sql case multiple assignments

SQL: CASE with multiple WHEN conditions

• Quick Example: SQL CASE with multiple WHEN conditions
• How to use CASE with multiple WHEN conditions?

In SQL Server, there are 3 main ways to use CASE with multiple WHEN conditions:

• 1. Use CASE WHEN with multiple conditions

You can use the SQL CASE WHEN statement for multiple conditions by chaining additional WHEN clauses separated by spaces or newlines. Remember to end the statement with the ELSE clause to provide a default value.

Here's an example:

• 2. Use CASE with multiple conditions

If you want to use multiple conditions within a single WHEN clause, you can use the AND, OR, or NOT logical operators to combine these conditions:

• 3. CASE WHEN with a subquery

You can provide a subquery as a part of the WHEN condition:

Keep in mind: Using CASE WHEN with a subquery may impact performance negatively, especially if the subquery is complex or large. It can increase execution time and may cause suboptimal query plans.

• What's the syntax of CASE WHEN?

The CASE WHEN in SQL is a conditional expression used to perform conditional logic in queries. It allows you to perform different actions or calculations based on specific conditions being met.

The basic syntax is:

• CASE : Begins the expression.
• WHEN : Specifies a condition to check.
• condition : The condition to be evaluated, e.g., column_name = 'value'.
• THEN : Indicates the result to be returned if the condition is met.
• result : The value or calculation to return when the condition is true.
• ELSE : Optional, specifies a default result if no conditions are met.
• default_result : The value or calculation to return if no conditions are true.
• END : Ends the CASE expression.

This expression can be used in SELECT, UPDATE, or DELETE statements, as well as in aggregate functions, to apply different calculations or transformations based on specific conditions being met.

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Multiple CASE WHEN in SQL: Full Guide with Examples

Structured Query Language (SQL) is important for managing and manipulating relational databases. At the core of its functionality, the CASE statement stands out as a powerful tool that allows query designers to implement flexible logic, enabling the customization and transformation of data within SQL queries.

The use of conditional logic, particularly through the Multiple CASE WHEN statement, is pivotal in shaping the outcome of database queries. It facilitates the manipulation of data based on specific conditions. In this article, we explore the significance and application of the Multiple CASE WHEN statement in SQL, along with some examples that you might encounter in a technical interview.

How does Multiple Case When SQL Works?

Multiple CASE WHEN statements allow you to implement conditional logic in SQL queries, allowing for the evaluation of multiple conditions and the execution of different actions based on those conditions.

Here is the basic syntax of a Multiple CASE WHEN statement:

This construct proves invaluable in handling scenarios where more than one condition needs consideration.

The above syntax allows the user to select specific columns from a table while creating a new column (new_column) with values calculated based on specified conditions using the CASE WHEN statement. Depending on the fulfillment of conditions, different results are assigned to a new column.

Importance in Handling Multiple Conditions in SQL Queries

In databases, we can face problems if a simple IF-THEN-ELSE statement falls short. This is where we need the Multiple CASE WHEN statement.

Its importance becomes evident when dealing with complex conditions that require precise control over the outcome. It acts as a decision-making tool, allowing you to specify different actions based on various conditions.

• Categorizing Data:

CASE WHEN is used to categorize data into different groups based on specific given conditions, resulting in easier analysis.

• Customizing Output:

CASE WHEN statements can be used to display custom messages or alter the format of the output based on certain conditions, CASE WHEN proves invaluable.

• Data Transformation:

In data transformation tasks, especially when migrating or cleaning data, CASE WHEN helps structure and modify information efficiently.

• Dynamic Sorting:

CASE WHEN can be employed in the ORDER BY clause to dynamically sort query results depending on various conditions.

• Handling NULL Values:

In case of NULL values, the CASE WHEN statement allows the user to define specific actions or replacements, preventing unexpected results.

Additionally, the COALESCE function also helps manage NULL values by providing a default value for the CASE WHEN statement to ensure reliable outcomes in SQL queries.

Example Scenario of Using Multiple CASE WHEN in SQL

Having a good grasp of conditional logic in SQL, especially when it comes to Multiple CASE WHEN statements, is crucial for efficiently manipulating data.

For example, let’s say you’ve been given the task of analyzing a customer database for an e-commerce platform. You need to group customers based on their purchase behavior, loyalty, and engagement with a promotional campaign. How would you go about solving this problem?

Breakdown of the Problem

This involves navigating through multiple data points, including purchase frequency, total spending, response to marketing emails, and the duration of the customer’s relationship with the platform.

Each criterion holds a varying weight in determining the customer’s classification: ‘High Value,’ ‘Medium Value,’ or ‘Low Value.’ This approach requires a level of understanding beyond a simple IF-THEN structure.

Step-by-Step Solution using Multiple CASE WHEN

Identify Criteria and Weights

• Purchase Frequency (PF) : High PF contributes more to ‘High Value.’
• Total Spending (TS) : Higher spending results in a ‘High Value’ classification.
• Email Engagement (EE) : Those who engage more with emails lean towards ‘High Value.’
• Customer Tenure (CT) : Longer tenure is considered in ‘High Value

This query uses a CASE WHEN statement to categorize each customer as ‘High Value’, ‘Medium Value’, or ‘Low Value’ based on their purchase frequency and total spending, labeling the result as customer_classification .

Possible Variations or Edge Cases

As with any real-world scenario, there are variations and edge cases to consider:

• Handling NULL Values : Account for missing data in any of the criteria.
• Dynamic Adjustments : Consider periodic adjustments to criteria based on business strategy.
• A/B Testing : Incorporate variations for experimenting with different classification strategies.
• Scalability : Optimize the query for large datasets to ensure efficient execution.

This example showcases how Multiple CASE WHEN statements provide a robust solution to intricate data categorization challenges. The flexibility and precision afforded by this construct are invaluable in crafting sophisticated SQL queries for various business requirements.

Additional Multiple CASE WHEN SQL Scenarios

1. common use cases for multiple case when.

Multiple CASE WHEN statements shine in various situations where complex conditions dictate data manipulation. Here are common scenarios where it can be used:

• Categorization: Assigning categories based on multiple conditions.
• Dynamic Sorting: Adjusting the sort order dynamically.
• Customized Output: Tailoring output messages or formats based on conditions.

Example Code:

Potential Pitfalls:

Overcomplicating Simple Scenarios with Unnecessary CASE WHEN Statements: Using multiple CASE WHEN statements in situations that require simpler logic can unnecessarily complicate queries, making them harder to understand, maintain, and optimize, and can increase the likelihood of errors.

Forgetting to account for all possible conditions: In complex CASE WHEN constructs, there’s a risk of overlooking certain conditions or outcomes, which can lead to incomplete or incorrect results, especially in scenarios with many potential data variations.

