From 01253e255f4382d6635fee6a0a7343767509fe66 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Tue, 11 Mar 2025 15:23:18 +0530
Subject: [PATCH 001/353] this is adding image

---
 README.md             |  13 ++++++++-----
 img/SQL.jpeg          | Bin 0 -> 8536 bytes
 img/leetcodecode.jpeg | Bin 0 -> 5812 bytes
 img/salution.png      | Bin 0 -> 2552 bytes
 4 files changed, 8 insertions(+), 5 deletions(-)
 create mode 100644 img/SQL.jpeg
 create mode 100644 img/leetcodecode.jpeg
 create mode 100644 img/salution.png

diff --git a/README.md b/README.md
index 61ebe5e..eff7bd4 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,19 @@
+<img src="img/SQL.jpeg">
+
 # LeetCode_SQL_Database
 This repo contains my SQL solutions for LeetCode problems. Each file includes a problem description, optimized SQL query, and explanations. The goal is to improve SQL skills, share knowledge, and collaborate. Feel free to explore, suggest improvements, or contribute! 🚀
 
 
-LeetCode SQL Solutions:-
+## LeetCode SQL Solutions:-
+<img src="img/salution.png">
 This repository contains my solutions to SQL problems from LeetCode. Each solution is implemented using MySQL and aims to demonstrate efficient and optimal approaches to solve common database-related challenges.
-
-Repository Structure:-
+<img src="img/leetcodecode.jpeg">
+## Repository Structure:-
 Problem Name: Each SQL file is named after the LeetCode problem it solves.
 Solution Explanation: Each SQL file includes comments explaining the logic and approach used to solve the problem.
 Testing: Sample test cases or scenarios may be included in the comments or README where applicable.
 Why This Repository?
-Learning and Practice:-
+## Learning and Practice:-
 This repository serves as a resource for learning SQL techniques for solving algorithmic problems.
 Collaboration: I welcome feedback and suggestions for improving solutions or alternative approaches.
 Community: By sharing these solutions, I hope to contribute to the SQL programming community and help others learn and grow.
@@ -18,6 +21,6 @@ Contents:
 Problem 1: Description of the problem and the SQL solution.
 Problem 2: Description of the problem and the SQL solution.
 Problem 3: Description of the problem and the SQL solution.
-Contributing:
+### Contributing:
 Feel free to fork this repository, suggest improvements, or submit your own solutions via pull requests. Let's learn and grow together!
 
diff --git a/img/SQL.jpeg b/img/SQL.jpeg
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From 3c282fda1915638305eb349dc841c5f8a75cee60 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Tue, 11 Mar 2025 15:45:48 +0530
Subject: [PATCH 002/353] this is creating readme and hard code of sql

---
 185. Department Top Three Salaries.md  | 111 +++++++++++++
 185. Department Top Three Salaries.sql | 208 +++++++++++++++++++++++++
 README.md                              |  28 +++-
 img/leetcode salution.png              | Bin 0 -> 149298 bytes
 img/leetcode.png                       | Bin 0 -> 139527 bytes
 5 files changed, 341 insertions(+), 6 deletions(-)
 create mode 100644 185. Department Top Three Salaries.md
 create mode 100644 185. Department Top Three Salaries.sql
 create mode 100644 img/leetcode salution.png
 create mode 100644 img/leetcode.png

