MySQL Pivot - SQL

In MySQL, a pivot is a way to transform rows into columns. This can be very useful for data analysis and reporting. In this article, we will discuss how to pivot data in MySQL using SQL queries.

Sample Data

To demonstrate pivot queries, we will use a sample dataset that contains the following fields:

• id - An integer value representing the record ID
• month - A string value representing the month of the year (Jan, Feb, Mar, etc.)
• category - A string value representing a product category (books, electronics, clothing, etc.)
• sales - A decimal value representing the total sales for the month and category

Here is a sample dataset:

| id | month | category | sales | |---:|-------|------------|-------| | 1 | Jan | books | 100.0 | | 2 | Jan | electronics| 200.0 | | 3 | Jan | clothing | 300.0 | | 4 | Feb | books | 150.0 | | 5 | Feb | electronics| 250.0 | | 6 | Feb | clothing | 350.0 | | 7 | Mar | books | 200.0 | | 8 | Mar | electronics| 300.0 | | 9 | Mar | clothing | 400.0 |

Pivot Query

To pivot data in MySQL, we will use the CASE statement and the GROUP BY clause. Here is a query that pivots the sample dataset by month:

SELECT
  category,
  SUM(CASE WHEN month = 'Jan' THEN sales ELSE 0 END) AS 'Jan',
  SUM(CASE WHEN month = 'Feb' THEN sales ELSE 0 END) AS 'Feb',
  SUM(CASE WHEN month = 'Mar' THEN sales ELSE 0 END) AS 'Mar'
FROM
  sales_data
GROUP BY
  category;

This query will produce the following output:

| category | Jan | Feb | Mar | |-------------|-------|-------|-------| | books | 100.0 | 150.0 | 200.0 | | electronics | 200.0 | 250.0 | 300.0 | | clothing | 300.0 | 350.0 | 400.0 |

Explanation

Let's break down the pivot query step-by-step:

1. Select the category field and pivot the data by month using the CASE statement.
2. Use the SUM function to calculate the total sales for each category and month.
3. Group the results by the category field using the GROUP BY clause.

The CASE statement inside the SUM function will only add the sales amount for the specified month and category combination. For example, in the first row of the pivot table, the query adds the sales amounts for books in January, February, and March. The other months are excluded (i.e. the sales amount is zero).

Conclusion

Pivoting data in MySQL using SQL queries can be a powerful tool for analyzing and reporting data. By using the CASE statement and the GROUP BY clause, you can transform rows into columns and gain new insights into your dataset.