2. Performance Considerations

While Multiple CASE WHEN statements offer flexibility, their impact on query performance should be considered. Here’s what to keep in mind:

• Resource Usage: Each condition adds computational overhead.
• Index Utilization: Complex conditions may limit the use of indexes.
• Query Optimization: Regularly review and optimize queries for efficiency.

Unoptimized Queries Leading to Slower Performance: When queries are not well-optimized, especially with multiple CASE WHEN statements, they can become inefficient in processing data. This inefficiency mainly arises because each CASE WHEN adds extra conditions for the database to evaluate, increasing the computational workload.

Excessive Use of CASE WHEN Impacting Readability: While CASE WHEN statements provide flexibility in handling multiple conditional logic scenarios, overusing them can lead to convoluted and hard-to-read SQL code.

3. Handling NULL Values

Dealing with NULL values is a very common challenge in databases. Multiple CASE WHEN statements provide a structured approach to handle these scenarios:

• Coalesce Function: Use COALESCE to handle NULL values effectively.
• Default Values: Provide default values for NULL scenarios.
• Conditional Actions: Tailor actions based on NULL or non- NULL conditions.

Forgetting to consider NULL scenarios in each condition: In complex queries, it’s easy to overlook NULL scenarios in CASE WHEN conditions. This omission can lead to inaccurate query results, as NULL values might not be handled as intended. Ensuring each condition accounts for possible NULL values is crucial for data accuracy and integrity.

Overlooking the impact of NULL handling on query performance: Handling of NULL values, especially in large datasets, can impact query performance. Using functions like COALESCE or incorporating NULL checks in CASE WHEN statements adds computational overhead. If not managed properly, this can lead to slower query execution, necessitating careful optimization to maintain performance.

4. Nesting Multiple CASE WHEN Statements

Nesting Multiple CASE WHEN statements allows for intricate conditional logic. It’s useful when conditions depend on the outcome of previous conditions:

• Hierarchical Conditions: Conditions based on the result of prior conditions.
• Sequential Logic: Executing conditions in a specific order.
• Complex Scenarios: Addressing scenarios with layered conditions.

Let’s take a look at a sample code:

Complex nested structures may reduce query readability: Nesting CASE WHEN statements can lead to highly intricate and complex SQL queries. This complexity can significantly reduce the readability of the code, making it difficult for others (or even the original author at a later time) to understand the logic. The more nested the structure, the harder it becomes to trace through each level of logic, increasing the risk of misinterpretation or errors.

Ensure proper indentation and formatting for clarity: With nested CASE WHEN statements, maintaining proper indentation and formatting becomes crucial for clarity. Poor formatting can make an already complex structure even more challenging to navigate and understand. Clear formatting helps in distinguishing different levels of logic and makes the query more maintainable.

5. Tips for Optimizing Queries using Multiple CASE WHEN

Optimizing queries involving Multiple CASE WHEN statements is crucial for efficient database operations. Consider the following tips:

• Indexing: Utilize indexes on columns involved in conditions.
• Simplify Logic: Streamline logic for readability and performance.
• Regular Review: Periodically review and optimize queries for changing data patterns.

Multiple CASE WHEN SQL Interview Questions

1. in what situations would you choose to use case when over other conditional constructs like if or coalesce.

Answer: CASE WHEN is useful when dealing with multiple conditions and categorizing the data. When dealing with NULLs, It provides a cleaner and more readable solution compared to nested IF statements or COALESCE.

2. What is the significance of the ELSE clause in a CASE WHEN statement?

Answer: If none of the preceding conditions is true, the ELSE clause provides a default result.

3. Provide a real-world scenario where using Multiple CASE WHEN statements would be beneficial.

Answer: In retail cases, we can use multiple CASE WHEN statements to categorize products based on rating, sales, and profit.

4. Explain the concept of nesting in SQL. How and when would you use nested CASE WHEN statements?

Answer: Nesting involves placing one CASE WHEN statement inside another. This can be used when conditions depend on the outcome of prior conditions, creating a hierarchy of logic.

5. Provide an example where nesting CASE WHEN statements are necessary for a more complex condition.

Answer: In a grading system, you might nest CASE WHEN statements to categorize students as ‘Excellent,’ ‘Good,’ ‘Satisfactory,’ or ‘Needs Improvement’ based on both grade and participation.

6. How does the CASE WHEN statement handle NULL values in conditions?

Answer: CASE WHEN handles NULL values by evaluating conditions as false when dealing with NULL. COALESCE function is used to handle NULL values explicitly.

7. Discuss potential performance considerations when using Multiple CASE WHEN statements.

Answer: Multiple conditions may impact query performance. Indexing columns involved in conditions and simplifying logic can optimize performance.

8. How would you optimize a query involving multiple nested CASE WHEN statements for better performance?

Answer: Regularly review and optimize the query, ensure proper indexing, and simplify complex logic for improved performance.

9. Imagine a scenario where the classification criteria for products based on sales need to be adjusted dynamically. How would you implement this using CASE WHEN ?

Answer: By introducing variables or parameters in the CASE WHEN conditions, allowing for dynamic adjustments based on changing business requirements.

10. Consider a situation where some data points are missing ( NULL values). How would you handle this when using Multiple CASE WHEN statements?

Answer: I would use the COALESCE function to handle NULL values and ensure that the conditions are explicitly defined for such scenarios.

11. Discuss potential pitfalls or challenges when working with complex conditions in a CASE WHEN statement.

Answer: Pitfalls include overcomplicating queries, overlooking specific conditions, and potentially impacting query readability. Careful consideration is needed to balance complexity and clarity.

12. Compare and contrast the CASE WHEN statement with the IF statement in SQL.

Answer: Unlike the IF statement, CASE WHEN is SQL’s standard conditional construct and provides a more readable and flexible solution for handling multiple conditions.

13. In what scenarios would you prefer using a CASE WHEN statement over using a JOIN clause?

Answer: While a JOIN clause is used to combine data from multiple tables, CASE WHEN is used for conditional logic within a single table. I would use CASE WHEN for categorization and JOIN for combining related data.

For an end-to-end overview of SQL, including complex joins, advanced reporting, and creating multi-table databases, explore our specialized learning path dedicated to SQL.

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Data to Fish

Case Statement using SQL (examples included)

Here are 3 different ways to apply a case statement using SQL:

(1) For a single condition:

(2) For multiple conditions using AND:

(3) For multiple conditions and results:

Let’s review few examples with the steps to apply case statements using SQL.

Steps to Apply Case Statements using SQL

Step 1: create a table.

If you haven’t already done so, create a table in your database.