diff --git a/185. Department Top Three Salaries.md b/185. Department Top Three Salaries.md
new file mode 100644
index 0000000..3b15680
--- /dev/null
+++ b/185. Department Top Three Salaries.md	
@@ -0,0 +1,111 @@
+# 185. Department Top Three Salaries
+
+### Question
+```
+ Table: Employee
+
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| id           | int     |
+| name         | varchar |
+| salary       | int     |
+| departmentId | int     |
++--------------+---------+
+```
+id is the primary key (column with unique values) for this table.
+departmentId is a foreign key (reference column) of the ID from the Department table.
+Each row of this table indicates the ID, name, and salary of an employee. It also contains the ID of their department.
+ 
+
+Table: Department
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| name        | varchar |
++-------------+---------+
+```
+id is the primary key (column with unique values) for this table.
+Each row of this table indicates the ID of a department and its name.
+ 
+
+A company's executives are interested in seeing who earns the most money in each of the company's departments. A high earner in a department is an employee who has a salary in the top three unique salaries for that department.
+
+Write a solution to find the employees who are high earners in each of the departments.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+```
+Input: 
+Employee table:
++----+-------+--------+--------------+
+| id | name  | salary | departmentId |
++----+-------+--------+--------------+
+| 1  | Joe   | 85000  | 1            |
+| 2  | Henry | 80000  | 2            |
+| 3  | Sam   | 60000  | 2            |
+| 4  | Max   | 90000  | 1            |
+| 5  | Janet | 69000  | 1            |
+| 6  | Randy | 85000  | 1            |
+| 7  | Will  | 70000  | 1            |
++----+-------+--------+--------------+
+```
+```
+Department table:
++----+-------+
+| id | name  |
++----+-------+
+| 1  | IT    |
+| 2  | Sales |
++----+-------+
+Output: 
++------------+----------+--------+
+| Department | Employee | Salary |
++------------+----------+--------+
+| IT         | Max      | 90000  |
+| IT         | Joe      | 85000  |
+| IT         | Randy    | 85000  |
+| IT         | Will     | 70000  |
+| Sales      | Henry    | 80000  |
+| Sales      | Sam      | 60000  |
++------------+----------+--------+
+```
+Explanation: 
+In the IT department:
+- Max earns the highest unique salary
+- Both Randy and Joe earn the second-highest unique salary
+- Will earns the third-highest unique salary
+
+In the Sales department:
+- Henry earns the highest salary
+- Sam earns the second-highest salary
+- There is no third-highest salary as there are only two employees
+ 
+
+Constraints:
+
+There are no employees with the exact same name, salary and department.
+
+
+## Solution
+
+```MySQL
+# Write your MySQL query statement below
+SELECT d.Name as Department,
+e.Name as Employee,
+e.Salary as Salary
+FROM Department d, Employee e
+WHERE(
+    SELECT COUNT(distinct Salary)
+    FROM Employee
+    WHERE Salary > e.Salary AND DepartmentId = d.Id
+) < 3 AND e.DepartmentId = d.Id
+ORDER BY d.Id, e.Salary desc;
+```
\ No newline at end of file
diff --git a/185. Department Top Three Salaries.sql b/185. Department Top Three Salaries.sql
new file mode 100644
index 0000000..7998e64
--- /dev/null
+++ b/185. Department Top Three Salaries.sql	
@@ -0,0 +1,208 @@
+# 185. Department Top Three Salaries
+
+### Question
+```
+ Table: Employee
+
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| id           | int     |
+| name         | varchar |
+| salary       | int     |
+| departmentId | int     |
++--------------+---------+
+```
+id is the primary key (column with unique values) for this table.
+departmentId is a foreign key (reference column) of the ID from the Department table.
+Each row of this table indicates the ID, name, and salary of an employee. It also contains the ID of their department.
+ 
+
+Table: Department
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| name        | varchar |
++-------------+---------+
+```
+id is the primary key (column with unique values) for this table.
+Each row of this table indicates the ID of a department and its name.
+ 
+
+A company's executives are interested in seeing who earns the most money in each of the company's departments. A high earner in a department is an employee who has a salary in the top three unique salaries for that department.
+
+Write a solution to find the employees who are high earners in each of the departments.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+```
+Input: 
+Employee table:
++----+-------+--------+--------------+
+| id | name  | salary | departmentId |
++----+-------+--------+--------------+
+| 1  | Joe   | 85000  | 1            |
+| 2  | Henry | 80000  | 2            |
+| 3  | Sam   | 60000  | 2            |
+| 4  | Max   | 90000  | 1            |
+| 5  | Janet | 69000  | 1            |
+| 6  | Randy | 85000  | 1            |
+| 7  | Will  | 70000  | 1            |
++----+-------+--------+--------------+
+```
+```
+Department table:
++----+-------+
+| id | name  |
++----+-------+
+| 1  | IT    |
+| 2  | Sales |
++----+-------+
+Output: 
++------------+----------+--------+
+| Department | Employee | Salary |
++------------+----------+--------+
+| IT         | Max      | 90000  |
+| IT         | Joe      | 85000  |
+| IT         | Randy    | 85000  |
+| IT         | Will     | 70000  |
+| Sales      | Henry    | 80000  |
+| Sales      | Sam      | 60000  |
++------------+----------+--------+
+```
+Explanation: 
+In the IT department:
+- Max earns the highest unique salary
+- Both Randy and Joe earn the second-highest unique salary
+- Will earns the third-highest unique salary
+
+In the Sales department:
+- Henry earns the highest salary
+- Sam earns the second-highest salary
+- There is no third-highest salary as there are only two employees
+ 
+
+Constraints:
+
+There are no employees with the exact same name, salary and department.
+
+
+## Solution
+
+```sql
+# 185. Department Top Three Salaries
+
+### Question
+```
+ Table: Employee
+
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| id           | int     |
+| name         | varchar |
+| salary       | int     |
+| departmentId | int     |
++--------------+---------+
+```
+id is the primary key (column with unique values) for this table.
+departmentId is a foreign key (reference column) of the ID from the Department table.
+Each row of this table indicates the ID, name, and salary of an employee. It also contains the ID of their department.
+ 
+
+Table: Department
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| name        | varchar |
++-------------+---------+
+```
+id is the primary key (column with unique values) for this table.
+Each row of this table indicates the ID of a department and its name.
+ 
+
+A company's executives are interested in seeing who earns the most money in each of the company's departments. A high earner in a department is an employee who has a salary in the top three unique salaries for that department.
+
+Write a solution to find the employees who are high earners in each of the departments.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+```
+Input: 
+Employee table:
++----+-------+--------+--------------+
+| id | name  | salary | departmentId |
++----+-------+--------+--------------+
+| 1  | Joe   | 85000  | 1            |
+| 2  | Henry | 80000  | 2            |
+| 3  | Sam   | 60000  | 2            |
+| 4  | Max   | 90000  | 1            |
+| 5  | Janet | 69000  | 1            |
+| 6  | Randy | 85000  | 1            |
+| 7  | Will  | 70000  | 1            |
++----+-------+--------+--------------+
+```
+```
+Department table:
++----+-------+
+| id | name  |
++----+-------+
+| 1  | IT    |
+| 2  | Sales |
++----+-------+
+Output: 
++------------+----------+--------+
+| Department | Employee | Salary |
++------------+----------+--------+
+| IT         | Max      | 90000  |
+| IT         | Joe      | 85000  |
+| IT         | Randy    | 85000  |
+| IT         | Will     | 70000  |
+| Sales      | Henry    | 80000  |
+| Sales      | Sam      | 60000  |
++------------+----------+--------+
+
+Explanation: 
+In the IT department:
+- Max earns the highest unique salary
+- Both Randy and Joe earn the second-highest unique salary
+- Will earns the third-highest unique salary
+
+In the Sales department:
+- Henry earns the highest salary
+- Sam earns the second-highest salary
+- There is no third-highest salary as there are only two employees
+ 
+
+Constraints:
+
+There are no employees with the exact same name, salary and department.
+
+
+## Solution
+
+# Write your MySQL query statement below
+SELECT d.Name as Department,
+e.Name as Employee,
+e.Salary as Salary
+FROM Department d, Employee e
+WHERE(
+    SELECT COUNT(distinct Salary)
+    FROM Employee
+    WHERE Salary > e.Salary AND DepartmentId = d.Id
+) < 3 AND e.DepartmentId = d.Id
+ORDER BY d.Id, e.Salary desc;
diff --git a/README.md b/README.md
index eff7bd4..0add1f5 100644
--- a/README.md
+++ b/README.md
@@ -1,26 +1,42 @@
 <img src="img/SQL.jpeg">
 
 # LeetCode_SQL_Database
-This repo contains my SQL solutions for LeetCode problems. Each file includes a problem description, optimized SQL query, and explanations. The goal is to improve SQL skills, share knowledge, and collaborate. Feel free to explore, suggest improvements, or contribute! 🚀
 
+This repo contains my SQL solutions for LeetCode problems. Each file includes a problem description, optimized SQL query, and explanations. The goal is to improve SQL skills, share knowledge, and collaborate. Feel free to explore, suggest improvements, or contribute! 🚀
 
 ## LeetCode SQL Solutions:-
+
 <img src="img/salution.png">
 This repository contains my solutions to SQL problems from LeetCode. Each solution is implemented using MySQL and aims to demonstrate efficient and optimal approaches to solve common database-related challenges.
+
 <img src="img/leetcodecode.jpeg">
+
 ## Repository Structure:-
+
 Problem Name: Each SQL file is named after the LeetCode problem it solves.
 Solution Explanation: Each SQL file includes comments explaining the logic and approach used to solve the problem.
 Testing: Sample test cases or scenarios may be included in the comments or README where applicable.
 Why This Repository?
+
 ## Learning and Practice:-
-This repository serves as a resource for learning SQL techniques for solving algorithmic problems.
-Collaboration: I welcome feedback and suggestions for improving solutions or alternative approaches.
+
+<img src="img\leetcode.png">
+
+**This repository serves as a resource for learning SQL techniques for solving algorithmic problems.**
+
+### Collaboration:-
+
+I welcome feedback and suggestions for improving solutions or alternative approaches.
 Community: By sharing these solutions, I hope to contribute to the SQL programming community and help others learn and grow.
-Contents:
+
+### Contents:-
+
 Problem 1: Description of the problem and the SQL solution.
 Problem 2: Description of the problem and the SQL solution.
 Problem 3: Description of the problem and the SQL solution.
-### Contributing:
-Feel free to fork this repository, suggest improvements, or submit your own solutions via pull requests. Let's learn and grow together!
 
