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  • LeetCode SQL 50 Solution/1193. Monthly Transactions I

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

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