For example, let’s create a table called ‘ people ‘ that includes the following data and fields:

Step 2: Define the Rules

Next, define the rules for the case statement.

For example, let’s define the following rules:

• When the person’s age is equal or above 60, then the person is eligible for a ‘senior discount’
• Else , there should be ‘no discount’

Step 3: Apply the Case Statement using SQL

Finally, you can use the following template for a single condition:

For our example:

• condition_1: age >= 60
• result_1: ‘senior discount’
• result_2: ‘no discount’
• new_field_name: discount

So the complete syntax would look like this:

Run the query, and you’ll get the following result:

Additional Examples of Case Statements

Example 1: multiple conditions using and.

Let’s now review an example with multiple conditions, where the rules are:

• When the person’s age is equal or above 60, and the person is a member, then the person is eligible for a ‘membership gift’
• Else , there should be ‘no membership gift’

You can use this template for multiple conditions using AND:

• condition_1 AND condition_2: age >= 60 AND member = ‘Yes’
• result_1: ‘membership gift’
• result_2: ‘no membership gift’
• new_field_name: gift

So the complete syntax, for our example, would look like this:

Example 2: Multiple Conditions and Results

For the final section of this guide, let’s review an example with multiple condition and results, where the rules are:

• When the person’s age is equal or above 18, and less than 60, then there should be ‘no discount’
• Else  the person is eligible for a ‘junior discount’

You can then use the following template for multiple conditions and results:

And here is the complete syntax for our example:

Run the query, and you’ll get:

You may also want to check the following page for additional SQL tutorials .

Home » SQL Tutorial » SQL CASE

Summary : in this tutorial, you will learn how to use the SQL CASE expression to add the logic to the SQL statements.

Introduction to SQL CASE expression

The SQL CASE expression allows you to evaluate a list of conditions and returns one of the possible results. The CASE expression has two formats: simple CASE and searched CASE.

You can use the CASE expression in a clause or statement that allows a valid expression. For example, you can use the CASE expression in statements such as SELECT , DELETE , and UPDATE or in clauses such as SELECT , ORDER BY , and HAVING .

Simple CASE expression

The following illustrates the simple CASE expression:

The CASE expression compares an expression to a set of expression (when_expression_1, when_expression_2, when_expression_3, …) using the equality operator (=). If you want to use other comparison operators such as greater than (>), less than (<), etc., you use the searched CASE expression.

The CASE statement returns the result_1, result_2, or result_3 if the expression matches the corresponding expression in the WHEN clause.

If the expression does not match any expression in the WHEN clause, it returns the esle_result in the ELSE clause. The ELSE clause is optional.

If you omit the ELSE clause and the expression does not match any expression in the WHEN clause, the CASE expression returns NULL.

Simple CASE expression example

Let’s take a look at the employees table.

Suppose the current year is 2000.

We can use the simple CASE expression to get the work anniversaries of employees by using the following statement:

The YEAR function returns the year when the employee joined the company. We get the number of years that the employee has been with the company and by subtracting the year when the employee joined the company from the current year (2000).

We get the number of years that the employee has been with the company by subtracting the year when the employee joined the company from the current year (2000).

Then we compare the result with 1, 3, 5, 10, 15, 20, 25, 30 If the year of service equals one of these numbers, the CASE expression returns the work anniversary of the employee.

If the year of services of the employee does not match these numbers, the CASE expression returns NULL.

Searched CASE expression

The following shows the searched CASE expression.

The database system evaluates the boolean expression for each WHEN clause in the order specified in the CASE expression.

If the Boolean expression in each WHEN clause evaluates to true, the searched CASE statement returns the result in the corresponding THEN clause.

If no Boolean expression returns true, the CASE expression return the result else_result in the ELSE clause.

Like the simple CASE expression, the END clause is optional. If you omit the ELSE clause and no Boolean expression evaluates to true, the CASE expression returns a NULL value.

Search CASE expression example

The following illustrates the searched CASE expression example.

If the salary is less than 3000, the CASE expression returns “Low”. If the salary is between 3000 and 5000, it returns “average”. When the salary is greater than 5000, the CASE expression returns “High”.

In this tutorial, we have introduced you to the SQL CASE statement that allows you to add the IF THEN ELSE logic to the SQL statements.

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How to Use CASE statement in SQL: Explained with Examples

By Cristian G. Guasch •  Updated: 06/28/23 • 19 min read

When working with SQL, one might often need to run complex queries that involve multiple conditional statements. This is where the SQL CASE expression comes into play. Essentially a versatile and powerful tool, the CASE expression enables users to perform conditional logic within SQL queries, making it tremendously helpful for dealing with diverse data manipulation scenarios.

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The SQL CASE expression operates similar to a switch statement found in programming languages like C# or Java, allowing users to execute specific actions depending on predefined conditions. By utilizing this feature, users can gain improved control over how data is presented, analyzed, and manipulated within SQL, ultimately enhancing the overall efficiency and readability of database queries.

Through mastering the use of SQL CASE expressions, users can effectively streamline their database operations, optimize query performance, and achieve more accurate results. Whether it’s conditional aggregation or creating dynamic pivot tables, the power of CASE expressions is undeniable when it comes to handling complex data manipulation tasks.

Understanding SQL Case Statements

SQL case statements are a versatile and powerful tool in the database programmer’s arsenal. They provide a method for performing conditional expressions within SQL queries and stored procedures. With case statements, users can evaluate conditions and return a value or perform different actions based on the result of the evaluation.

A common use of case statements is to return a specific value depending on a column’s value in the result set. Consider the following scenarios:

• Display user status based on their account age.
• Find customers’ geographic regions based on their address data.
• Calculate discounts or sales taxes based on product categories or pricing tiers.

These scenarios require conditional logic, which can be easily implemented using case statements. The two main variants of SQL case statements are the simple case and the searched case . Let’s explore each of these in more detail.

Simple Case Statements

A simple case statement evaluates a single expression against multiple conditions and returns a matching value. Here’s the general syntax for a simple case statement:

With this syntax, if the expression matches value1 , the result would be result1 . If expression matches value2 , the result would be result2 , and so on. If there’s no match, the default_result is returned.

Searched Case Statements

Searched case statements, on the other hand, evaluate multiple Boolean conditions and return a matching result. Here’s the general syntax for a searched case statement:

With this syntax, if condition1 is true, the result would be result1 . If condition2 is true, the result would be result2 , and so on. If there’s no condition met, the default_result is returned.

To sum up, understanding and utilizing SQL case statements are crucial for effectively handling conditional logic within SQL queries. With their ability to evaluate single expressions and multiple conditions, simple case and searched case statements offer flexibility and power for various scenarios. By mastering these concepts, users can streamline their SQL code and optimize their database-related tasks.

