Practical sharing of data statistical analysis in MySQL

With the advent of the data era, data statistical analysis has become increasingly important in the fields of business and science. As a popular relational database management system, MySQL provides rich data processing and analysis functions. In this article, I will share several practical experiences in data statistical analysis based on MySQL.

1. Data preprocessing

Before conducting statistical analysis of data, data preprocessing is a very important step. This typically includes data extraction, cleaning, filtering, and transformation. In MySQL, we can use statements such as LOAD DATA INFILE, SELECT, UPDATE and DELETE to accomplish these tasks. For example, we can use the SELECT statement to exclude invalid data:

SELECT *
FROM table_name
WHERE column_name IS NOT NULL;

2. Aggregation function

Aggregation function is the basis for statistical analysis of data in MySQL. Common aggregate functions include SUM, AVG, MAX, MIN and COUNT. These functions can be applied to a single column or to multiple columns.

For example, we can use the SUM function to calculate the sum of a certain column:

SELECT SUM(column_name)
FROM table_name;

3. Grouping and sorting

When we need to analyze between different groups of data When comparing differences, grouping and sorting can be used to achieve this. In MySQL, we can use GROUP BY and ORDER BY statements to accomplish these tasks. The GROUP BY statement groups data by specific columns, while the ORDER BY statement sorts data by specific columns.

For example, we can use the GROUP BY statement to calculate the number of products in each category:

SELECT category, COUNT(*)
FROM products
GROUP BY category;

4. Subquery

The subquery is performed in MySQL Another important tool for statistical analysis of data. Subqueries can be used in SELECT, UPDATE and DELETE statements.

For example, we can use a subquery to find some data that does not meet the conditions:

SELECT *
FROM table_name
WHERE column_name NOT IN (
    SELECT column_name
    FROM another_table_name
    WHERE condition
);

5. Combined query

Sometimes, we need to query from multiple Data are combined in tables for statistical analysis. In MySQL, we can use the UNION statement to achieve this. The UNION statement can combine the results of multiple SELECT statements into a result set.

For example, we can use the UNION statement to combine data from two tables:

SELECT column1, column2
FROM table1
UNION
SELECT column1, column2
FROM table2;

Summary

MySQL provides rich data processing and analysis functions that can Better support data statistical analysis. In practice, we need to flexibly use various statements and functions and choose appropriate methods to process and analyze data. This allows you to better understand your data, find useful information within it, and make more informed decisions.

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