How to Add Conditional Columns In Power BI?

How to Add Conditional Columns In Power BI?

Imagine transforming your data reports with dynamic, context-specific insights. That is what the conditional columns in Power BI does. These versatile tools allow you to create new columns whose values adapt based on your defined conditions. No more static data with power bi conditional column; you can personalized visualizations and have deeper data understanding. 

Learn the Power BI conditional column and business analysis with Be10x Power BI Workshop.


What are Power BI Conditional Columns? 

Power BI Conditional columns are a feature in data analysis tools like Power BI that allows users to create new columns in a dataset based on specified conditions. These conditions are defined using logical expressions, and the resulting column values are determined by whether the conditions are met or not. For example, you could create a column that categorizes customers as “high-value” if their purchase exceeds a certain amount or flags potential sales risks based on specific criteria. 

Two Ways to Create Power BI Conditional Columns are

  1. Power BI Conditional Column using GUI: Power BI offers a built-in feature for adding conditional columns through a simple point-and-click interface. Ideal for basic scenarios and quick explorations. 
  1. Power BI Conditional Column using DAX: For complex conditions and advanced customization, use DAX formula language. This allows intricate calculations and tailored logic for your specific needs. 
conditional columns

Why Use Conditional Columns in Power BI? 

Power BI conditional columns offer various benefits:

  • Enhanced analysis: Gain deeper insights by highlighting particular data points based on conditions. 
  • Clearer visualizations: Use conditional formatting to color-code or categorize data for visually impactful reports.
  • Improved decision-making: Identify trends, patterns, and anomalies more quickly.
  • Greater flexibility: Adapt your reports to different audiences and analysis needs by showcasing relevant data subsets. 

Adding Power BI Conditional Columns with Multiple Conditions Using the GUI

Power BI makes it convenient to add conditional columns through the GUI. The “Add Column” > “Conditional Column” feature allows users to implement complex logic quickly. Below are the steps to add a conditional column with multiple conditions:

  1. Open Power BI Desktop and load your datasets. Go to the “Transform Data” Option under the “Home” tab in the Power BI Desktop to open the Power Query Editor.
  1. In the Power Query Editor, choose the table that contains the data to which you want to add a conditional column.
  1. To Initiate the Power BI conditional column creation, click “Add Column” under the “Home” tab. Select “Conditional Column” from the dropdown menu.
  1. In the “New column name” field, enter a name for your new conditional column. In the “If” dropdown, choose the first condition column. Set the operator (equals, greater than, etc.) and provide the value for the condition.
  1. To add more conditions, use the “AND/OR” dropdown. Choose whether the new condition should be combined with the previous one using “AND” or “OR.”
  1. For more complex logic, click on the “Add Rule” button. This allows you to create nested “IF” statements, adding layers of conditions for intricate scenarios.
  1. For each condition, specify the outcome or value if the condition is met. Continue this process for all conditions you want to include.
  1. eview the logic in the “Conditional Column” dialog box to ensure accuracy. Click “OK” to confirm and apply the changes.

Example with Power Bi conditional column using GUI:

Let’s consider an example where we want to create a column called “Customer Segment” based on two conditions: “Total Sales” and “Number of Orders.”

  1. If Total Sales > $50,000 and Number of Orders > 10, then Customer Segment is “High Value.”
  2. If Total Sales > $30,000 and Number of Orders > 5, then Customer Segment is “Medium Value.”
  3.  If neither condition is met, Customer Segment is “Low Value.”

Following the steps outlined above, users can easily set up such multiple conditions using the GUI, allowing for precise and customizable data categorization.

Adding Power BI Conditional Columns with Multiple Conditions Using the DAX


DAX is a formula language used in Power BI to create custom calculations and express complex logic. It plays a vital role in enhancing data modeling and analysis. DAX formulas are commonly used to add calculated columns, measures, and tables to a Power BI dataset, providing flexibility for tailored data manipulation.

Assume we want to create a conditional column named “Customer Segment” based on “Total Sales” and “Number of Orders.” The logic is similar to the GUI example.

Customer Segment =


    ‘Table'[Total Sales] > 50000 && ‘Table'[Number of Orders] > 10, “High Value”,


        ‘Table'[Total Sales] > 30000 && ‘Table'[Number of Orders] > 5, “Medium Value”,

        “Low Value”



Logic Behind the DAX Formula and Its Components are:

  • `IF`: The main function that evaluates the conditions and returns the specified value if true or another if false.
  • `&&`: Represents the logical AND operator, ensuring both conditions are met.
  • `ELSE IF`: Achieved through nested `IF` statements. If the first condition is tested false, the formula will checks the second condition.
  • `”High Value”,` `”Medium Value”,` `”Low Value”`: The outcomes if the conditions are met. These could be any desired values.