Types of Case Expressions

Diving into the world of SQL case expressions, one discovers that there are two main types: the SIMPLE case expression and the SEARCHED case expression . Each has its unique function and application in SQL queries, but both play a crucial role in transforming and manipulating data.

SIMPLE Case Expression

The SIMPLE case expression can be thought of as a more sophisticated version of the traditional IF-THEN-ELSE logic. When working with data in SQL, it’s often necessary to evaluate multiple conditions and return a value based on the first condition that is met. The SIMPLE case expression does precisely that. Here’s the general syntax for a SIMPLE case expression:

Some key features of the SIMPLE case expression include:

• Comparing an input_expression to a specified set of expressions (expression1, expression2, etc.).
• Returning the corresponding result (result1, result2, etc.) when a match is found.
• Using an ELSE clause for situations where no match occurs, providing a default value (result_n).
• Ensuring a clean, readable syntax that improves code clarity and reduces the likelihood of errors.

SEARCHED Case Expression

When more complex conditions are needed or when multiple columns must be evaluated, the SEARCHED case expression comes into play. It offers greater flexibility than the SIMPLE case expression by allowing for more intricate boolean expressions. The general syntax for a SEARCHED case expression is as follows:

A few highlights of the SEARCHED case expression include:

• Evaluating a set of conditions (condition1, condition2, etc.) that can involve multiple columns and boolean operators.
• Returning the corresponding result (result1, result2, etc.) when a condition evaluates to true.
• Using an ELSE clause to provide a default value (result_n) when no conditions are met.
• Adding versatility to SQL case statements by handling more intricate scenarios and multi-column evaluations.

To sum up, both SIMPLE and SEARCHED case expressions offer powerful tools in manipulating and transforming data when using SQL case . The SIMPLE case is ideal for situations where an input expression needs to be compared against a list of values, while the SEARCHED case provides increased flexibility for complex conditions and multi-column evaluations. By mastering these case expressions, one can unlock new possibilities in data retrieval and transformation.

Simple Case Syntax and Examples

When working with SQL case statements, it’s essential to understand the basic syntax and how to construct simple examples. Case statements in SQL are versatile tools used to perform conditional logic on data, offering a clean and easy-to-read way to work with data. In this section, several examples of simple case syntax will be demonstrated, assisting in building a foundational understanding of how to leverage this powerful SQL construct.

Simple case syntax can be broken down into the following key components:

• The CASE keyword: this initiates the case expression.
• Column or expression: this is the data upon which the case statement operates.
• The WHEN keyword: defines a condition to test against the column or expression.
• The THEN keyword: specifies the result to return if the condition is met.
• The ELSE keyword (optional): provides a default result if no conditions are met, is optional but good practice to include.
• The END keyword: completes the case expression.

Here’s a simple example using the CASE statement. Imagine a table named Employee with columns Age , Name , and EmployeeStatus . To create an analysis that determines employee categories based on age, the SQL code might look like this:

In this example, the CASE statement is used to assign an employee to one of three age categories: Young , Adult , or Senior . The statement evaluates each row’s Age column value and, depending on the range it falls into, assigns the appropriate category.

Another example involves a table named Orders with columns OrderID , Quantity , and TotalPrice . To apply a discount based on the quantity purchased, use this SQL code:

In this case, order quantities of 10 or more receive a 10% discount on the TotalPrice . By utilizing the SQL case statement, this discount is efficiently applied to the relevant rows.

In summary, the SQL case statement enables users to flexibly handle conditional logic when querying data in a simple, readable format. Understanding the basic syntax and working with examples helps build a strong foundation in effectively implementing case statements within SQL projects.

Searched Case Syntax and Examples

Utilizing SQL case expressions allows users to perform conditional operations within their SQL queries. The Searched Case expression, in particular, is a versatile tool that arises when a simple case expression isn’t adequate. This section delves into the Searched Case syntax and showcases several practical examples to help users utilize this functionality.

The general syntax of a Searched Case expression is as follows:

Users must keep in mind that conditions are evaluated sequentially – the system stops once it encounters the first condition that evaluates to true. If none of the conditions are met, the result specified in the ELSE clause is returned. The ELSE clause, however, is optional – when omitted, the expression returns NULL if no conditions are met.

Here’s a sample query utilizing Searched Case with the ELSE clause:

In this example, the query retrieves products and categorizes their PriceRange as ‘Cheap’, ‘Moderate’, or ‘Expensive’. The Searched Case evaluates each product’s price and assigns the respective category to it.

Searched Case expressions can also be nested, allowing users to implement more complex conditional logic. Here’s an example of nested Searched Case:

This query retrieves customers and categorizes them by the Continent they reside in, using a nested Searched Case expression which includes sets of countries to map to specific continents.

In summary, Searched Case expressions provide a powerful way to incorporate conditional logic into SQL queries by evaluating conditions sequentially and assigning results accordingly. Many developers find them particularly valuable when simple case expressions are insufficient. With practical examples and a keen understanding of the syntax, users can easily apply Searched Case expressions to make their queries more dynamic and adapt to changing conditions.

Nesting Case Expressions

When working with SQL case statements, sometimes nesting case expressions becomes necessary for more complex query conditions. In this section, we’ll discuss how to nest SQL case expressions properly and explore some examples to help visualize how they work in practice.

When nesting case expressions, it’s essential to remember that additional case statements should be placed inside the existing case statement to achieve the desired outcome. Each inner case expression needs its own WHEN and THEN clauses, and every nested case must end with an END keyword.

Let’s take a look at an example of using nested case expressions in SQL:

In this example, we’re categorizing customers into three age groups – Underage , Adult , and Senior . The outer case expression checks if a customer is underage, while the inner case expression handles categorizing adult and senior customers.

Using nested case expressions can also help to handle NULL values. For instance:

In this example, the outer case expression deals with null prices, and the inner one assigns price ranges for the products with known prices.

Here are some general tips for using nested case expressions:

• Keep nested case expressions as simple as possible.
• Make sure every case has its own WHEN , THEN , and END keywords.
• Use nested case expressions to handle NULL values appropriately.
• Test your nested case statements thoroughly to avoid logical errors in your SQL query.

In summary, mastering nested case expressions in SQL opens up new possibilities for handling complex query conditions and improving the accuracy of your data retrieval. Keep practicing with various scenarios and become proficient in using nested case statements in your day-to-day work with SQL. Just remember to maintain simplicity and thoroughly test your nested case expressions.

Using Case with Aggregate Functions

Using case with aggregate functions in SQL can be a powerful way to manipulate and analyze data. By combining case statements with aggregate functions, one can derive valuable insights from their database. This section will cover the basics of using the SQL case expression with aggregate functions like COUNT, SUM, AVG, MIN, and MAX.

