]), type number}, {"AllData", each _, type table}}),
#"Expanded AllData" = Table.ExpandTableColumn(#"Grouped Rows", "AllData", {"title", "Parent Id", "Index"}, {"title", "Parent Id", "Index"}),
#"Added Custom" = Table.AddColumn(#"Expanded AllData", "MaxTitle", each if [MaxIndex]=[Index] then [title] else null),
#"Filled Up" = Table.FillUp(#"Added Custom", {"MaxTitle"}),
#"Added Custom1" = Table.AddColumn(#"Filled Up", "LesserMaxIndex", each [MinIndex]-1)
in
#"Added Custom1"
Step 3: Merging Tables for Calculated Columns
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Create Another Table Reference: Use "Reference" again to create Table3 from Table2. The M code for Table3 can be remarkably simple, essentially copying the data from Table2.
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Merge Table2 and Table3: Use the "Merge Queries as New" option under the "Home" tab to merge Table2 and Table3 based on the criteria that matches LesserMaxIndex from Table2 with MaxIndex from Table3, applying a Left Outer join.
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Clean Up the Merged Table: After merging, you may need to clean up the resulting table (Merge1) by expanding columns, sorting rows, removing duplicates, and selecting relevant columns only.
Here is an example of the M code for the cleanup process:
let
Source = Table.NestedJoin(Table2, {"LesserMaxIndex"}, Table3, {"MaxIndex"}, "Table1 (4)", JoinKind.LeftOuter),
#"Expanded Table1 (4)" = Table.ExpandTableColumn(Source, "Table1 (4)", {"MaxTitle"}, {"ParentTitle"}),
#"Sorted Rows" = Table.Sort(#"Expanded Table1 (4)", {{"Id", Order.Ascending}, {"title", Order.Ascending}, {"Parent Id", Order.Ascending}}),
#"Removed Duplicates" = Table.Distinct(#"Sorted Rows"),
#"Removed Other Columns" = Table.SelectColumns(#"Removed Duplicates", {"Id", "title", "Parent Id", "ParentTitle"})
in
#"Removed Other Columns"
This process may seem complex, but it provides a high level of control and flexibility over calculated columns in Power BI.
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Conclusion
Adding and optimizing calculated columns from the same table in Power BI can enhance your reporting and analytics significantly. The steps outlined above, including creating reference tables and using Power Query with M code, offer a detailed pathway to achieving robust and optimized calculated columns in your Power BI projects.
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Through meticulous attention to detail and a comprehensive understanding of Power BI's capabilities, you can unlock new levels of analytics and reporting excellence, driving more informed and effective decision-making across your organization.