+<img src="img\leetcode salution.png">
+
+### Contributing:-
+
+Feel free to fork this repository, suggest improvements, or submit your own solutions via pull requests. Let's learn and grow together!
diff --git a/img/leetcode salution.png b/img/leetcode salution.png
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From 8fcb036c9788b547380cc63a56759879711f68f6 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Tue, 11 Mar 2025 15:50:01 +0530
Subject: [PATCH 003/353] this is seeing

---
 README.md     |   2 +-
 img/work.jpeg | Bin 0 -> 7822 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 img/work.jpeg

diff --git a/README.md b/README.md
index 0add1f5..46d74eb 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ This repo contains my SQL solutions for LeetCode problems. Each file includes a
 <img src="img/salution.png">
 This repository contains my solutions to SQL problems from LeetCode. Each solution is implemented using MySQL and aims to demonstrate efficient and optimal approaches to solve common database-related challenges.
 
-<img src="img/leetcodecode.jpeg">
+<img src="img/work.jpeg">
 
 ## Repository Structure:-
 
diff --git a/img/work.jpeg b/img/work.jpeg
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From 4724e956c0f551b8470ca4ba6e3eb01ce255014e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Tue, 11 Mar 2025 17:00:08 +0530
Subject: [PATCH 004/353] this is the photo

---
 README.md             |   4 +++-
 img/leetcode me.png   | Bin 0 -> 267796 bytes
 img/leetcode-sql.png  | Bin 0 -> 15382 bytes
 img/leetcodecode.jpeg | Bin 5812 -> 0 bytes
 4 files changed, 3 insertions(+), 1 deletion(-)
 create mode 100644 img/leetcode me.png
 create mode 100644 img/leetcode-sql.png
 delete mode 100644 img/leetcodecode.jpeg

diff --git a/README.md b/README.md
index 46d74eb..ae09ded 100644
--- a/README.md
+++ b/README.md
@@ -4,9 +4,11 @@
 
 This repo contains my SQL solutions for LeetCode problems. Each file includes a problem description, optimized SQL query, and explanations. The goal is to improve SQL skills, share knowledge, and collaborate. Feel free to explore, suggest improvements, or contribute! 🚀
 
+<img src="img/leetcode me.png">
+
 ## LeetCode SQL Solutions:-
 
-<img src="img/salution.png">
+<img src="img/leetcode-sql.png">
 This repository contains my solutions to SQL problems from LeetCode. Each solution is implemented using MySQL and aims to demonstrate efficient and optimal approaches to solve common database-related challenges.
 
 <img src="img/work.jpeg">
diff --git a/img/leetcode me.png b/img/leetcode me.png
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From 4e49ce7d5ddbe18f0514162ea2cad725e331f8b6 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Tue, 11 Mar 2025 17:02:48 +0530
Subject: [PATCH 005/353] this is image

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index ae09ded..3fc30a1 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-<img src="img/SQL.jpeg">
+<center><img src="img/SQL.jpeg"></center>
 
 # LeetCode_SQL_Database
 
@@ -10,8 +10,9 @@ This repo contains my SQL solutions for LeetCode problems. Each file includes a
 
 <img src="img/leetcode-sql.png">
 This repository contains my solutions to SQL problems from LeetCode. Each solution is implemented using MySQL and aims to demonstrate efficient and optimal approaches to solve common database-related challenges.
-
+<center>
 <img src="img/work.jpeg">
+</center>
 
 ## Repository Structure:-
 

From 2485ca7d5f52541a4052f60c8b90e3ed9e626c27 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Wed, 12 Mar 2025 16:20:58 +0530
Subject: [PATCH 006/353] today i solve movie sql problem

---
 620. Not Boring Movies.sql | 56 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 620. Not Boring Movies.sql

diff --git a/620. Not Boring Movies.sql b/620. Not Boring Movies.sql
new file mode 100644
index 0000000..d194174
--- /dev/null
+++ b/620. Not Boring Movies.sql	
@@ -0,0 +1,56 @@
+620. Not Boring Movies
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Cinema
+
++----------------+----------+
+| Column Name    | Type     |
++----------------+----------+
+| id             | int      |
+| movie          | varchar  |
+| description    | varchar  |
+| rating         | float    |
++----------------+----------+
+id is the primary key (column with unique values) for this table.
+Each row contains information about the name of a movie, its genre, and its rating.
+rating is a 2 decimal places float in the range [0, 10]
+ 
+
+Write a solution to report the movies with an odd-numbered ID and a description that is not "boring".
+
+Return the result table ordered by rating in descending order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Cinema table:
++----+------------+-------------+--------+
+| id | movie      | description | rating |
++----+------------+-------------+--------+
+| 1  | War        | great 3D    | 8.9    |
+| 2  | Science    | fiction     | 8.5    |
+| 3  | irish      | boring      | 6.2    |
+| 4  | Ice song   | Fantacy     | 8.6    |
+| 5  | House card | Interesting | 9.1    |
++----+------------+-------------+--------+
+Output: 
++----+------------+-------------+--------+
+| id | movie      | description | rating |
++----+------------+-------------+--------+
+| 5  | House card | Interesting | 9.1    |
+| 1  | War        | great 3D    | 8.9    |
++----+------------+-------------+--------+
+Explanation: 
+We have three movies with odd-numbered IDs: 1, 3, and 5. The movie with ID = 3 is boring so we do not include it in the answer.
+
+We sort the remaining movies by rating in descending order.
+
+# Write your MySQL query statement below
+SELECT * FROM Cinema WHERE id % 2 = 1 AND description != 'boring' ORDER BY rating DESC

From eeeaa0a7ed97115fffaf4f34f77592093907418f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Wed, 12 Mar 2025 16:24:05 +0530
Subject: [PATCH 007/353] date:12-03-2025

