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Here’s a well-structured `README.md` for **LeetCode 1327 - List the Products Ordered in a Period**, formatted for a GitHub repository:
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```md
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# 🛒 List the Products Ordered in a Period - LeetCode 1327
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## 📌 Problem Statement
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You are given two tables: **Products** and **Orders**.
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Your task is to **list the product names** that had at least **100 units ordered in February 2020** along with the total amount ordered.
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---
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## 📊 Table Structure
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### **Products Table**
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| Column Name | Type |
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| ---------------- | ------- |
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| product_id | int |
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| product_name | varchar |
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| product_category | varchar |
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- `product_id` is the **primary key** (unique identifier).
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- This table contains details about products.
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---
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### **Orders Table**
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| Column Name | Type |
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| ----------- | ---- |
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| product_id | int |
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| order_date | date |
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| unit | int |
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- `product_id` is a **foreign key** referencing the `Products` table.
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- `order_date` represents when the order was placed.
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- `unit` represents the **number of products ordered** on that date.
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- The table **may contain duplicate rows**.
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---
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## 🔢 Goal:
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Find all products that had **at least 100 units ordered** during **February 2020** and display:
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- `product_name`
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- Total `unit` ordered in that period
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---
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## 📊 Example 1:
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### **Input:**
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#### **Products Table**
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| product_id | product_name | product_category |
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| ---------- | --------------------- | ---------------- |
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| 1 | Leetcode Solutions | Book |
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| 2 | Jewels of Stringology | Book |
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| 3 | HP | Laptop |
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| 4 | Lenovo | Laptop |
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| 5 | Leetcode Kit | T-shirt |
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#### **Orders Table**
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| product_id | order_date | unit |
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| ---------- | ---------- | ---- |
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| 1 | 2020年02月05日 | 60 |
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| 1 | 2020年02月10日 | 70 |
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| 2 | 2020年01月18日 | 30 |
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| 2 | 2020年02月11日 | 80 |
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| 3 | 2020年02月17日 | 2 |
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| 3 | 2020年02月24日 | 3 |
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| 4 | 2020年03月01日 | 20 |
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| 4 | 2020年03月04日 | 30 |
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| 4 | 2020年03月04日 | 60 |
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| 5 | 2020年02月25日 | 50 |
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| 5 | 2020年02月27日 | 50 |
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| 5 | 2020年03月01日 | 50 |
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### **Output:**
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| product_name | unit |
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| ------------------ | ---- |
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| Leetcode Solutions | 130 |
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| Leetcode Kit | 100 |
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### **Explanation:**
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- **Leetcode Solutions** (ID=1) was ordered in February:
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\[
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60 + 70 = 130 \quad (\text{✓ included})
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\]
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- **Jewels of Stringology** (ID=2) was ordered **only 80** times in February. (**✗ not included**)
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- **HP Laptop** (ID=3) was ordered **5 times** in February. (**✗ not included**)
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- **Lenovo Laptop** (ID=4) was **not ordered** in February. (**✗ not included**)
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- **Leetcode Kit** (ID=5) was ordered **100 times** in February. (**✓ included**)
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---
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## 🖥 SQL Solution
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### ✅ **Using `JOIN` + `GROUP BY` + `HAVING`**
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#### **Explanation:**
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1. **Join** the `Products` and `Orders` tables on `product_id`.
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2. **Filter orders** placed in **February 2020** (`BETWEEN '2020年02月01日' AND '2020年02月29日'`).
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3. **Sum up the `unit` ordered** for each product.
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4. **Use `HAVING` to filter products with at least 100 units.**
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5. Return results in **any order**.
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```sql
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SELECT P.PRODUCT_NAME, SUM(O.UNIT) AS UNIT
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FROM PRODUCTS P
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INNER JOIN ORDERS O
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ON P.PRODUCT_ID = O.PRODUCT_ID
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WHERE O.ORDER_DATE BETWEEN '2020年02月01日' AND '2020年02月29日'
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GROUP BY P.PRODUCT_NAME
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HAVING SUM(O.UNIT) >= 100;
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```
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---
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## 🐍 Pandas Solution (Python)
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#### **Explanation:**
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1. **Merge** `products` and `orders` on `product_id`.
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2. **Filter only February 2020 orders**.
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3. **Group by `product_name`** and **sum `unit`**.
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4. **Filter products with at least 100 units**.
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5. **Return the final DataFrame**.
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```python
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import pandas as pd
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def products_ordered(products: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame:
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# Merge both tables on product_id
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merged_df = pd.merge(orders, products, on="product_id", how="inner")
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# Convert order_date to datetime format and filter February 2020
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merged_df["order_date"] = pd.to_datetime(merged_df["order_date"])
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feb_orders = merged_df[
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(merged_df["order_date"] >= "2020年02月01日") & (merged_df["order_date"] <= "2020年02月29日")
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]
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# Group by product_name and sum the units
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result = feb_orders.groupby("product_name")["unit"].sum().reset_index()
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# Filter products with at least 100 units
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result = result[result["unit"] >= 100]
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return result
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```
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---
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## 📁 File Structure
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```
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📂 List-Products-Ordered
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│── 📜 README.md
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│── 📜 solution.sql
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│── 📜 solution_pandas.py
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│── 📜 test_cases.sql
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```
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---
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## 🔗 Useful Links
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- 📖 [LeetCode Problem](https://leetcode.com/problems/list-the-products-ordered-in-a-period/)
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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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```
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### Features of this `README.md`:
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✅ **Detailed problem statement with tables**
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✅ **Example with step-by-step calculations**
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✅ **SQL and Pandas solutions with explanations**
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✅ **File structure for easy organization**
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✅ **Helpful references for further reading**
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Would you like any modifications? 🚀

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