How to use MySQL's data analysis functions for advanced data analysis

How to use MySQL's data analysis functions for advanced data analysis

In the field of data analysis, MySQL, as a powerful and easy-to-use relational database, has a wealth of data analysis functions that can help us perform various tasks. Advanced data analysis. This article will introduce how to use MySQL's data analysis functions to perform advanced data analysis, and attach code examples.

1. Overview

Data analysis functions are a set of powerful built-in functions provided by MySQL, which can perform aggregation, sorting, ranking, window calculation and other operations on data. These functions can help us perform efficient calculations and analysis on large-scale data to gain insight into data patterns and trends.

2. Introduction to commonly used data analysis functions

1. SUM: Calculate the sum of a column.
2. AVG: Calculate the average of a column.
3. COUNT: Count the number of rows in a column.
4. MAX: Calculate the maximum value of a column.
5. MIN: Calculate the minimum value of a column.
6. GROUP_CONCAT: Concatenate the values ​​of a column into a string.
7. RANK: Calculate the ranking of a column.
8. ROW_NUMBER: Assign a unique row number to each row.
9. LAG: Get the value of the previous row.
10. LEAD: Get the value of the next row.
11. NTILE: Divide the data into multiple groups by ranking.
12. SUM OVER: Calculate the cumulative sum of a column.
13. AVG OVER: Calculate the cumulative average of a column.
14. ROWS BETWEEN: Define the scope of the window function.

3. Code example

1. Calculate the sum of sales

SELECT SUM(sales) AS total_sales FROM orders;

2. Calculate the average order amount

SELECT AVG(order_amount) AS average_amount FROM orders;

3. Count order quantity

SELECT COUNT(*) AS total_orders FROM orders;

4. Find the highest and lowest order amount

SELECT MAX(order_amount) AS max_amount, MIN(order_amount) AS min_amount FROM orders;

5. Concatenate the values ​​of a specific column into a string

SELECT GROUP_CONCAT(product_name) AS products FROM products;

6. Calculate the ranking of a column

SELECT product_name, sales, RANK() OVER (ORDER BY sales DESC) AS ranking FROM products;

7. Assign a unique row number to each row

SELECT product_name, ROW_NUMBER() OVER (ORDER BY product_id) AS row_number FROM products;

8. Get the value of the previous row and the next row

SELECT order_date, order_amount, LAG(order_amount) OVER (ORDER BY order_date) AS previous_amount, LEAD(order_amount) OVER (ORDER BY order_date) AS next_amount FROM orders;

9. Divide the data into 4 groups by ranking

SELECT product_name, sales, NTILE(4) OVER (ORDER BY sales DESC) AS quartile FROM products;

10. Calculate the accumulation of a column Sum

SELECT order_date, order_amount, SUM(order_amount) OVER (ORDER BY order_date) AS cumulative_sales FROM orders;

11. Calculate the cumulative average of a column

SELECT order_date, order_amount, AVG(order_amount) OVER (ORDER BY order_date) AS average_sales FROM orders;

12. Define the range of the window function

SELECT order_date, order_amount, SUM(order_amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_sales FROM orders;

four , Summary

By using MySQL's data analysis functions, we can easily perform various advanced data analysis. This article introduces commonly used data analysis functions and provides corresponding code examples. We hope that readers can use these examples to further familiarize themselves with and master the data analysis functions of MySQL, thereby playing a greater role in actual data analysis work.

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