---
 1251. Average Selling Price.sql | 84 +++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 1251. Average Selling Price.sql

diff --git a/1251. Average Selling Price.sql b/1251. Average Selling Price.sql
new file mode 100644
index 0000000..df09476
--- /dev/null
+++ b/1251. Average Selling Price.sql	
@@ -0,0 +1,84 @@
+1251. Average Selling Price
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Prices
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| product_id    | int     |
+| start_date    | date    |
+| end_date      | date    |
+| price         | int     |
++---------------+---------+
+(product_id, start_date, end_date) is the primary key (combination of columns with unique values) for this table.
+Each row of this table indicates the price of the product_id in the period from start_date to end_date.
+For each product_id there will be no two overlapping periods. That means there will be no two intersecting periods for the same product_id.
+ 
+
+Table: UnitsSold
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| product_id    | int     |
+| purchase_date | date    |
+| units         | int     |
++---------------+---------+
+This table may contain duplicate rows.
+Each row of this table indicates the date, units, and product_id of each product sold. 
+ 
+
+Write a solution to find the average selling price for each product. average_price should be rounded to 2 decimal places. If a product does not have any sold units, its average selling price is assumed to be 0.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Prices table:
++------------+------------+------------+--------+
+| product_id | start_date | end_date   | price  |
++------------+------------+------------+--------+
+| 1          | 2019-02-17 | 2019-02-28 | 5      |
+| 1          | 2019-03-01 | 2019-03-22 | 20     |
+| 2          | 2019-02-01 | 2019-02-20 | 15     |
+| 2          | 2019-02-21 | 2019-03-31 | 30     |
++------------+------------+------------+--------+
+UnitsSold table:
++------------+---------------+-------+
+| product_id | purchase_date | units |
++------------+---------------+-------+
+| 1          | 2019-02-25    | 100   |
+| 1          | 2019-03-01    | 15    |
+| 2          | 2019-02-10    | 200   |
+| 2          | 2019-03-22    | 30    |
++------------+---------------+-------+
+Output: 
++------------+---------------+
+| product_id | average_price |
++------------+---------------+
+| 1          | 6.96          |
+| 2          | 16.96         |
++------------+---------------+
+Explanation: 
+Average selling price = Total Price of Product / Number of products sold.
+Average selling price for product 1 = ((100 * 5) + (15 * 20)) / 115 = 6.96
+Average selling price for product 2 = ((200 * 15) + (30 * 30)) / 230 = 16.96
+Note that the average selling price is 0 if the product was not sold.
+
+# Write your MySQL query statement below
+SELECT a.product_id,ROUND(SUM(b.units*a.price)/SUM(b.units),2) as average_price
+FROM Prices as a
+JOIN UnitsSold as b
+ON a.product_id=b.product_id AND (b.purchase_date BETWEEN a.start_date AND a.end_date)
+GROUP BY product_id;
+
+-- SELECT product_id, ROUND(AVG(COALESCE(price, 0)), 2) AS average_price
\ No newline at end of file

From c0890ffd073dd4634a767ae20bc9ba6110f20815 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Thu, 13 Mar 2025 22:25:58 +0530
Subject: [PATCH 008/353] adding mysql file prosnol to add language

---
 prosnal_database.sql | 104 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 prosnal_database.sql

diff --git a/prosnal_database.sql b/prosnal_database.sql
new file mode 100644
index 0000000..4e75df7
--- /dev/null
+++ b/prosnal_database.sql
@@ -0,0 +1,104 @@
+CREATE DATABASE IF NOT EXISTS prosnal_database;
+USE prosnal_database;
+
+CREATE TABLE IF NOT EXISTS Employees (
+    Id INT,
+    Name VARCHAR(255),
+    Salary INT,
+    Department VARCHAR(255)
+);
+
+INSERT INTO Employees (Id, Name, Salary, Department)
+VALUES (1, 'John Doe', 50000, 'HR'),
+       (2, 'Jane Smith', 60000, 'Finance'),
+       (3, 'Mike Johnson', 70000, 'IT'),
+           (4, 'Sarah Black', 60000, 'Finance'),
+           (5, 'David White', 70000, 'IT');
+           
+CREATE TABLE IF NOT EXISTS Projects (
+     Id INT,
+     Name VARCHAR(255),
+     Department VARCHAR(255)
+     );
+     
+ INSERT INTO Projects (Id, Name, Department)
+     VALUES (1, 'Project A', 'IT'),
+          (2, 'Project B', 'Finance'),
+          (3, 'Project C', 'IT');
+          
+CREATE TABLE IF NOT EXISTS EmployeeProjects (
+     EmployeeId INT,
+     ProjectId INT
+     );
+     
+ INSERT INTO EmployeeProjects (EmployeeId, ProjectId)
+     VALUES (1, 1),
+          (1, 2),
+          (2, 1),
+          (3, 1),
+          (3, 3),
+          (4, 2),
+          (5, 1);
+          
+SELECT e.Name AS EmployeeName, e.Department, p.Name AS ProjectName, p.Department
+FROM Employees e
+JOIN EmployeeProjects ep
+ON e.Id = ep.EmployeeId
+JOIN Projects p
+ON ep.ProjectId = p.Id;
+
+CREATE TABLE users (
+    id INT PRIMARY KEY,
+    name VARCHAR(50),
+    email VARCHAR(100)
+);
+
+INSERT INTO users (id, name, email)
+VALUES (1, 'John Doe', 'john.doe@example.com'),
+       (2, 'Jane Smith', 'antima@example.com');
+
+CREATE TABLE sessions (
+    id INT PRIMARY KEY,
+    user_id INT,
+    session_date DATE
+);
+
+INSERT INTO sessions (id, user_id, session_date)
+VALUES (1, 1, '2022-01-01'),
+       (2, 1, '2022-01-02'),
+       (3, 2, '2022-01-03');
+
+SELECT u.name AS user_name, s.session_date
+FROM users u
+JOIN sessions s
+ON u.id = s.user_id;
+
+CREATE TABLE IF NOT EXISTS products (
+    id INT PRIMARY KEY,
+    name VARCHAR(50),
+    price DECIMAL(10, 2)
+);
+
+INSERT INTO products (id, name, price)
+VALUES (1, 'Product A', 10.99),
+           (2, 'Product B', 20.99),
+           (3, 'Product C', 30.99);
+           
+CREATE TABLE IF NOT EXISTS orders (
+    id INT PRIMARY KEY,
+    product_id INT,
+    quantity INT
+);
+
+INSERT INTO orders (id, product_id, quantity)
+VALUES (1, 1, 5),
+       (2, 2, 3),
+       (3, 3, 1),
+       (4, 1, 2),
+       (5, 2, 4);
+       
+SELECT p.name AS product_name, o.quantity, p.price * o.quantity AS total_price
+FROM products p
+JOIN orders o
+ON p.id = o.product_id;
+```

From 64243dc658bf300b9317510b33e94a77522560aa Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Thu, 13 Mar 2025 22:31:58 +0530
Subject: [PATCH 009/353] Force GitHub to detect SQL

---
 .gitattributes | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 .gitattributes

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..82dabcb
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,2 @@
+*.sql linguist-detectable=true
+*.sql linguist-language=SQL

From 3cfededd9cc5c503803ff6a1339e5d2d418054ff Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Thu, 13 Mar 2025 22:33:03 +0530
Subject: [PATCH 010/353] Trigger GitHub language update


From 67d2b43ee92ddfa1f015e52675c812e217b95c71 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Thu, 13 Mar 2025 23:02:03 +0530
Subject: [PATCH 011/353] today Sql work