To implement the SQL case statement within an aggregate function, one has to include the case expression within the function call itself. The following example demonstrates how to do this:

Here is a breakdown of using some commonly used aggregate functions with case expressions:

• COUNT : This function can be used to count the number of rows that match specific conditions using case: SELECT COUNT(CASE WHEN column_name = 'value1' THEN 1 END) AS count_of_value1, COUNT(CASE WHEN column_name = 'value2' THEN 1 END) AS count_of_value2 FROM table_name;
• SUM : The sum function calculates the total of a numeric column based on certain conditions. An example use of case with SUM is: SELECT SUM(CASE WHEN column_name = 'value1' THEN amount_column ELSE 0 END) AS sum_of_value1_amount, SUM(CASE WHEN column_name = 'value2' THEN amount_column ELSE 0 END) AS sum_of_value2_amount FROM table_name;
• AVG : To calculate the average of a numeric column subject to specific conditions, the SQL case expression can be employed: SELECT AVG(CASE WHEN column_name = 'value1' THEN numeric_column END) AS avg_for_value1, AVG(CASE WHEN column_name = 'value2' THEN numeric_column END) AS avg_for_value2 FROM table_name;
• MIN and MAX : The minimum and maximum values in a column can also be determined using case: SELECT MIN(CASE WHEN column_name = 'value1' THEN numeric_column END) AS min_for_value1, MAX(CASE WHEN column_name = 'value1' THEN numeric_column END) AS max_for_value1 FROM table_name;

In summary, the versatility of the SQL case expression with aggregate functions allows for more efficient data analysis and manipulation. By understanding the basics of this technique, users can effectively leverage their databases to gain valuable insights.

Case in SQL Order By Clause

Using CASE in SQL is a powerful technique to manipulate and control data dynamically. It’s especially useful when working with the ORDER BY clause, allowing for greater flexibility in the sorting of results. Let’s dive into how to use the SQL CASE statement in the ORDER BY clause.

To start, the SQL CASE expression lets users construct conditional statements that allow the result set to change based on specified conditions. For example, the structure of a simple CASE expression is as follows:

To put this into practice within the ORDER BY clause, consider the following example. A user wants to sort a list of products by their categories: Electronics should come first, followed by Clothing , but all other categories can be sorted alphabetically. Here’s how this can be achieved using CASE in the ORDER BY clause:

The CASE expression assigns different numeric values to the categories we want to sort, and then orders them accordingly. After sorting by the numeric values, the query sorts alphabetically for the other categories.

Another application of CASE in the ORDER BY clause comes in handy when sorting results based on multiple columns with varying sorting criteria. For instance, a user may want to sort employee data by salary, but in the event of a tie, sort by the employees’ hire dates. Here’s an example of how to accomplish this:

In this example, employees with the lowest salary will be sorted in descending order by hire date, while all other employees will be sorted in ascending order by hire date.

In conclusion, using CASE expressions within the ORDER BY clause can provide valuable enhancements to SQL queries. By incorporating conditional logic, users can dynamically adjust the sorting order of data, ensuring that important data can be prioritized and displayed according to specific needs.

Handling NULL Values with Case

When working with SQL case statements, handling NULL values is essential for producing accurate results. Since NULL values indicate missing or unknown information, an SQL query must account for them to provide meaningful data. This section explains various techniques to handle NULL values with case statements and prevent any potential issues.

The first technique involves using COALESCE or NULLIF functions in conjunction with case statements. These functions allow developers to replace NULL values with default values or transform non-NULL values into NULLs. Here’s an example of using COALESCE:

In this example, if salary_bonus is NULL, it’s replaced by 0 to ensure an accurate calculation of the bonus.

Another approach is using CASE…WHEN…THEN…ELSE…END in SQL queries to manage NULL values directly. Based on certain conditions, this structure processes NULL values and returns matching results. For instance:

This query checks if the salary_bonus column contains NULL values and, if so, returns 0 as the bonus value.

Sometimes, it may be necessary to include or exclude NULL values from query results depending on specific requirements. With case statements, this becomes easy:

• Including NULL values : To ensure a query includes NULL values when calculating results, use an appropriate condition: SELECT employee_id, position FROM employees WHERE salary_level >= 1000 OR salary_level IS NULL In this example, the query returns all employees with a salary_level equal to or greater than 1000, or where the salary_level is NULL.
• Excluding NULL values : To exclude NULL values from query results, add an additional condition: SELECT employee_id, position FROM employees WHERE salary_level >= 1000 AND salary_level IS NOT NULL Here, the query only retrieves employees with a salary_level equal to or greater than 1000, completely ignoring records with NULL values in the salary_level .

By incorporating these techniques for handling NULL values with SQL case , developers improve the accuracy and efficiency of their queries while maintaining the integrity of their data.

Common Mistakes to Avoid

When working with SQL CASE statements, it’s crucial to avoid some common errors that can impact query performance or yield incorrect results. By recognizing these pitfalls, developers can write more accurate, efficient, and streamlined SQL queries.

One typical mistake is over-nesting the case statements. Excessive nesting can make queries difficult to read, maintain, and debug, while also increasing the risk of performance issues. Although SQL allows for a high level of nesting, it’s best to keep the code organized, streamlined, and use other optimization techniques to maintain performance.

Another common issue is forgetting to use the ELSE clause in a case statement. While it’s not always required, leaving out the ELSE clause can lead to unexpected NULL values in the query results. By including a default outcome with the ELSE clause, developers can avoid potential confusion and ensure a more reliable output.

• E.g., instead of writing: CASE WHEN a > b THEN 'greater' END
• It’s recommended to add an ELSE clause: CASE WHEN a > b THEN 'greater' ELSE 'not greater' END

Misusing the Searched CASE form is another common error. The searched CASE operates in a slightly different manner than the simple CASE, checking each search condition in the WHEN clause sequentially and returning the corresponding result for the first true condition. Mixing up these two forms can lead to invalid expressions or incorrect results.

Moreover, watch out for data type inconsistencies when working with CASE statements. SQL usually attempts to convert differing data types to the one with the highest precedence automatically, but this conversion can cause errors or lead to unexpected output. Ensure the data types matched, or explicitly cast them to the desired type for consistency.

Lastly, remember that the SQL CASE statement in some database management systems (DBMS) is case insensitive . Though this may not cause issues most of the time, there could be rare instances where case insensitivity affects query results. Being aware of this factor can help in maintaining uniformity across various DBMS.

In conclusion, by staying vigilant and avoiding these common SQL case mistakes, developers can create queries that are more reliable, performant, and easily maintainable.

