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Merge pull request #91 from iamAntimPal/Branch-1
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  • LeetCode SQL 50 Solution

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# 📩 Find Users With Valid E-Mails - LeetCode 1517
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## 📌 Problem Statement
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You are given a table **Users** that contains user registration details, including their emails. Some of these emails may be **invalid**.
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A valid email:
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- Has a **prefix name** and a **domain**.
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- The prefix:
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- **Must start with a letter** (uppercase or lowercase).
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- Can contain **letters**, **digits**, **underscore (`_`)**, **period (`.`)**, and/or **dash (`-`)**.
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- The domain must be **"@leetcode.com"**.
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Your task is to **find users with valid emails**.
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---
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## 📊 Table Structure
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### **Users Table**
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| Column Name | Type |
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| ----------- | ------- |
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| user_id | int |
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| name | varchar |
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| mail | varchar |
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- `user_id` is the **primary key**.
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---
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## 📊 Example 1:
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### **Input:**
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#### **Users Table**
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| user_id | name | mail |
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| ------- | --------- | ----------------------- |
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| 1 | Winston | winston@leetcode.com |
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| 2 | Jonathan | jonathanisgreat |
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| 3 | Annabelle | bella-@leetcode.com |
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| 4 | Sally | sally.come@leetcode.com |
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| 5 | Marwan | quarz#2020@leetcode.com |
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| 6 | David | david69@gmail.com |
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| 7 | Shapiro | .shapo@leetcode.com |
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### **Output:**
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| user_id | name | mail |
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| ------- | --------- | ----------------------- |
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| 1 | Winston | winston@leetcode.com |
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| 3 | Annabelle | bella-@leetcode.com |
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| 4 | Sally | sally.come@leetcode.com |
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### **Explanation:**
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- ✅ **Valid emails:**
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- `winston@leetcode.com` ✅ (Starts with a letter, correct domain)
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- `bella-@leetcode.com` ✅ (Starts with a letter, correct domain)
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- `sally.come@leetcode.com` ✅ (Starts with a letter, correct domain)
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- ❌ **Invalid emails:**
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- `jonathanisgreat` ❌ (No domain)
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- `quarz#2020@leetcode.com` ❌ (Contains `#`, which is not allowed)
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- `david69@gmail.com` ❌ (Wrong domain)
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- `.shapo@leetcode.com` ❌ (Starts with a period `.`)
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---
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## 🖥 SQL Solution
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### ✅ **Using `REGEXP_LIKE` (MySQL)**
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#### **Explanation:**
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- Use `REGEXP_LIKE(mail, '^[A-Za-z]+[A-Za-z0-9_.-]*@leetcode\\.com$')`
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- `^` → Start of string.
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- `[A-Za-z]+` → First character **must** be a **letter**.
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- `[A-Za-z0-9_.-]*` → Rest can be **letters, numbers, `_`, `.`, or `-`**.
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- `@leetcode\\.com$` → Must end with `"@leetcode.com"`.
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```sql
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SELECT *
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FROM Users
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WHERE REGEXP_LIKE(mail, '^[A-Za-z]+[A-Za-z0-9_.-]*@leetcode\\.com$');
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```
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---
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## 📁 File Structure
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```
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📂 Find-Users-With-Valid-Emails
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│── 📜 README.md
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│── 📜 solution.sql
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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/find-users-with-valid-e-mails/)
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- 🔍 [MySQL REGEXP_LIKE Documentation](https://dev.mysql.com/doc/refman/8.0/en/regexp.html)
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- 📝 [SQL Regular Expressions Cheatsheet](https://www.w3schools.com/sql/sql_regex.asp)
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her learning**
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## Would you like any modifications? 🚀
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# 🏥 Patients With a Condition - LeetCode 1527
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## 📌 Problem Statement
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You are given a **Patients** table that stores patient health records, including their medical conditions.
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Each patient's **conditions** column contains **0 or more condition codes**, separated by spaces.
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Your task is to **find all patients who have Type I Diabetes**.
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- Type I Diabetes is identified by a condition **starting with the prefix "DIAB1"**.
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Return the result **in any order**.
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---
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## 📊 Table Structure
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### **Patients Table**
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| Column Name | Type |
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| ------------ | ------- |
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| patient_id | int |
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| patient_name | varchar |
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| conditions | varchar |
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- `patient_id` is the **primary key**.
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- `conditions` contains **space-separated condition codes**.