---
 .gitattributes                  |  5 ++
 1075. Project Employees I.sql   | 82 +++++++++++++++++++++++++++++++++
 1251. Average Selling Price.sql | 12 +++--
 3 files changed, 94 insertions(+), 5 deletions(-)
 create mode 100644 1075. Project Employees I.sql

diff --git a/.gitattributes b/.gitattributes
index 82dabcb..8e05635 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,2 +1,7 @@
 *.sql linguist-detectable=true
 *.sql linguist-language=SQL
+
+
+*.md linguist-vendored
+*.json linguist-vendored
+*.py linguist-vendored
diff --git a/1075. Project Employees I.sql b/1075. Project Employees I.sql
new file mode 100644
index 0000000..385a71d
--- /dev/null
+++ b/1075. Project Employees I.sql	
@@ -0,0 +1,82 @@
+1075. Project Employees I
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Project
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| project_id  | int     |
+| employee_id | int     |
++-------------+---------+
+(project_id, employee_id) is the primary key of this table.
+employee_id is a foreign key to Employee table.
+Each row of this table indicates that the employee with employee_id is working on the project with project_id.
+ 
+
+Table: Employee
+
++------------------+---------+
+| Column Name      | Type    |
++------------------+---------+
+| employee_id      | int     |
+| name             | varchar |
+| experience_years | int     |
++------------------+---------+
+employee_id is the primary key of this table. It's guaranteed that experience_years is not NULL.
+Each row of this table contains information about one employee.
+ 
+
+Write an SQL query that reports the average experience years of all the employees for each project, rounded to 2 digits.
+
+Return the result table in any order.
+
+The query result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Project table:
++-------------+-------------+
+| project_id  | employee_id |
++-------------+-------------+
+| 1           | 1           |
+| 1           | 2           |
+| 1           | 3           |
+| 2           | 1           |
+| 2           | 4           |
++-------------+-------------+
+Employee table:
++-------------+--------+------------------+
+| employee_id | name   | experience_years |
++-------------+--------+------------------+
+| 1           | Khaled | 3                |
+| 2           | Ali    | 2                |
+| 3           | John   | 1                |
+| 4           | Doe    | 2                |
++-------------+--------+------------------+
+Output: 
++-------------+---------------+
+| project_id  | average_years |
++-------------+---------------+
+| 1           | 2.00          |
+| 2           | 2.50          |
++-------------+---------------+
+Explanation: The average experience years for the first project is (3 + 2 + 1) / 3 = 2.00 and for the second project is (3 + 2) / 2 = 2.50
+
+'''SQL
+
+# Write your MySQL query statement below
+select project_id , round(avg(experience_years), 2) as average_years
+from project as p
+left join employee as e
+on p.employee_id = e.employee_id
+group by project_id;
+
+'''
\ No newline at end of file
diff --git a/1251. Average Selling Price.sql b/1251. Average Selling Price.sql
index df09476..82eb691 100644
--- a/1251. Average Selling Price.sql	
+++ b/1251. Average Selling Price.sql	
@@ -75,10 +75,12 @@ Average selling price for product 2 = ((200 * 15) + (30 * 30)) / 230 = 16.96
 Note that the average selling price is 0 if the product was not sold.
 
 # Write your MySQL query statement below
-SELECT a.product_id,ROUND(SUM(b.units*a.price)/SUM(b.units),2) as average_price
-FROM Prices as a
-JOIN UnitsSold as b
-ON a.product_id=b.product_id AND (b.purchase_date BETWEEN a.start_date AND a.end_date)
-GROUP BY product_id;
 
+# Write your MySQL query statement below
+SELECT p.product_id, IFNULL(round(SUM(p.price*u.units)/sum(u.units),2),0) as average_price
+FROM Prices p 
+LEFT JOIN UnitsSold u
+ON p.product_id = u.product_id AND 
+u.purchase_date BETWEEN p.Start_date and p.end_date
+GROUP BY p.product_id
 -- SELECT product_id, ROUND(AVG(COALESCE(price, 0)), 2) AS average_price
\ No newline at end of file

From edfbe7fb80310fdb79f6cb4560119eec7d4f771f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Thu, 13 Mar 2025 23:17:35 +0530
Subject: [PATCH 012/353] ok=

---
 ...Percentage of Users Attended a Contest.sql | 84 +++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 1633. Percentage of Users Attended a Contest.sql

diff --git a/1633. Percentage of Users Attended a Contest.sql b/1633. Percentage of Users Attended a Contest.sql
new file mode 100644
index 0000000..86ac117
--- /dev/null
+++ b/1633. Percentage of Users Attended a Contest.sql	
@@ -0,0 +1,84 @@
+1633. Percentage of Users Attended a Contest
+
+Table: Users
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| user_id     | int     |
+| user_name   | varchar |
++-------------+---------+
+user_id is the primary key (column with unique values) for this table.
+Each row of this table contains the name and the id of a user.
+ 
+
+Table: Register
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| contest_id  | int     |
+| user_id     | int     |
++-------------+---------+
+(contest_id, user_id) is the primary key (combination of columns with unique values) for this table.
+Each row of this table contains the id of a user and the contest they registered into.
+ 
+
+Write a solution to find the percentage of the users registered in each contest rounded to two decimals.
+
+Return the result table ordered by percentage in descending order. In case of a tie, order it by contest_id in ascending order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Users table:
++---------+-----------+
+| user_id | user_name |
++---------+-----------+
+| 6       | Alice     |
+| 2       | Bob       |
+| 7       | Alex      |
++---------+-----------+
+Register table:
++------------+---------+
+| contest_id | user_id |
++------------+---------+
+| 215        | 6       |
+| 209        | 2       |
+| 208        | 2       |
+| 210        | 6       |
+| 208        | 6       |
+| 209        | 7       |
+| 209        | 6       |
+| 215        | 7       |
+| 208        | 7       |
+| 210        | 2       |
+| 207        | 2       |
+| 210        | 7       |
++------------+---------+
+Output: 
++------------+------------+
+| contest_id | percentage |
++------------+------------+
+| 208        | 100.0      |
+| 209        | 100.0      |
+| 210        | 100.0      |
+| 215        | 66.67      |
+| 207        | 33.33      |
++------------+------------+
+Explanation: 
+All the users registered in contests 208, 209, and 210. The percentage is 100% and we sort them in the answer table by contest_id in ascending order.
+Alice and Alex registered in contest 215 and the percentage is ((2/3) * 100) = 66.67%
+Bob registered in contest 207 and the percentage is ((1/3) * 100) = 33.33%
+
+
+# Write your MySQL query statement below
+
+SELECT contest_id, ROUND((COUNT(user_id) / (SELECT COUNT(*) FROM Users) * 100), 2) AS percentage
+FROM Register
+GROUP BY contest_id
+ORDER BY percentage DESC, contest_id ASC;
\ No newline at end of file

