How to use group by in mysql query with where condition with examples

In MySQL, the GROUP BY clause is used to group rows based on the values of one or more columns. When combined with a WHERE condition, it can be used to filter and group data to gain insights into a dataset. In this article, we will explore five examples of how to use GROUP BY with WHERE conditions in MySQL to perform different types of analysis.

Examples of using GROUP BY with WHERE condition in MySQL:

1. Counting the number of orders by product name

SELECT product_name, COUNT(*) AS order_countFROM ordersWHERE order_date >= '2022-01-01' AND order_date <= '2022-01-31'GROUP BY product_name;

This query groups the orders by product name and counts the number of orders for each product, but only for orders placed in January 2022.

2. Finding the total revenue by product category

SELECT product_category, SUM(product_price * quantity) AS total_revenueFROM ordersWHERE order_date >= '2022-01-01' AND order_date <= '2022-01-31'GROUP BY product_category;

This query groups the orders by product category and calculates the total revenue for each category, but only for orders placed in January 2022.

3. Finding the average order value by customer

SELECT customer_name, AVG(order_total) AS avg_order_valueFROM (  SELECT customer_name, order_id, SUM(product_price * quantity) AS order_total  FROM orders  WHERE order_date >= '2022-01-01' AND order_date <= '2022-01-31'  GROUP BY customer_name, order_id) AS order_totalsGROUP BY customer_name;

This query calculates the total value of each order, groups the orders by customer, and then calculates the average order value for each customer, but only for orders placed in January 2022.

4. Finding the most popular product by region

SELECT region, product_name, MAX(order_count) AS max_order_countFROM (  SELECT region, product_name, COUNT(*) AS order_count  FROM orders  WHERE order_date >= '2022-01-01' AND order_date <= '2022-01-31'  GROUP BY region, product_name) AS order_counts_by_regionGROUP BY region;

This query groups the orders by region and product name, counts the number of orders for each product in each region, and then finds the most popular product in each region, but only for orders placed in January 2022.

5. Finding the top 5 customers by total spending

SELECT customer_name, SUM(product_price * quantity) AS total_spendingFROM ordersWHERE order_date >= '2022-01-01' AND order_date <= '2022-01-31'GROUP BY customer_nameORDER BY total_spending DESCLIMIT 5;

This query groups the orders by customer name and calculates the total spending for each customer, but only for orders placed in January 2022. The results are sorted by total spending in descending order and limited to the top 5 customers.

Conclusion:

The GROUP BY clause in MySQL is a powerful tool for grouping and analyzing data. By using a WHERE condition to filter the data before grouping it, we can gain deeper insights into the dataset. In this article, we explored five examples of using GROUP BY with WHERE conditions to perform different types of analysis, including counting orders, calculating revenue, finding popular products, and identifying top customers. These examples demonstrate the flexibility and versatility of the GROUP BY clause in MySQL and highlight its usefulness in data analysis.