task: #3564

Author: Ivanbyone

README.md

@@ -187,6 +187,7 @@ Useful for preparing for technical interviews and improving your SQL skills.
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     - [3497. Analyze Subscription Conversion](./leetcode/medium/3497.%20Analyze%20Subscription%20Conversion.sql)
+    - [3564. Seasonal Sales Analysis](./leetcode/medium/3564.%20Seasonal%20Sales%20Analysis.sql)
     - [3601. Find Drivers with Improved Fuel Efficiency](./leetcode/medium/3601.%20Find%20Drivers%20with%20Improved%20Fuel%20Efficiency.sql)
 3. [Hard](./leetcode/hard/)
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leetcode/medium/3564. Seasonal Sales Analysis.sql

@@ -0,0 +1,76 @@
+/*
+Question 3564. Seasonal Sales Analysis
+Link: https://leetcode.com/problems/seasonal-sales-analysis/description/?envType=problem-list-v2&envId=database
+
+Table: sales
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| sale_id       | int     |
+| product_id    | int     |
+| sale_date     | date    |
+| quantity      | int     |
+| price         | decimal |
++---------------+---------+
+sale_id is the unique identifier for this table.
+Each row contains information about a product sale including the product_id, date of sale, quantity sold, and price per unit.
+Table: products
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| product_id    | int     |
+| product_name  | varchar |
+| category      | varchar |
++---------------+---------+
+product_id is the unique identifier for this table.
+Each row contains information about a product including its name and category.
+Write a solution to find the most popular product category for each season. The seasons are defined as:
+
+Winter: December, January, February
+Spring: March, April, May
+Summer: June, July, August
+Fall: September, October, November
+The popularity of a category is determined by the total quantity sold in that season. If there is a tie, select the category with the highest total revenue (quantity × price).
+
+Return the result table ordered by season in ascending order.
+*/
+
+WITH stats AS (
+    SELECT
+        p.category,
+        (CASE
+            WHEN EXTRACT(MONTH FROM s.sale_date) BETWEEN 3 AND 5
+                THEN 'Spring'
+            WHEN EXTRACT(MONTH FROM s.sale_date) BETWEEN 6 AND 8
+                THEN 'Summer'
+            WHEN EXTRACT(MONTH FROM s.sale_date) BETWEEN 9 AND 11
+                THEN 'Fall'
+            ELSE 'Winter'
+        END) AS season,
+        SUM(s.quantity) AS total_quantity,
+        SUM(s.quantity * s.price) AS total_revenue
+    FROM sales AS s
+    LEFT JOIN 
+        products AS p
+        ON s.product_id = p.product_id
+    GROUP BY s.season, p.category
+)
+
+SELECT 
+    s.season,
+    s.category,
+    s.total_quantity,
+    s.total_revenue
+FROM (
+    SELECT 
+        s1.season,
+        s1.category,
+        s1.total_quantity,
+        s1.total_revenue,
+        RANK() OVER(PARTITION BY s1.season ORDER BY s1.total_quantity DESC, s1.total_revenue DESC) AS rank
+    FROM stats AS s1
+) AS s
+WHERE s.rank = 1
+ORDER BY s.season ASC