From 68f7131bb9a901302a655e699da5b64dd7bcd439 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 03:09:42 +0530
Subject: [PATCH 013/353] Create 1211. Queries Quality and Percentage.sql

this is today task of sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1211. Queries Quality and Percentage.sql | 71 ++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 1211. Queries Quality and Percentage.sql

diff --git a/1211. Queries Quality and Percentage.sql b/1211. Queries Quality and Percentage.sql
new file mode 100644
index 0000000..b900382
--- /dev/null
+++ b/1211. Queries Quality and Percentage.sql	
@@ -0,0 +1,71 @@
+1211. Queries Quality and Percentage
+
+Table: Queries
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| query_name  | varchar |
+| result      | varchar |
+| position    | int     |
+| rating      | int     |
++-------------+---------+
+This table may have duplicate rows.
+This table contains information collected from some queries on a database.
+The position column has a value from 1 to 500.
+The rating column has a value from 1 to 5. Query with rating less than 3 is a poor query.
+ 
+
+We define query quality as:
+
+The average of the ratio between query rating and its position.
+
+We also define poor query percentage as:
+
+The percentage of all queries with rating less than 3.
+
+Write a solution to find each query_name, the quality and poor_query_percentage.
+
+Both quality and poor_query_percentage should be rounded to 2 decimal places.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Queries table:
++------------+-------------------+----------+--------+
+| query_name | result            | position | rating |
++------------+-------------------+----------+--------+
+| Dog        | Golden Retriever  | 1        | 5      |
+| Dog        | German Shepherd   | 2        | 5      |
+| Dog        | Mule              | 200      | 1      |
+| Cat        | Shirazi           | 5        | 2      |
+| Cat        | Siamese           | 3        | 3      |
+| Cat        | Sphynx            | 7        | 4      |
++------------+-------------------+----------+--------+
+Output: 
++------------+---------+-----------------------+
+| query_name | quality | poor_query_percentage |
++------------+---------+-----------------------+
+| Dog        | 2.50    | 33.33                 |
+| Cat        | 0.66    | 33.33                 |
++------------+---------+-----------------------+
+Explanation: 
+Dog queries quality is ((5 / 1) + (5 / 2) + (1 / 200)) / 3 = 2.50
+Dog queries poor_ query_percentage is (1 / 3) * 100 = 33.33
+
+Cat queries quality equals ((2 / 5) + (3 / 3) + (4 / 7)) / 3 = 0.66
+Cat queries poor_ query_percentage is (1 / 3) * 100 = 33.33
+
+
+# Write your MySQL query statement below
+SELECT query_name, 
+ROUND(AVG(rating * 1.0 / position), 2) AS quality,
+ROUND(SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS poor_query_percentage
+FROM Queries
+GROUP BY query_name;
\ No newline at end of file

From c9e2837152f13080d4c8b1e8a019fd991e29e7e9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 03:17:39 +0530
Subject: [PATCH 014/353] Create contributing.md

Signed-off-by: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
---
 contributing.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 contributing.md

diff --git a/contributing.md b/contributing.md
new file mode 100644
index 0000000..65c67f9
--- /dev/null
+++ b/contributing.md
@@ -0,0 +1 @@
+# Contribute everyone

From 024363e66d299904d5c3686468132be12c6498dd Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 03:44:43 +0530
Subject: [PATCH 015/353] Update 1211. Queries Quality and Percentage.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1211. Queries Quality and Percentage.sql | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/1211. Queries Quality and Percentage.sql b/1211. Queries Quality and Percentage.sql
index b900382..cc299ea 100644
--- a/1211. Queries Quality and Percentage.sql	
+++ b/1211. Queries Quality and Percentage.sql	
@@ -68,4 +68,10 @@ SELECT query_name,
 ROUND(AVG(rating * 1.0 / position), 2) AS quality,
 ROUND(SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS poor_query_percentage
 FROM Queries
-GROUP BY query_name;
\ No newline at end of file
+GROUP BY query_name;
+
+-- SELECT query_name,
+-- ROUND(AVG(rating * 1.0 / position), 2) AS quality,
+-- ROUND(SUM(IF(rating < 3, 1, 0)) * 100.0 / COUNT(*), 2) AS poor_query_percentage  
+-- FROM Queries
+-- GROUP BY query_name;
\ No newline at end of file

From aa075d2d3b3eb7a7bfd4a1365cb84d802b7930d1 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 16:47:58 +0530
Subject: [PATCH 016/353] yes this is to add today task

yes this is to add today task

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1193. Monthly Transactions I.sql | 49 ++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 1193. Monthly Transactions I.sql

diff --git a/1193. Monthly Transactions I.sql b/1193. Monthly Transactions I.sql
new file mode 100644
index 0000000..293451f
--- /dev/null
+++ b/1193. Monthly Transactions I.sql	
@@ -0,0 +1,49 @@
+1193. Monthly Transactions I
+
+Table: Transactions
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| id            | int     |
+| country       | varchar |
+| state         | enum    |
+| amount        | int     |
+| trans_date    | date    |
++---------------+---------+
+id is the primary key of this table.
+The table has information about incoming transactions.
+The state column is an enum of type ["approved", "declined"].
+ 
+
+Write an SQL query to find for each month and country, the number of transactions and their total amount, the number of approved transactions and their total amount.
+
+Return the result table in any order.
+
+The query result format is in the following example.
+
+ 
+
+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                  |
++----------+---------+-------------+----------------+--------------------+-----------------------+
+
+
+# Write your MySQL query statement below
\ No newline at end of file

From 4965b5ded0db5cb97c7470e036588fd8a3fa3334 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 16:54:54 +0530
Subject: [PATCH 017/353] this is some more question of sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1174. Immediate Food Delivery II.sql | 72 ++++++++++++++++++++++++++++
 550. Game Play Analysis IV.sql       | 45 +++++++++++++++++
 2 files changed, 117 insertions(+)
 create mode 100644 1174. Immediate Food Delivery II.sql
 create mode 100644 550. Game Play Analysis IV.sql

