Comprehensive Guide to the SWITCH() Function in DAX

The SWITCH() function in DAX is a powerful tool for simplifying conditional logic. It allows you to evaluate an expression and return specific values based on the outcome of that expression. This makes it incredibly useful when working with data in fields like healthcare, finance, or any area where you need to categorize or handle multiple scenarios in a streamlined way.

In this article, we’ll explore the SWITCH() function in depth, how it works, and provide practical examples using healthcare and financial data to demonstrate its capabilities.

1. Definition:

SWITCH() evaluates an expression against a list of values and returns a corresponding result. It's similar to a CASE statement in SQL or IF-ELSE logic in other programming languages.

2. Syntax:

SWITCH(
    expression, 
    value1, result1, 
    value2, result2, 
    ..., 
    [else_result]
)        

Parameters:

• expression: The expression or value to be tested.
• value1, result1, value2, result2, ...: A list of values and their corresponding results. If the expression matches one of the values, the corresponding result is returned.
• else_result (optional): The result to return if no match is found.

3. Basic Usage:

Example:

Category = SWITCH(
    Sales[ProductCategory],
    "Electronics", "High-Tech",
    "Clothing", "Fashion",
    "Grocery", "Daily Essentials",
    "Other"
)        

Explanation:

• Expression (Sales[ProductCategory]): This evaluates the value in the ProductCategory column.
• Matching Values: If the ProductCategory is "Electronics", it returns "High-Tech"; if it's "Clothing," it returns "Fashion," and so on.
• Default ("Other"): If no match is found, it returns "Other."

4. When to Use SWITCH():

SWITCH() is particularly useful when you have a predefined set of values to evaluate, and you want to avoid complex nested IF statements. It provides cleaner, more readable code and performs better in scenarios where multiple conditions need to be checked.

5. Example 1: Categorizing Financial Risk Levels

Scenario:

You have a table of customers with their credit scores, and you want to categorize customers into risk levels based on their score.

Data:

DAX Code:

RiskCategory = SWITCH(
    TRUE(),
    Customers[CreditScore] >= 750, "Low Risk",
    Customers[CreditScore] >= 650, "Medium Risk",
    Customers[CreditScore] >= 550, "High Risk",
    "Very High Risk"
)        

Explanation:

• Expression (TRUE()): This allows us to evaluate multiple conditions. Each condition compares the credit score to a threshold.
• Matching Values: Customers with a credit score of 750 or above are categorized as "Low Risk", while those with scores between 650 and 749 are "Medium Risk", and so on.
• Default Value: If none of the conditions are met (i.e., if the credit score is less than 550), the result will be "Very High Risk".

Output:

6. Example 2: Healthcare Example – Categorizing BMI Levels

Scenario:

You want to categorize patients based on their Body Mass Index (BMI) values into different weight categories.

Data:

DAX Code:

BMICategory = SWITCH(
    TRUE(),
    Patients[BMI] < 18.5, "Underweight",
    Patients[BMI] >= 18.5 && Patients[BMI] < 24.9, "Normal Weight",
    Patients[BMI] >= 25 && Patients[BMI] < 29.9, "Overweight",
    "Obesity"
)        

Explanation:

• Expression (TRUE()): Used here to evaluate multiple conditions. We check the BMI against different ranges.
• Matching Values: Depending on the BMI, patients are categorized as "Underweight," "Normal Weight," "Overweight," or "Obesity".
• Default Value: If BMI is 30 or above, the result is "Obesity".

Output:

7. Example 3: Financial Example – Loan Interest Rate Based on Credit Score

Scenario:

You want to assign interest rates to customers based on their credit scores.

Data:

DAX Code:

InterestRate = SWITCH(
    TRUE(),
    Customers[CreditScore] >= 750, 3.5,
    Customers[CreditScore] >= 700, 4.0,
    Customers[CreditScore] >= 600, 5.0,
    6.5
)        

Explanation:

• Expression (TRUE()): This allows us to compare the credit score against various thresholds.
• Matching Values: If the credit score is 750 or above, the customer receives a 3.5% interest rate. Customers with scores between 700 and 749 get a 4% rate, and so on.
• Default Value: If the credit score is below 600, the default interest rate is 6.5%.

Output:

8. Example 4: Healthcare Example – Categorizing Age Groups

Scenario:

You have a dataset with patient ages, and you want to classify patients into age groups: "Child," "Adult," and "Senior."

Data:

DAX Code:

AgeGroup = SWITCH(
    TRUE(),
    Patients[Age] < 18, "Child",
    Patients[Age] >= 18 && Patients[Age] < 60, "Adult",
    "Senior"
)        

Explanation:

• Expression (TRUE()): Allows us to evaluate multiple conditions in a clean and efficient manner.
• Matching Values: Patients under 18 are classified as "Child," between 18 and 59 as "Adult," and 60 or above as "Senior."
• Default Value: If none of the conditions are met, it defaults to "Senior."

Output:

9. Key Features of SWITCH():

• Readability: It simplifies conditional logic compared to using nested IF() statements, making the code more readable and maintainable.
• Performance: SWITCH() often performs better than multiple IF() statements because it evaluates the expression once and matches it against possible outcomes.
• Flexibility: Useful for creating categories, assigning values, or applying different calculations based on conditions.

10. Performance Considerations:

SWITCH() is typically more efficient than IF() for handling multiple conditions. However, it is important to structure conditions logically, with the most common cases evaluated first to improve performance.

Summary:

The SWITCH() function in DAX is an excellent tool for handling multiple conditional evaluations in a streamlined and efficient way. Whether you're categorizing data in healthcare, like BMI ranges, or financial risk levels based on credit scores, SWITCH() allows you to write clean, easy-to-understand code without the complexity of nested IF() statements.

By mastering SWITCH(), you can make your DAX calculations more readable, easier to maintain, and more efficient—especially when dealing with complex datasets in finance, healthcare, and beyond.