Understanding how to use SQL CASE is essential for any database professional. It offers flexibility in querying and processing data, allowing for a more refined and tailored approach to managing information. The power of CASE lies in its ability to implement conditional logic directly into SQL queries, making it an incredibly versatile tool for a wide range of applications.

Efficiency is also a key benefit when utilizing SQL CASE. With its ability to handle multiple conditions in a single statement, one can eliminate the need for multiple separate queries. This streamlined approach can lead to significant time savings, especially in complex database environments.

It’s important to note that various databases may have differing syntax for SQL CASE. However, the core functionality remains consistent across platforms. Whether it’s in an Oracle, MySQL, or SQL Server database, the overall structure will involve:

• Evaluating a series of conditions with the CASE statement
• Specifying the corresponding actions to be taken with the WHEN and THEN clauses
• Setting a default action in case no conditions are met, using the ELSE clause
• Finalizing the statement with the END keyword

An example of a SQL CASE expression:

To sum up, mastering the use of SQL CASE can enhance one’s database management skills and greatly improve efficiency when working with data. With this knowledge, professionals can create more powerful and versatile SQL queries, allowing them to handle condition-based scenarios and complex data-processing tasks with confidence.

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SQL CASE WHEN Explained: 10 Easy Examples for Beginners

The CASE WHEN statement lets us make decisions on our data, categorizing and manipulating records based on specified conditions. Find out how to use CASE WHEN in this article.

Imagine you're deciding what to wear for the day. You take out your umbrella if it's raining; if not, you leave it at home. This decision-making procedure is essentially the same as a SQL CASE WHEN statement.

In the realm of SQL, the CASE WHEN statement functions much like an if-then-else expression, allowing us to create custom classifications within a query. Other programming languages use similar logic – e.g. Python uses if , elif , and else , and JavaScript uses the switch statement. This concept's widespread use across computer languages emphasizes how crucial it is: it gives users the capacity to handle a variety of situations. In SQL, this construct is a vital tool for data analysis.

In this article, you'll find real-world practical exercises using the CASE WHEN statement for data analysis. This statement enables analysts to craft customized logic for classification and decision-making in their queries. As a result, the query’s accuracy and the analysis’ depth is enhanced.

If this sounds interesting, why not explore our Creating Basic SQL Reports course? Along with covering details of the CASE WHEN syntax, this course also teaches you how to use SQL aggregation functions such as COUNT() and SUM() . You'll quickly learn how to compute averages, compare business groupings, and arrange intricate queries.

Now, let's dive into the intricacies of SQL CASE WHEN and demystify the statement through some straightforward examples!

Understanding CASE WHEN Syntax

To explore the complexities of the CASE WHEN statement, let's break down its syntax using a few examples.

Basic Syntax: CASE WHEN THEN

To begin, we will examine the simplest syntax of the SQL CASE WHEN statement. This construct is especially helpful for segmenting records according to a given criteria and generating a new column to show the outcomes. Here’s the syntax:

Let's explain each part in detail:

• SELECT : Specifies the columns to be included in the result set.
• CASE : Evaluates the specified condition for each row in the dataset. This Initiates the conditional logic.
• WHEN condition THEN result : Defines the condition to be checked and the result to be assigned if the condition is met. This allows for the dynamic classification of data.
• END : Marks the end of the CASE It signifies that the evaluation of conditions and assignment of results are complete.
• AS new_column : Creates a new column named new_column in the result set. This column captures the outcomes of the CASE WHEN evaluation for each row.

This approach is particularly valuable when you want to introduce a categorical dimension to your data based on specific conditions. In the query below, we use CASE WHEN to label cities with temperatures exceeding 30 degrees Celsius as High :

This simplifies temperature pattern analysis with clear threshold labels in the new column, contributing to a more expressive and informative dataset for further analysis.

Note that in this statement, records that don’t meet the specified condition will have a NULL value in the new column. This leads us to our next statement.

CASE WHEN THEN ELSE

Without an ELSE clause, we run the risk of our new column containing NULL values. By incorporating an ELSE clause, this construct provides a fallback result when the condition is not satisfied. This is useful when you want to ensure that every record in the dataset receives a meaningful value in the new column.

Here’s the syntax:

Let's break down the conditions:

• WHEN condition THEN result : Defines the primary condition to be checked and the corresponding result to be assigned if the condition is met.
• ELSE alternative_result : Specifies an alternative result to be assigned when the primary condition is not This ensures that the new column will not contain NULL values.

Let’s return to the weather data example. By introducing an ELSE clause, temperatures below the specified threshold will now be labeled as Normal . This construction works well to ensure every record in the new column has a defined value – which improves data analysis accuracy. This is the new query:

As you can see, all temperatures under 30 degrees Celsius are classed as normal; everything over 30 is high.

Multiple THENs in CASE WHEN

What happens if we have more than one condition we want to apply to our data? The following example shows how to use the CASE WHEN statement's syntax with multiple conditions. This offers a method for classifying data according to different standards:

• WHEN condition1 THEN result1 : Defines the first condition to be checked and the corresponding result if this condition is met.
• WHEN condition2 THEN result2 : Specifies a second condition and its associated result.
• Additional WHEN clauses can be added for further conditions.

In a CASE statement with multiple WHEN clauses, the order is significant . The conditions are evaluated sequentially, and the first condition that is met determines the result. Once a condition is satisfied, the corresponding result is returned and the subsequent WHEN clauses are skipped. In this example, if condition1 is true for a particular row, result1 will be returned, and condition2 will not be evaluated for that row.

In the context of our previous weather data example, the final query incorporates multiple clauses as well as an ELSE clause to ensure that any records not meeting the specified temperature conditions receive a non-NULL output. This structure ensures that every record in the temperature_category column will be assigned a meaningful label; this contributes to a more comprehensive and informative weather analysis. Here’s the query:

As you can see, we now have three weather categories: High for temperatures over 30 degrees, Moderate for temps between 20 and 30, and Low for all other temps – i.e. those under 20 degrees Celsius.

Now let’s apply these concepts to some real-world data analysis problems.

Examples of Using CASE WHEN in Data Analysis

Example 1: categorizing data.

Understanding transaction data is important for evaluating customer purchasing behavior in the context of a retail business.

Let’s imagine you have a sales transaction dataset. It could be difficult to quickly obtain insights into the distribution of transactions and to analyze raw transaction amounts. One way to handle this situation is to group transactions according to amount. Here’s a query that lets us group transactions into High , Medium , and Low categories:

Using the CASE expression, we can categorize transactions into meaningful groups, simplifying the analysis and allowing for a quick overview of transaction patterns. A transaction with an amount of 980 will be classified as Low , while a transaction with an amount of 5,200 will be categorized as High . This dynamic categorization simplifies the analysis and provides a quick overview of transaction patterns.

