Data analysis skills in MySQL

MySQL is a very powerful database management system, which has the advantages of efficiency, stability, and ease of use. Applying some data analysis skills in MySQL can allow you to master the data faster and study the data more accurately. In this article, we will introduce some data analysis techniques in MySQL.

1. Using subqueries

Subqueries are a very common technique for using subqueries for data analysis. It can use the results of one query as a condition or restriction for another query. Through this operation, complex data analysis operations such as grouping, filtering, restriction, and statistics can be easily implemented.

For example, if we want to query the 5 users with the most occurrences, we can use the following code:

SELECT user_id, COUNT(*) AS count
FROM log
GROUP BY user_id
ORDER BY count DESC
LIMIT 5;

If we want to see the detailed information of these 5 users, such as username, registration Time, etc., you can use the following code:

SELECT *
FROM user
WHERE user_id IN (
  SELECT user_id
  FROM log
  GROUP BY user_id
  ORDER BY COUNT(*) DESC
  LIMIT 5
);

2. Using analytic functions

The analytic functions in MySQL are also a very useful data analysis technique. Through it, we can perform statistics, sorting and other operations very conveniently.

For example, if we want to query the information of recently logged-in users, we can use the following code:

SELECT user_id, login_time,
  ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY login_time DESC) rn
FROM log;

This query uses the ROW_NUMBER function to sort the last login time of each user. And numbered using analytic functions. Here, we use PARTITION BY to specify the user ID as the grouping condition, and ORDER BY to specify the login time as the sorting basis.

3. Using the WITH statement

The WITH statement is also a very useful data analysis technique. It can help us better organize and call subqueries and improve query efficiency.

For example, if we want to query products with greater than average sales, we can use the following code:

WITH avg_sales AS (
  SELECT AVG(sales) AS avg_sales
  FROM product
)
SELECT *
FROM product
WHERE sales > (SELECT avg_sales FROM avg_sales);

In this query, we use the WITH statement to define the subquery avg_sales for calculation average sales. In the main query, we use the avg_sales subquery to determine whether sales are greater than average sales.

4. Using the JOIN statement

The JOIN statement is also a very common data analysis technique. It can associate data in multiple tables for more in-depth analysis.

For example, if we want to query the category of the product with the highest sales, we can use the following code:

SELECT category.name, product.name, product.sales
FROM product
JOIN category ON product.category_id = category.category_id
ORDER BY product.sales DESC
LIMIT 1;

In this query, we use the JOIN statement to associate the product table and category table. , query the category of the product with the highest sales through the name column in the category table.

Summary

The above are some data analysis techniques in MySQL. Using these techniques, you can master the data faster and conduct data analysis more accurately. Of course, this is just the tip of the iceberg. MySQL is widely used in data analysis. I hope readers can further understand and master this technology and provide more powerful support for data analysis.

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