diff --git a/1174. Immediate Food Delivery II.sql b/1174. Immediate Food Delivery II.sql
new file mode 100644
index 0000000..f7c9563
--- /dev/null
+++ b/1174. Immediate Food Delivery II.sql	
@@ -0,0 +1,72 @@
+1174. Immediate Food Delivery II
+Medium
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Delivery
+
++-----------------------------+---------+
+| Column Name                 | Type    |
++-----------------------------+---------+
+| delivery_id                 | int     |
+| customer_id                 | int     |
+| order_date                  | date    |
+| customer_pref_delivery_date | date    |
++-----------------------------+---------+
+delivery_id is the column of unique values of this table.
+The table holds information about food delivery to customers that make orders at some date and specify a preferred delivery date (on the same order date or after it).
+ 
+
+If the customer's preferred delivery date is the same as the order date, then the order is called immediate; otherwise, it is called scheduled.
+
+The first order of a customer is the order with the earliest order date that the customer made. It is guaranteed that a customer has precisely one first order.
+
+Write a solution to find the percentage of immediate orders in the first orders of all customers, rounded to 2 decimal places.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Delivery table:
++-------------+-------------+------------+-----------------------------+
+| delivery_id | customer_id | order_date | customer_pref_delivery_date |
++-------------+-------------+------------+-----------------------------+
+| 1           | 1           | 2019-08-01 | 2019-08-02                  |
+| 2           | 2           | 2019-08-02 | 2019-08-02                  |
+| 3           | 1           | 2019-08-11 | 2019-08-12                  |
+| 4           | 3           | 2019-08-24 | 2019-08-24                  |
+| 5           | 3           | 2019-08-21 | 2019-08-22                  |
+| 6           | 2           | 2019-08-11 | 2019-08-13                  |
+| 7           | 4           | 2019-08-09 | 2019-08-09                  |
++-------------+-------------+------------+-----------------------------+
+Output: 
++----------------------+
+| immediate_percentage |
++----------------------+
+| 50.00                |
++----------------------+
+Explanation: 
+The customer id 1 has a first order with delivery id 1 and it is scheduled.
+The customer id 2 has a first order with delivery id 2 and it is immediate.
+The customer id 3 has a first order with delivery id 5 and it is scheduled.
+The customer id 4 has a first order with delivery id 7 and it is immediate.
+Hence, half the customers have immediate first orders.
+
+
+
+# Write your MySQL query statement below
+'''
+
+
+select 
+round(100*sum(case when b.min_order_date = b.min_delivery_date then 1 else 0 end)/count(*), 2)
+as immediate_percentage
+from (
+  select min(order_date) as min_order_date, min(customer_pref_delivery_date) as min_delivery_date
+  from delivery
+  group by customer_id
+) b;
\ No newline at end of file
diff --git a/550. Game Play Analysis IV.sql b/550. Game Play Analysis IV.sql
new file mode 100644
index 0000000..a33c9f8
--- /dev/null
+++ b/550. Game Play Analysis IV.sql	
@@ -0,0 +1,45 @@
+550. Game Play Analysis IV
+
+Table: Activity
+
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| player_id    | int     |
+| device_id    | int     |
+| event_date   | date    |
+| games_played | int     |
++--------------+---------+
+(player_id, event_date) is the primary key (combination of columns with unique values) of this table.
+This table shows the activity of players of some games.
+Each row is a record of a player who logged in and played a number of games (possibly 0) before logging out on someday using some device.
+ 
+
+Write a solution to report the fraction of players that logged in again on the day after the day they first logged in, rounded to 2 decimal places. In other words, you need to count the number of players that logged in for at least two consecutive days starting from their first login date, then divide that number by the total number of players.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Activity table:
++-----------+-----------+------------+--------------+
+| player_id | device_id | event_date | games_played |
++-----------+-----------+------------+--------------+
+| 1         | 2         | 2016-03-01 | 5            |
+| 1         | 2         | 2016-03-02 | 6            |
+| 2         | 3         | 2017-06-25 | 1            |
+| 3         | 1         | 2016-03-02 | 0            |
+| 3         | 4         | 2018-07-03 | 5            |
++-----------+-----------+------------+--------------+
+Output: 
++-----------+
+| fraction  |
++-----------+
+| 0.33      |
++-----------+
+Explanation: 
+Only the player with id 1 logged back in after the first day he had logged in so the answer is 1/3 = 0.33
+

From 0d52857bca1eee2e1d1def644b5be58adf7436e7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 16:57:47 +0530
Subject: [PATCH 018/353] solution

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 550. Game Play Analysis IV.sql | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/550. Game Play Analysis IV.sql b/550. Game Play Analysis IV.sql
index a33c9f8..dda489d 100644
--- a/550. Game Play Analysis IV.sql	
+++ b/550. Game Play Analysis IV.sql	
@@ -43,3 +43,13 @@ Output:
 Explanation: 
 Only the player with id 1 logged back in after the first day he had logged in so the answer is 1/3 = 0.33
 
+# Write your MySQL query statement below
+select 
+round((select count(distinct a.player_id) from Activity a
+inner join 
+(select player_id, min(event_date) as first_logged 
+    from Activity 
+    group by player_id) b on datediff(a.event_date, b.first_logged)=1
+    and a.player_id = b.player_id)
+    /
+    (select count(distinct player_id) from Activity),2) as fraction;
\ No newline at end of file

From fd54abf986f342d6acbc0529726818e83c53aad7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 17:01:52 +0530
Subject: [PATCH 019/353] problem no 1141

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .... User Activity for the Past 30 Days I.sql | 67 +++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 1141. User Activity for the Past 30 Days I.sql

diff --git a/1141. User Activity for the Past 30 Days I.sql b/1141. User Activity for the Past 30 Days I.sql
new file mode 100644
index 0000000..e3a7bda
--- /dev/null
+++ b/1141. User Activity for the Past 30 Days I.sql	
@@ -0,0 +1,67 @@
+1141. User Activity for the Past 30 Days I
+
+Table: Activity
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| user_id       | int     |
+| session_id    | int     |
+| activity_date | date    |
+| activity_type | enum    |
++---------------+---------+
+This table may have duplicate rows.
+The activity_type column is an ENUM (category) of type ('open_session', 'end_session', 'scroll_down', 'send_message').
+The table shows the user activities for a social media website. 
+Note that each session belongs to exactly one user.
+ 
+
+Write a solution to find the daily active user count for a period of 30 days ending 2019-07-27 inclusively. A user was active on someday if they made at least one activity on that day.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Activity table:
++---------+------------+---------------+---------------+
+| user_id | session_id | activity_date | activity_type |
++---------+------------+---------------+---------------+
+| 1       | 1          | 2019-07-20    | open_session  |
+| 1       | 1          | 2019-07-20    | scroll_down   |
+| 1       | 1          | 2019-07-20    | end_session   |
+| 2       | 4          | 2019-07-20    | open_session  |
+| 2       | 4          | 2019-07-21    | send_message  |
+| 2       | 4          | 2019-07-21    | end_session   |
+| 3       | 2          | 2019-07-21    | open_session  |
+| 3       | 2          | 2019-07-21    | send_message  |
+| 3       | 2          | 2019-07-21    | end_session   |
+| 4       | 3          | 2019-06-25    | open_session  |
+| 4       | 3          | 2019-06-25    | end_session   |
++---------+------------+---------------+---------------+
+Output: 
++------------+--------------+ 
+| day        | active_users |
++------------+--------------+ 
+| 2019-07-20 | 2            |
+| 2019-07-21 | 2            |
++------------+--------------+ 
+Explanation: Note that we do not care about days with zero active users.
+
+# 1141. User Activity for the Past 30 Days I
+
+# Find the daily active user count for a period of 30 days ending 2019-07-27 inclusively. A user was active on some day if he/she made at least one activity on that day.
+# Note that we do not care about days with zero active users
+
+SELECT 
+    activity_date AS day, 
+    COUNT(DISTINCT user_id) AS active_users
+FROM 
+    Activity
+WHERE 
+    DATEDIFF('2019-07-27', activity_date) < 30 AND DATEDIFF('2019-07-27', activity_date)>=0
+GROUP BY 1
\ No newline at end of file

