Cleaning and transforming data is a crucial step in Power BI reporting. Conditional Columns in Power Query allow you to apply logic-based transformations without complex coding. You can create rules that automatically categorize, modify, or clean data dynamically.

In this guide, you’ll learn how to:
✔️ Add Conditional Columns in Power Query
✔️ Use IF-THEN logic for data cleaning
✔️ Apply multiple conditions for advanced transformations

1. What Are Conditional Columns in Power Query?

A Conditional Column lets you define if-then-else logic to modify or categorize data dynamically. This is useful for:

✅ Fixing inconsistent data (e.g., replacing errors or missing values)
✅ Classifying data (e.g., assigning labels based on numerical ranges)
✅ Cleaning text fields (e.g., standardizing spelling variations)

Instead of manually editing records, Power Query automates data transformation with rule-based logic.

2. How to Add a Conditional Column in Power Query

Follow these steps to create a Conditional Column in Power Query:

Step 1: Open Power Query Editor

• In Power BI, go to the Home tab and select Transform Data to open Power Query Editor.

Step 2: Select a Table & Add a New Column

• Choose the table where you want to apply conditional logic.
• Go to the Add Column tab and select Conditional Column.

Step 3: Define Conditional Rules

• In the Conditional Column dialog box:
  1. Enter a New Column Name.
  2. Select a column for evaluation.
  3. Choose an Operator (e.g., equals, contains, greater than).
  4. Define Value & Output based on conditions.
  5. Click OK to apply the rule.

Example: If the Sales Amount is greater than 1000, label it as “High”, otherwise “Low”.

3. Example Use Cases of Conditional Columns

📌 Example 1: Categorizing Sales Data

Scenario: You have a sales dataset and want to classify transactions into categories:
✔️ High Sales (above $10,000)
✔️ Medium Sales ($5,000 – $10,000)
✔️ Low Sales (below $5,000)

Solution:

• Create a Conditional Column:
  • If Sales > 10000, output “High”
  • Else If Sales > 5000, output “Medium”
  • Else output “Low”

📌 Example 2: Handling Missing Data with Defaults

Scenario: Some customer records have missing Region values.
Solution: Use a Conditional Column to fill missing values with a default “Unknown” category.

• If Region is null, replace with “Unknown”
• Otherwise, keep the original Region

📌 Example 3: Standardizing Text Entries

Scenario: Your dataset contains different spellings for “Yes” responses (e.g., “Yes”, “Y”, “YES”).
Solution: Create a Conditional Column:

• If Response is “Y” or “YES”, replace with “Yes”
• Otherwise, keep the existing value

4. Using Advanced Conditional Logic with Multiple Conditions

You can apply multiple conditions within the same Conditional Column:

✔ Using AND conditions: (e.g., if Sales > 5000 AND Customer = "VIP")
✔ Using OR conditions: (e.g., if Category = "A" OR Category = "B", set as “Priority”)

For complex logic, you can use Custom Columns with M language instead of Conditional Columns.

5. Best Practices for Using Conditional Columns in Power Query

✅ Plan Your Logic First – Define your conditions before adding them.
✅ Use Else Statements – Always include a default value in case none of the conditions match.
✅ Keep It Simple – Avoid overcomplicating rules. If logic becomes too complex, consider using a Custom Column.
✅ Optimize for Performance – Apply transformations before loading large datasets into Power BI.

Conclusion

🚀 Conditional Columns in Power Query simplify data cleaning, categorization, and transformation with rule-based logic. Whether you need to replace missing values, classify sales, or standardize text, this feature automates and optimizes your data workflows.

By mastering Conditional Columns, you can create smarter, more dynamic Power BI reports with cleaner data.