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---
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## 📊 Example 1:
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### **Input:**
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#### **Patients Table**
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| patient_id | patient_name | conditions |
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| ---------- | ------------ | ------------ |
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| 1 | Daniel | YFEV COUGH |
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| 2 | Alice | |
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| 3 | Bob | DIAB100 MYOP |
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| 4 | George | ACNE DIAB100 |
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| 5 | Alain | DIAB201 |
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### **Output:**
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| patient_id | patient_name | conditions |
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| ---------- | ------------ | ------------ |
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| 3 | Bob | DIAB100 MYOP |
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| 4 | George | ACNE DIAB100 |
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### **Explanation:**
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- ✅ **Bob's condition:** `"DIAB100 MYOP"` → **Starts with `"DIAB1"`** → ✅ **Valid**
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- ✅ **George's condition:** `"ACNE DIAB100"` → **Contains `"DIAB1"`** → ✅ **Valid**
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- ❌ **Daniel's condition:** `"YFEV COUGH"` → **No `"DIAB1"`**
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- ❌ **Alice's condition:** `""` (Empty)
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- ❌ **Alain's condition:** `"DIAB201"` → Does **not** start with `"DIAB1"`
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---
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## 🖥 SQL Solution
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### ✅ **Using `LIKE` with wildcards**
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#### **Explanation:**
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- We need to check if `"DIAB1"` appears **at the beginning** or **somewhere in the conditions column**.
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- `LIKE 'DIAB1%'` → Matches if `"DIAB1"` is at the **start** of the column.
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- `LIKE '% DIAB1%'` → Matches if `"DIAB1"` appears **after a space (as part of multiple conditions).**
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```sql
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SELECT patient_id, patient_name, conditions
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FROM Patients
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WHERE conditions LIKE 'DIAB1%' OR conditions LIKE '% DIAB1%';
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```
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---
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## 📁 File Structure
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```
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📂 Patients-With-Condition
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│── 📜 README.md
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│── 📜 solution.sql
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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/patients-with-a-condition/)
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- 🔍 [SQL LIKE Operator](https://www.w3schools.com/sql/sql_like.asp)
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- 📝 [MySQL String Functions](https://dev.mysql.com/doc/refman/8.0/en/string-functions.html)
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## Would you like any refinements? 🚀
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# 🏬 Customer Who Visited but Did Not Make Any Transactions - LeetCode 1581
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## 📌 Problem Statement
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You are given two tables, **Visits** and **Transactions**.
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- The **Visits** table records the customers who visited the mall.
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- The **Transactions** table records the **transactions made** during a visit.
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Your task is to **find customers who visited but did not make any transactions**.
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Also, return the **number of times** these customers **visited without making any transactions**.
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Return the result **in any order**.
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---
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## 📊 Table Structure
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### **Visits Table**
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| Column Name | Type |
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| ----------- | ---- |
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| visit_id | int |
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| customer_id | int |
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- `visit_id` is the **unique identifier** for each visit.
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### **Transactions Table**
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| Column Name | Type |
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| -------------- | ---- |
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| transaction_id | int |
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| visit_id | int |
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| amount | int |
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- `transaction_id` is the **unique identifier** for each transaction.
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- `visit_id` represents the visit **associated with this transaction**.
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---
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## 📊 Example 1:
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### **Input:**
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#### **Visits Table**
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| visit_id | customer_id |
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| -------- | ----------- |
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| 1 | 23 |
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| 2 | 9 |
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| 4 | 30 |
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| 5 | 54 |
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| 6 | 96 |
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| 7 | 54 |
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| 8 | 54 |
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#### **Transactions Table**
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| transaction_id | visit_id | amount |
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| -------------- | -------- | ------ |
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| 2 | 5 | 310 |
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| 3 | 5 | 300 |
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| 9 | 5 | 200 |
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| 12 | 1 | 910 |
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| 13 | 2 | 970 |
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### **Output:**
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| customer_id | count_no_trans |
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| ----------- | -------------- |
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| 54 | 2 |
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| 30 | 1 |
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| 96 | 1 |
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### **Explanation:**
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- ✅ **Customer 23:** Visited **once**, made **1 transaction** → ❌ Not included
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- ✅ **Customer 9:** Visited **once**, made **1 transaction** → ❌ Not included
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- ✅ **Customer 30:** Visited **once**, made **0 transactions** → ✅ Included (`count_no_trans = 1`)
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- ✅ **Customer 54:** Visited **3 times**, made **transactions in 1 visit**, **but 2 visits had no transactions** → ✅ Included (`count_no_trans = 2`)
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- ✅ **Customer 96:** Visited **once**, made **0 transactions** → ✅ Included (`count_no_trans = 1`)
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---
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## 🖥 SQL Solution
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### ✅ **Using `NOT IN` to Find Visits Without Transactions**
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#### **Explanation:**
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- First, **find all visit IDs** that **had at least one transaction** (`SELECT DISTINCT visit_id FROM Transactions`).
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- Then, filter out these visit IDs from the **Visits** table.
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- Finally, count the number of such visits **per customer**.
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```sql
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SELECT customer_id, COUNT(*) AS count_no_trans
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FROM Visits
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WHERE visit_id NOT IN (SELECT DISTINCT visit_id FROM Transactions)
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GROUP BY customer_id;
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```
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---
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## 📁 File Structure
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```
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📂 Customer-No-Transaction
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│── 📜 README.md
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│── 📜 solution.sql
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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/customer-who-visited-but-did-not-make-any-transactions/)
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- 🔍 [SQL NOT IN Operator](https://www.w3schools.com/sql/sql_in.asp)
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- 📝 [MySQL Aggregate Functions](https://dev.mysql.com/doc/refman/8.0/en/group-by-functions.html)

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