Example 2: Handling NULL Values

Tracking order dates is essential for an e-commerce platform; it helps us comprehend customer behavior and develop better order fulfillment schedules.

Let's say you have a dataset in which the OrderDate column contains a large number of NULL values. When examining order-related data, missing values in the OrderDate column may cause misunderstandings or confusion. So let’s write a query that places orders into two groups: those with an order date ( Order Placed ) and those with No Order Date .

Here the CASE expression distinguishes between records with and without order dates, providing clear labels and enhancing the accuracy of data analysis.  A row with a NULL OrderDate will be labeled as No Order Date , indicating that no specific order date is recorded for that transaction. In contrast, a row with a specific OrderDate , such as 2023-10-12 , will be labeled as Order Placed , indicating that an order has been placed and has a specific order date.

Example 3: Creating Aggregated Columns

To optimize production and inventory management, a manufacturing company may seek to determine the level of demand for its products. The different levels of demand for different products may not be immediately apparent from looking at the total quantity sold. Therefore, additional columns can be created in the result set by aggregating data based on specific conditions or criteria.

Let’s analyze a sample query:

Here, the CASE expression manages the dynamic categorization of demand levels, allowing the company to identify high-demand products and adjust production plans accordingly. A row with a TotalQuantity of 120 will be labeled as High Demand , signifying a robust demand for that specific product. Conversely, a row with TotalQuantity of 80 will be labeled as Normal Demand , indicating a standard demand level for that particular product.

Example 4: Marketing Analysis

Imagine you are an online retailer who wishes to plan marketing campaigns and inventory stocking by analyzing the seasonality of customer orders. It is challenging to spot peak seasons or quarterly trends when viewing orders without classifying data by time; this is why we analyze data based on date ranges and offer insights into temporal patterns.

Using the CASE expression, we organize orders into quarters. A row with an OrderDate of 2023-02-15 will be labeled as Q1 , signifying that the order falls within the first quarter of the year. An order placed on 2023-05-20 will be labeled as Q2 , indicating its placement within the second quarter. This enables you to strategize marketing efforts and adjust inventory levels based on seasonal demand.

Example 5: Customer Segmentation

A service that charges a subscription seeks to customize its products and marketing tactics according to its users' purchasing habits. When preparing certain reports, an analyst will need to group customers based on various criteria; in the example below, we segment customers into groups by their spending habits:

The CASE expression segments customers into categories, allowing the service provider to offer personalized promotions, discounts, or services based on the spending level of each customer segment.

Example 6: Categorizing Products by Price Range

Exercise: Understanding the distribution of product prices at an e-commerce site is essential for making informed pricing and marketing decisions. With so many products available, it can be difficult to obtain insights. You have been asked to simplify the data so your boss can understand the pricing environment and make better decisions.

The output of this exercise should contain the ProductID , ProductName , Price , and PriceCategory for each product. The PriceCategory column should categorize the products as follows:

• Low Price : Assigned to products with prices under 50.
• Moderate Price : Assigned to products with prices between 50 and 100.
• High Price : Assigned to products with prices over 100.

Solution explanation: This example effectively uses the CASE WHEN statement to categorize products by price range. The conditions are clear and the resulting labels are meaningful, allowing for easier analysis.

Example 7: Analyzing Order Fulfillment Status

Exercise: In an online store, ensuring orders are delivered on time is key to customer satisfaction. With a high volume of orders, gaining insights is tough. How can you improve data analysis to track and optimize order fulfillment?

The expected output of this exercise should present a clear breakdown of each order, including the OrderID , OrderDate , ShippedDate , and FulfillmentStatus . The FulfillmentStatus column categorizes orders into three groups:

• Not Shipped : Indicates orders awaiting shipment with a NULL ShippedDate .
• Shipped Late : Designates orders where the ShippedDate exceeds three days from the OrderDate , signaling a delay.
• Shipped On Time : Applied to orders shipped within three days of the OrderDate , ensuring timely fulfillment.

Solution explanation: This example showcases the effective use of CASE WHEN to categorize orders by fulfillment status. The conditions are logically structured, providing clear insights into the fulfillment process. The query aligns with the previously discussed tips by addressing NULL values so that orders with no shipment date will not be recorded as NULL.

Example 8: Segmenting Customers by Purchase Frequency

Exercise: In online retail, understanding customer buying habits is crucial. But when you have a large customer base, gaining clear insights is a challenge. Take this into account for this exercise, as you’re asked to segment customers based on their purchase frequency.

The expected output of this exercise should provide a segmented view of customers based on their purchase frequency. It should include the CustomerID, TotalOrders, and CustomerSegment. The CustomerSegment column categorizes customers into three groups:

• Infrequent Shopper : Applied to customers with a purchase frequency of one order.
• Regular Shopper : Applied to customers with a purchase frequency between two and five orders.
• Frequent Shopper : Applied to customers with a purchase frequency exceeding five orders.

Solution explanation: In this example, we include aggregate functions with the CASE WHEN statement to categorize customers by order frequency. By doing so, we can categorize the customers based on the frequency of their spending on the website. You can find more examples of combining aggregate functions with the CASE WHEN statement in our article How to Use CASE WHEN With SUM() .

Example 9: Assessing Employee Performance Ratings

Exercise: For effective HR decisions, evaluating employee performance is essential. Yet, with so many employees, the analysis process is complex. How can you streamline data analysis to assess and categorize employees based on productivity scores?

The expected output of this exercise should streamline the analysis of employee performance, providing a clear evaluation of each employee. It should return the EmployeeID , ProductivityScore , and PerformanceRating . The PerformanceRating column categorizes employees into three groups:

• Excellent : Applied to employees with a ProductivityScore of 90 or higher, indicating outstanding performance.
• Good : Applied to employees with a ProductivityScore between 70 and 89, reflecting good performance.
• Needs Improvement : Applied to employees with a ProductivityScore below 70, indicating areas where improvement is needed.

Solution explanation: By defining ranges for performance with the CASE WHEN statement, we can categorize employees based on productivity scores. Since the conditions are clear, the resulting performance ratings provide actionable insights.

Example 10: Grouping Products by Release Year

Exercise: Managing product inventory requires understanding product life cycles. In a large catalog, identifying patterns can be tricky. How can you simplify this to effectively group products based on their release years?

The expected output of this exercise should provide a clear grouping of products based on their release years. It should include the ProductID , ProductName , ReleaseYear , and ReleaseCategory . The ReleaseCategory column categorizes products into three groups:

• New Release : Applied to products released in the year 2023, indicating the latest additions to the catalog.
• Recent Release : Applied to products released between 2018 and 2022, signifying recently introduced items.
• Old Release : Applied to products released before the year 2018, identifying older items in the catalog.