Power BI Conditional Columns: Specific Use Cases

Here are the Power BI conditional columns Specific cases:

Power BI Conditional Column Based on Two Columns Method

Example Scenario: Consider a retail dataset with “Units Sold” and “Discount Percentage” columns. You want to create a conditional column named “Discount Level” based on the interaction of these two columns:

  • If Units Sold > 100 and Discount Percentage > 10%, then “High Discount.”
  • If Units Sold > 50 and Discount Percentage > 5%, then “Medium Discount.”
  • Otherwise, “Low Discount.”

Power BI Conditional Column Using GUI:

  1. Open Power BI Desktop, load your dataset, and go to the “Transform Data” option.
  2. Choose the relevant table.
  3. Under “Home,” click “Add Column” > “Conditional Column.”
  4. Define conditions and outcomes based on the scenario.

Power BI Conditional Column Using DAX:

Discount Level =


    ‘Table'[Units Sold] > 100 && ‘Table'[Discount Percentage] > 0.1, “High Discount”,


        ‘Table'[Units Sold] > 50 && ‘Table'[Discount Percentage] > 0.05, “Medium Discount”,

        “Low Discount”



Power BI Conditional Column Between Values

Explanation: Suppose you have a “Temperature” column, and you want to create a column “Weather Type” based on the temperature range:

  • If Temperature is between 30 and 40 degrees, then “Mild.”
  • If the Temperature is between 20 and 30 degrees, then “Cool.” 
  • Otherwise, “Hot.”

Power BI Conditional Column Using GUI:

1. Open Power BI Desktop, load your dataset, and go to the “Transform Data” option.

3. Choose the relevant table.

4. Under “Home,” click “Add Column” > “Conditional Column.”

5. Use the BETWEEN operator and set the appropriate conditions.

Power BI Conditional Column Using DAX:

Weather Type =


    ‘Table'[Temperature] >= 30 && ‘Table'[Temperature] <= 40, “Mild”,


        ‘Table'[Temperature] >= 20 && ‘Table'[Temperature] < 30, “Cool”,




These examples illustrate how to create Power BI conditional columns based on two columns or between specific values using both the GUI and DAX methods. Choose the method that aligns with your preference and scenario complexity.


Best Practices and Considerations for Power BI Conditional Columns

Here are the best practices and considerations for Power BI conditional columns:

  1. Use Descriptive Names: Choose names that clearly describe the purpose of the conditional column. A well-named column enhances readability and understanding for you and others.
  1. Comment Your Formulas: Add comments within the formula to explain complex logic or reasoning. Comments serve as documentation, making it easier for others (or future you) to understand the formula’s intent.
  1. Consistent Formatting: Adopt a consistent formatting style for your DAX formulas. Consistency improves readability and reduces the chance of errors..

Performance Considerations with Complex DAX Expressions 

Performance considerations with complex DAX expressions are:

  1. Evaluate Formula Complexity: Evaluate the complexity of DAX expressions, especially in large datasets. Highly complex formulas may impact report performance.
  1. Indexing and Filtering: Understand how DAX expressions affect indexing and filtering in Power BI. In some cases, a poorly optimized DAX formula can lead to slower query performance.
  1. Aggregate Functions: Be cautious with the use of certain aggregate functions within DAX expressions, as they might affect performance negatively.

Balancing clarity, efficiency, and performance is crucial when working with conditional columns in Power BI. Following best practices for naming conventions and clarity and recognizing the scenarios where DAX excel will contribute to an effective and well-performing data analysis environment.

Learn Power BI with the Best Microsoft Power BI Training

If you want to learn Power BI and become skilled in data analytics, grab the exclusive opportunity offered by Be10x Microsoft Power BI training and learn a step-by-step framework for business intelligence using AI. 

Why Join the Be10x’s Power BI Workshop?

  • Supercharge Your Data Analyst Career
  • Build Stunning Dashboards
  • Learn Data Visualization & Analytics
  • Become Top 1% Of Power BI Users
  • Get High Paying Job Opportunities
  • No Prior Knowledge Required
  • Learn From IIT Kharagpur Alumni
  • Grow Your Salary Up To 3X
  • Avoid Being Laid Off

The limited-time offer of just Rs. 9 (originally Rs. 1199) includes bonuses worth Rs. 11,500, including productivity hacks, customizable templates, and PowerBI projects for your resume.

Get enrolled in Be10x Microsoft Power BI training today at just Rs.9.