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  • LeetCode SQL 50 Solution/1045. Customers Who Bought All Products

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Here’s the updated README.md, including **Pandas and Window SQL solutions** along with the original **MySQL solution**.
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---
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# **1045. Customers Who Bought All Products**
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## **Problem Statement**
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You are given two tables:
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- `Customer` (contains `customer_id` and `product_key`)
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- `Product` (contains all available `product_key`s)
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Each `product_key` in `Customer` is a **foreign key** referring to the `Product` table.
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### **Customer Table**
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```
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+-------------+-------------+
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| Column Name | Type |
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+-------------+-------------+
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| customer_id | int |
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| product_key | int |
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+-------------+-------------+
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```
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- The table may contain **duplicate rows**.
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- `customer_id` is **not NULL**.
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- `product_key` refers to the `Product` table.
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### **Product Table**
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```
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+-------------+
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| product_key |
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+-------------+
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| int |
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+-------------+
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```
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- `product_key` is the **primary key** (unique values) of this table.
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### **Task:**
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Find **all customer IDs** who bought **every product** listed in the `Product` table.
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---
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## **Example 1:**
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### **Input:**
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**Customer Table**
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```
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+-------------+-------------+
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| customer_id | product_key |
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+-------------+-------------+
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| 1 | 5 |
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| 2 | 6 |
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| 3 | 5 |
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| 3 | 6 |
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| 1 | 6 |
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+-------------+-------------+
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```
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**Product Table**
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```
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+-------------+
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| product_key |
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+-------------+
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| 5 |
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| 6 |
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+-------------+
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```
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### **Output:**
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```
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+-------------+
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| customer_id |
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+-------------+
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| 1 |
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| 3 |
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+-------------+
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```
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### **Explanation:**
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- There are **two products** (5 and 6).
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- Customers who bought **both** products:
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- **Customer 1**: Bought `5, 6` ✅
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- **Customer 2**: Bought `6` ❌ (missing `5`)
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- **Customer 3**: Bought `5, 6` ✅
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- So, **customers 1 and 3** are returned.
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---
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## **SQL Solutions**
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### **1️⃣ Standard MySQL Solution**
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```sql
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SELECT customer_id
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FROM Customer
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GROUP BY customer_id
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HAVING COUNT(DISTINCT product_key) = (SELECT COUNT(product_key) FROM Product);
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```
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#### **Explanation:**
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1. **GROUP BY `customer_id`** → Group purchases per customer.
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2. **COUNT(DISTINCT product_key)** → Count unique products each customer bought.
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3. **Compare with total products:**
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- `(SELECT COUNT(product_key) FROM Product)` counts all available products.
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- Only customers with `COUNT(DISTINCT product_key) = total products` are included.
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4. **HAVING** ensures we return only those who bought **all products**.
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---
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### **2️⃣ Window Function (SQL) Solution**
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```sql
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WITH product_count AS (
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SELECT COUNT(*) AS total_products FROM Product
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),
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customer_purchase AS (
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SELECT customer_id, COUNT(DISTINCT product_key) AS purchased_count
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FROM Customer
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GROUP BY customer_id
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)
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SELECT customer_id
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FROM customer_purchase, product_count
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WHERE customer_purchase.purchased_count = product_count.total_products;
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```
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#### **Explanation:**
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1. **CTE `product_count`** → Stores total number of products in `Product` table.
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2. **CTE `customer_purchase`** → Groups purchases per customer and counts distinct products.
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3. **Final SELECT query** → Compares each customer's purchase count with `total_products` and returns only those who match.
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---
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## **Pandas Solution (Python)**
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```python
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import pandas as pd
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# Sample data
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customer_data = {'customer_id': [1, 2, 3, 3, 1],
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'product_key': [5, 6, 5, 6, 6]}
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product_data = {'product_key': [5, 6]}
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# Create DataFrames
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customer_df = pd.DataFrame(customer_data)
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product_df = pd.DataFrame(product_data)
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# Get the total number of products
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total_products = product_df['product_key'].nunique()
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# Count distinct products per customer
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customer_purchase = customer_df.groupby('customer_id')['product_key'].nunique()
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# Filter customers who bought all products
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result = customer_purchase[customer_purchase == total_products].reset_index()
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print(result)
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```
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### **Explanation:**
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1. **Create DataFrames** → Convert customer and product tables into Pandas DataFrames.
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2. **Get total unique products** → `product_df['product_key'].nunique()`
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3. **Count distinct products per customer** → `.groupby('customer_id')['product_key'].nunique()`
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4. **Filter customers who match total products** → Customers with `purchased_count == total_products`
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5. **Return final result**.
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---
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## **File Structure**
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```
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LeetCode1045/
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├── problem_statement.md # Contains the problem description and constraints.
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├── sql_solution.sql # Contains the SQL solutions (Standard + Window Functions).
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├── pandas_solution.py # Contains the Pandas solution.
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├── README.md # Overview of the problem and available solutions.
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```
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---
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## **Useful Links**
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- [LeetCode Problem 1045](https://leetcode.com/problems/customers-who-bought-all-products/)
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- [SQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
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- [SQL HAVING Clause](https://www.w3schools.com/sql/sql_having.asp)
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- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
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---ture & Useful Links**
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🚀 **Now it's a complete guide!** 🚀

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