Statements for grouping queries in sql

The statement used for grouping queries in SQL is GROUP BY, which groups the data set according to the specified column or expression and calculates the aggregate value of each group, such as sum or average. For example, GROUP BY product_name and SUM(sales_amount) calculates the total sales for each product, producing the following result: product_name, total_sales, where product_name is the grouping column and total_sales is the sum of sales for each group. GROUP BY queries can be nested to create more complex groupings, for example, nested GROUP BY product_ca

Statements for grouping queries in SQL

Grouping query is a query that groups a data set according to a specific column or expression and calculates the aggregate value (such as sum, average) of each group. In SQL, the main statement used for grouping queries is GROUP BY.

GROUP BY statement

The basic syntax of the GROUP BY statement is as follows:

<code>SELECT 列名, 聚合函数(列名)
FROM table_name
GROUP BY 列名</code>

Among them:

• Column names: The column or expression that needs to be grouped by.
• Aggregation function: Aggregation function applied to each group, such as SUM(), AVG(), COUNT().

Example

Consider a table named "sales" that contains the following columns:

##product_idproduct_namesales_amount##12345To calculate the total sales of each product, you can use the following GROUP BY query:

Apple	100
Orange	200
Banana	300
Apple	250
Orange	150

<code class="sql">SELECT product_name, SUM(sales_amount)
FROM sales
GROUP BY product_name;</code>

The query results will be As shown below:

product_name##Apple350Orange350Banana300NESTED GROUP BY

total_sales

GROUP BY queries can be nested within other queries to create more complex groupings. For example, to calculate the total sales for each product category and each product, you can use the following nested GROUP BY query:

<code class="sql">SELECT product_category, product_name, SUM(sales_amount)
FROM sales
GROUP BY product_category, product_name;</code>

The query results will look like this:

product_categoryproduct_nametotal_sales##FruitsAppleBananaOrange

350		Fruits
300		Fruits
350

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