Pivot MySQL

Prerequisites

Before attempting to write a query to pivot table in MySQL, you should have a good understanding of the following concepts:

• Aggregate functions: You should know how to use functions like COUNT, SUM, AVG, MIN, MAX, etc., to perform calculations on groups of rows in a table.
• GROUP BY clause: You should know how to use the GROUP BY clause to group rows based on one or more columns in a table.
• Joins: You should know how to use JOIN clauses to combine data from multiple tables based on common columns.
• Subqueries: You should be familiar with subqueries and how to use them to generate intermediate results that can be used in a larger query.
• Conditional expressions: You should be familiar with conditional expressions, such as IF, CASE, and COALESCE, and how to use them to create conditional logic in queries.

Once you have a good understanding of these concepts, you can start to learn about a pivot in MySQL. A pivot query is a way to transform data from rows to columns, or vice versa, based on a particular aggregation function and a set of grouping criteria. Pivot queries can be useful for generating summary reports, analyzing trends, or comparing data across multiple categories.

What is PIVOT?

A pivot table in MySQL is a database operation that allows you to transform data from rows into columns, effectively rotating a table on its axis. In a pivot operation, you select a set of columns that you want to use as new column headers, and then you pivot the data so that the values in those columns become the new column headers, while the remaining columns become the data associated with those headers.

Pivot in MySQL is particularly useful when you have data in a table that needs to be transformed so that you can analyze it more easily. In MySQL, you can use the Pivot operation to transform rows into columns. However, unlike other databases, MySQL doesn't have a built-in Pivot function.

To perform a pivot operation in MySQL, you typically use an SQL query that includes aggregate functions and a GROUP BY clause to group the data based on the columns that you want to use as headers. You then use conditional expressions to specify how to calculate the values for each header column based on the original data.

Sample Data and Expected Output

Pivot in MySQL is a type of query that allows you to transform rows of data into columns, effectively "pivoting" the table. This is useful when you want to summarize data and present it in a more digestible format.

In a pivot query, you specify the columns that you want to pivot on, as well as the aggregate function(s) that you want to apply to the values in those columns. The result of the query is a new table with the pivoted columns as its headers.

Assuming you have a sample table called sales_data with the following columns and data:

To pivot this data by Product Name, you can use the following query:

Code:

Output:

The output will be:

In this query, the MAX function is used to aggregate the Sales Amount for each Product Name for each Sales Date. The CASE statement is used to conditionally select the Sales Amount based on the Product Name. The GROUP BY statement is used to group the data by Sales Date.

This query will pivot the data so that the Product Name becomes the column headers, the Sales Date becomes the row headers, and the Sales Amount are the data associated with each Product Name and Sales Date combination.

Dynamic Pivot Columns

In MySQL, you can use dynamic pivot columns to create a pivot table with unknown or varying column values. Dynamic pivot columns are useful when you don't know the column values beforehand or when the column values change frequently.

Assuming you have a sample table called sales_data with the following columns and data:

To pivot this data by Product Name, you can use dynamic pivot columns with the following query:

Code:

Output:

The output will be:

In this query, the first SELECT statement uses GROUP_CONCAT to dynamically generate the pivot columns based on the distinct Product Name values in the sales_data table.

The generated SQL statement is then stored in a user-defined variable @sql. The PREPARE statement is used to prepare the SQL statement in @sql, and the EXECUTE statement is used to execute the prepared statement. Finally, the DEALLOCATE PREPARE statement is used to deallocate the prepared statement.

This query will pivot the data so that Product Name becomes the column header, Sales Date becomes the row header, and the Sales Amount is the data associated with each Product Name and Sales Date combination. The dynamic pivot columns allow for flexibility in column values, making it easy to pivot data with unknown or varying column values.

Rotate Rows to Columns Using CASE Statement

It's not possible to rotate rows to columns using a CASE statement alone, as the CASE statement is used for conditional expressions and doesn't provide the necessary functionality for pivoting data. To rotate rows to columns, you can use a combination of aggregate functions and the GROUP BY clause along with the CASE statement.

Suppose you have a table named sales_data that contains data about sales transactions, with columns transaction_id, product_name, and sales_amount. Here's some sample data:

Suppose you want to pivot the product_name column into separate columns so that the output shows the total sales amount for each product. Here's the code you can use:

Code:

Output:

In this query, the CASE statements are used to identify the sales amounts that need to be pivoted into columns for each product, and the aggregate function SUM is used to calculate the total sales amount for each product. The output of this query would be:

This output shows that Product A had total sales of 250, Product B had total sales of 500, and Product C had total sales of 50.

Rotate Rows to Columns Using CASE and SUM()

While it's not possible to rotate rows to columns using just the CASE statement, you can use the SUM() function in combination with the CASE statement to achieve this pivot.

Here's an example code snippet that demonstrates how to use the SUM() function and the CASE statement to pivot rows into columns:

Suppose you have a table named customer_orders with columns order_id, customer_name, order_date, and order_amount. Here's some sample data:

Suppose you want to pivot the order_date column into separate columns so that the output shows the total order amount for each date. Here's the code you can use:

Code:

Output:

In this query, the CASE statements are used to identify the order amounts that need to be pivoted into columns for each date, and the aggregate function SUM is used to calculate the total order amount for each date. The output of this query would be:

This output shows that John Smith had total orders of 100 on 2022-01-01, 150 on 2022-01-02, and no orders on 2022-01-03, Mary Johnson had total orders of 200 on 2022-01-01, no orders on 2022-01-02, and 300 on 2022-01-03, and Susan Lee had no orders on 2022-01-01 and 2022-01-03, and order of 50 on 2022-01-02.

Rotate Rows to Columns in Multiple Tables

Rotating rows to columns in multiple tables involves joining the tables together and using the CASE statement and aggregate functions such as SUM() or COUNT() to pivot the data. Here's an example:

Suppose you have two tables, customers, and orders.

• The customers table contains columns customer_id, customer_name, and customer_email.
• The orders table contains columns order_id, customer_id, order_date, and order_amount.

Customers table:

Orders table:

Suppose you want to pivot the order_date column into separate columns for each customer so that the output shows the total order amount for each customer on each date. Here's the code you can use:

Code:

Output:

The JOIN keyword is first used to combine rows from the customers and orders tables based on the common column customer_id to combine data from both tables.

Then, the CASE statements are used to identify the order amounts that need to be pivoted into columns for each date, and the aggregate function SUM is used to calculate the total order amount for each date. The output of this query would be:

This output shows that John Smith had total orders of 100 on 2022-01-01, 150 on 2022-01-02, and no orders on 2022-01-03, Mary Johnson had total orders of 200 on 2022-01-01, no orders on 2022-01-02, and 300 on 2022-01-03, and Susan Lee had no orders on 2022-01-01 and 2022-01-03, and order of 50 on 2022-01-02.