Solution explanation: This final example showcases the versatility of CASE WHEN . Here, we’ve used it with DATETIME functions to categorize products based on their release years.

8 Tips for Using CASE WHEN in Data Analysis

Here are 10 tips to help you make the most of the CASE WHEN statement:

• Understand Your Data: Before implementing CASE WHEN , make sure you clearly understand the data you're working with. Identify the specific conditions or criteria that will add value to your analysis.
• Start Simple: If you're new to using CASE WHEN , begin with simple conditions. You can gradually add complexity as needed. This ensures your code remains readable and easier to troubleshoot.
• Use Meaningful Labels: When categorizing data, choose labels that are clear and meaningful. This enhances the interpretability of your results and makes the analysis more accessible to others.
• Consider Data Types: Make sure the data types in your conditions correspond to the data you are examining. Mismatched data types can lead to unexpected behaviors and errors.
• Combine Conditions Logically: Use AND and OR operators to express compound conditions. Parentheses can help clarify the order of evaluation.
• Address NULL Values: Consider using IS NULL , IS NOT NULL , or ELSE conditions to explicitly handle any columns in your analysis that may have NULL
• Utilize Aggregated Columns: CASE WHEN is particularly useful when creating aggregated columns. For example, you can categorize groups based on aggregated values (e.g. totals or averages) to gain insights into patterns or trends.
• Test and Validate: Test your CASE WHEN statements on a smaller subset of your data before applying them to the entire dataset. This helps catch any unexpected issues and ensures the logic works as intended.

Beyond the Basics with CASE WHEN

The CASE WHEN statement in SQL is a pivotal tool. It provides a structured and flexible approach to conditional logic – one that mirrors everyday decision-making. Plus, its intuitive nature makes it an accessible yet powerful feature of SQL. If you need to create custom classifications, handle NULL values, and categorize data dynamically, the CASE WHEN statement is a true asset.

Whether you are a beginner navigating SQL or an experienced analyst, mastering the CASE WHEN statement is a key step toward unlocking deeper layers of data analysis. Dive further into its complexities with our articles How to Use CASE in ORDER BY in SQL and How to Use CASE WHEN in GROUP BY . And for an interactive learning experience, check out our Creating Basic SQL Reports course. Happy learning!

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Understanding the SQL Server CASE statement

SQL Server CASE statement is equivalent to the IF-THEN statement in Excel .

The CASE statement is used to implement the logic where you want to set the value of one column depending upon the values in other columns.

The SQL Server CASE Statement consists of at least one pair of WHEN and THEN statements. The WHEN statement specifies the condition to be tested. The THEN statement specifies the action if the WHEN condition returns TRUE.

The ELSE statement is optional and executes when none of the WHEN conditions return true. The CASE statement ends with an END keyword.

In this article, we will take a look at a number of different examples of the CASE statement. But before we do that, we’ll create some dummy data to work with.

Creating dummy data

Execute the following script to create the dummy data:

The script above has created a dummy database called ShowRoom with one Table in it called Cars. The Cars table has seven columns: id, name, company, power, color, model, and condition.

Now let’s insert some dummy data into the Cars table. Execute the following script:

Let’s check how our dataset looks, execute the following script:

The output looks like this:

You can see that the condition column contains an X in each row at the moment. We will set the value of the condition column, depending on the model column, using the CASE statement so that you can see clearly what is going on.

SQL Server CASE statement syntax

The syntax of the CASE statement is pretty straight forward:

The CASE statement has to be included inside the SELECT Statement. It starts with the CASE keyword followed by the WHEN keyword and then the CONDITION.

The condition can be any valid SQL Server expression which returns a boolean value. For instance, the condition can be model > 2000, the THEN clause is used after the CONDITION. If the CONDITION returns true the value that follows the THEN clause is stored in columnX. Else, the value after the ELSE clause, will also be stored in columnX. The SQL Server CASE statement ends with the END clause.

CASE statement examples

Let’s now see the CASE statement in action.

In a previous section, we created a table named Cars inside the ShowRoom database.

The condition column had the value X for all rows. We will use the SQL Server CASE statement to set the value of the condition column to “New” if the model column has a value greater than 2000, otherwise the value for the condition column will be set to “Old”.

Look at the following script:

The above script displays the name, model and condition columns from the Cars table. The output of the script above looks like this:

You can see that the value of X in the condition column has been replaced by “New” and “Old” depending upon the model of the car.

Multiple conditions in CASE statement

You can evaluate multiple conditions in the CASE statement.

Let’s write a SQL Server CASE statement which sets the value of the condition column to “New” if the value in the model column is greater than 2010, to ‘Average’ if the value in the model column is greater than 2000, and to ‘Old’ if the value in the model column is greater than 1990.

The output of the script above looks like this:

In the script above, we assigned three different values to the condition column depending on the value in the model column.

However, in the above script, the conditions are overlapping as. the model with a value greater than 2010 also has a value greater than 2000 and 1990. A better way to implement multiple conditions is to use logical operators like AND, OR, NOT, etc. Look at the following script:

We can also evaluate multiple conditions from different columns using the SQL Server CASE statement. In the following example, we will assign the value of “New White” to the condition column where the model is greater than 2010 and the color is white. Look at the following script:

You can see from row 5 that since the color is white and the model is greater than 2010, value for the condition column has been set to “New White” in the output.

Using GROUP BY with SQL Server CASE statement

The CASE statement can also be used in conjunction with the GROUP BY statement in order to apply aggregate functions.

For example, if we want to count the number of new (model number greater than 2000) and old (model number less than 2000) vehicles, we can use the GROUP BY clause with the CASE statement as follows:

In the script above we use the COUNT aggregate function with the CASE statement.

The SQL Server CASE statement sets the value of the condition column to “New” or “Old”. Inside the GROUP BY clause, we specify that the corresponding count for “New” is incremented by 1, whenever a model value of greater than 2000 is encountered. The Else section means that we increase the count for “Old” by 1 if the value of the model is 2000 or less. The output of the script above looks like this:

Since our dataset has five vehicles with a value for ‘model’ of greater than 2000, you can see a 5 in the count column for “New” vehicles. Similarly, we had 5 old vehicles and hence we can see 5 for the count column of “Old” vehicles.

Similarly, we can GROUP BY more than two values. Look at the following script:

In the script above, we grouped the data into three categories: “New”, “Average” and “Old”.

You can see the count for “New”, “Average” and “Old” condition cars.

The CASE statement comes in handy when you want to implement IF-THEN logic in SQL Server. In this article, we saw what CASE statement is along with its syntax. We also saw different examples of CASE statement along with its usage with the GROUP BY clause.

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