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LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md

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+Here's a well-structured `README.md` file for **LeetCode 1193 - Monthly Transactions I**, formatted for a GitHub repository:
+
+```md
+# 🏦 Monthly Transactions I - LeetCode 1193
+
+## 📌 Problem Statement
+You are given the **Transactions** table that records financial transactions.
+
+### Transactions Table
+| Column Name | Type    |
+| ----------- | ------- |
+| id          | int     |
+| country     | varchar |
+| state       | enum    |
+| amount      | int     |
+| trans_date  | date    |
+
+- **id** is the **primary key**.
+- The **state** column is an `ENUM` type with values **"approved"** and **"declined"**.
+- Each row **records a transaction** with an amount and a transaction date.
+
+### Task:
+Find **monthly statistics** for each country:
+- Total **number of transactions**.
+- Total **amount of transactions**.
+- Total **number of approved transactions**.
+- Total **amount of approved transactions**.
+
+The **month format should be `YYYY-MM`**.
+
+---
+
+## 📊 Example 1:
+### Input:
+**Transactions Table**
+| id  | country | state    | amount | trans_date |
+| --- | ------- | -------- | ------ | ---------- |
+| 121 | US      | approved | 1000   | 2018-12-18 |
+| 122 | US      | declined | 2000   | 2018-12-19 |
+| 123 | US      | approved | 2000   | 2019-01-01 |
+| 124 | DE      | approved | 2000   | 2019-01-07 |
+
+### Output:
+| month   | country | trans_count | approved_count | trans_total_amount | approved_total_amount |
+| ------- | ------- | ----------- | -------------- | ------------------ | --------------------- |
+| 2018-12 | US      | 2           | 1              | 3000               | 1000                  |
+| 2019-01 | US      | 1           | 1              | 2000               | 2000                  |
+| 2019-01 | DE      | 1           | 1              | 2000               | 2000                  |
+
+### Explanation:
+- **December 2018 (US)**:
+  - **2 transactions** (1000 + 2000).
+  - **1 approved transaction** (1000).
+- **January 2019 (US)**:
+  - **1 transaction** (2000).
+  - **1 approved transaction** (2000).
+- **January 2019 (DE)**:
+  - **1 transaction** (2000).
+  - **1 approved transaction** (2000).
+
+---
+
+## 🖥 SQL Solution
+
+### 1️⃣ Standard MySQL Solution
+#### Explanation:
+- **Extract the month** from `trans_date` using `DATE_FORMAT()`.
+- **Count transactions** for each `month` and `country`.
+- **Sum transaction amounts**.
+- **Filter only approved transactions** separately using `CASE WHEN`.
+
+```sql
+SELECT 
+    DATE_FORMAT(trans_date, '%Y-%m') AS month,
+    country,
+    COUNT(id) AS trans_count,
+    SUM(CASE WHEN state = 'approved' THEN 1 ELSE 0 END) AS approved_count,
+    SUM(amount) AS trans_total_amount,
+    SUM(CASE WHEN state = 'approved' THEN amount ELSE 0 END) AS approved_total_amount
+FROM Transactions
+GROUP BY month, country
+ORDER BY month, country;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### Explanation:
+- **Extract the month (`YYYY-MM`)** from `trans_date`.
+- **Group by month and country**.
+- **Compute counts and sums** using `.agg()`.
+
+```python
+import pandas as pd
+
+def monthly_transactions(transactions: pd.DataFrame) -> pd.DataFrame:
+    # Extract 'YYYY-MM' from the trans_date
+    transactions['month'] = transactions['trans_date'].dt.strftime('%Y-%m')
+
+    # Aggregate transaction counts and sums
+    result = transactions.groupby(['month', 'country']).agg(
+        trans_count=('id', 'count'),
+        approved_count=('state', lambda x: (x == 'approved').sum()),
+        trans_total_amount=('amount', 'sum'),
+        approved_total_amount=('amount', lambda x: x[transactions['state'] == 'approved'].sum())
+    ).reset_index()
+
+    return result.sort_values(['month', 'country'])
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Monthly-Transactions
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/monthly-transactions-i/)
+- 📚 [SQL `GROUP BY` Clause](https://www.w3schools.com/sql/sql_groupby.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/groupby.html)
+```
+
+This README includes:
+- **Problem statement**
+- **Example input and output**
+- **SQL solution with explanations**
+- **Pandas solution in Python**
+- **File structure for GitHub**
+- **Useful links**
+
+Let me know if you need any modifications! 🚀