From e4d5d1d719c5d374bc14f1c898e893009b5cddea Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 17:08:01 +0530
Subject: [PATCH 020/353] Create 2356. Number of Unique Subjects Taught by Each
 Teacher.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 ...Unique Subjects Taught by Each Teacher.sql | 61 +++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 2356. Number of Unique Subjects Taught by Each Teacher.sql

diff --git a/2356. Number of Unique Subjects Taught by Each Teacher.sql b/2356. Number of Unique Subjects Taught by Each Teacher.sql
new file mode 100644
index 0000000..16e090a
--- /dev/null
+++ b/2356. Number of Unique Subjects Taught by Each Teacher.sql	
@@ -0,0 +1,61 @@
+2356. Number of Unique Subjects Taught by Each Teacher
+
+Table: Teacher
+
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| teacher_id  | int  |
+| subject_id  | int  |
+| dept_id     | int  |
++-------------+------+
+(subject_id, dept_id) is the primary key (combinations of columns with unique values) of this table.
+Each row in this table indicates that the teacher with teacher_id teaches the subject subject_id in the department dept_id.
+ 
+
+Write a solution to calculate the number of unique subjects each teacher teaches in the university.
+
+Return the result table in any order.
+
+The result format is shown in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Teacher table:
++------------+------------+---------+
+| teacher_id | subject_id | dept_id |
++------------+------------+---------+
+| 1          | 2          | 3       |
+| 1          | 2          | 4       |
+| 1          | 3          | 3       |
+| 2          | 1          | 1       |
+| 2          | 2          | 1       |
+| 2          | 3          | 1       |
+| 2          | 4          | 1       |
++------------+------------+---------+
+Output:  
++------------+-----+
+| teacher_id | cnt |
++------------+-----+
+| 1          | 2   |
+| 2          | 4   |
++------------+-----+
+Explanation: 
+Teacher 1:
+  - They teach subject 2 in departments 3 and 4.
+  - They teach subject 3 in department 3.
+Teacher 2:
+  - They teach subject 1 in department 1.
+  - They teach subject 2 in department 1.
+  - They teach subject 3 in department 1.
+  - They teach subject 4 in department 1.
+
+
+  # Write your MySQL query statement below
+SELECT teacher_id, COUNT(DISTINCT subject_id) AS cnt
+FROM Teacher
+GROUP BY 1;
+

From 1a6981fd22ea1afcf8b3390f57e954c70e7a7660 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 17:15:19 +0530
Subject: [PATCH 021/353] Create 1070. Product Sales Analysis III.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1070. Product Sales Analysis III.sql | 90 ++++++++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 1070. Product Sales Analysis III.sql

diff --git a/1070. Product Sales Analysis III.sql b/1070. Product Sales Analysis III.sql
new file mode 100644
index 0000000..aac9538
--- /dev/null
+++ b/1070. Product Sales Analysis III.sql	
@@ -0,0 +1,90 @@
+1070. Product Sales Analysis III
+Medium
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Sales
+
++-------------+-------+
+| Column Name | Type  |
++-------------+-------+
+| sale_id     | int   |
+| product_id  | int   |
+| year        | int   |
+| quantity    | int   |
+| price       | int   |
++-------------+-------+
+(sale_id, year) is the primary key (combination of columns with unique values) of this table.
+product_id is a foreign key (reference column) to Product table.
+Each row of this table shows a sale on the product product_id in a certain year.
+Note that the price is per unit.
+ 
+
+Table: Product
+
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| product_id   | int     |
+| product_name | varchar |
++--------------+---------+
+product_id is the primary key (column with unique values) of this table.
+Each row of this table indicates the product name of each product.
+ 
+
+Write a solution to select the product id, year, quantity, and price for the first year of every product sold.
+
+Return the resulting table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Sales table:
++---------+------------+------+----------+-------+
+| sale_id | product_id | year | quantity | price |
++---------+------------+------+----------+-------+ 
+| 1       | 100        | 2008 | 10       | 5000  |
+| 2       | 100        | 2009 | 12       | 5000  |
+| 7       | 200        | 2011 | 15       | 9000  |
++---------+------------+------+----------+-------+
+Product table:
++------------+--------------+
+| product_id | product_name |
++------------+--------------+
+| 100        | Nokia        |
+| 200        | Apple        |
+| 300        | Samsung      |
++------------+--------------+
+Output: 
++------------+------------+----------+-------+
+| product_id | first_year | quantity | price |
++------------+------------+----------+-------+ 
+| 100        | 2008       | 10       | 5000  |
+| 200        | 2011       | 15       | 9000  |
++------------+------------+----------+-------+
+
+
+
+# Write your MySQL query statement below
+SELECT 
+  product_id, 
+  year AS first_year, 
+  quantity, 
+  price 
+FROM 
+  Sales 
+WHERE 
+  (product_id, year) IN (
+    SELECT 
+      product_id, 
+      MIN(year) AS year 
+    FROM 
+      Sales 
+    GROUP BY 
+      product_id
+  );
\ No newline at end of file

From 6527da530b04b15163a7a86fcc8a4abac36d01ee Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 17:18:46 +0530
Subject: [PATCH 022/353] 596. Classes More Than 5 Students

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1070. Product Sales Analysis III.sql  |  7 +---
 596. Classes More Than 5 Students.sql | 55 +++++++++++++++++++++++++++
 2 files changed, 56 insertions(+), 6 deletions(-)
 create mode 100644 596. Classes More Than 5 Students.sql

diff --git a/1070. Product Sales Analysis III.sql b/1070. Product Sales Analysis III.sql
index aac9538..f284841 100644
--- a/1070. Product Sales Analysis III.sql	
+++ b/1070. Product Sales Analysis III.sql	
@@ -1,10 +1,5 @@
 1070. Product Sales Analysis III
-Medium
-Topics
-Companies
-SQL Schema
-Pandas Schema
-Table: Sales
+
 
 +-------------+-------+
 | Column Name | Type  |
diff --git a/596. Classes More Than 5 Students.sql b/596. Classes More Than 5 Students.sql
new file mode 100644
index 0000000..9a61ac4
--- /dev/null
+++ b/596. Classes More Than 5 Students.sql	
@@ -0,0 +1,55 @@
+596. Classes More Than 5 Students
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| student     | varchar |
+| class       | varchar |
++-------------+---------+
+(student, class) is the primary key (combination of columns with unique values) for this table.
+Each row of this table indicates the name of a student and the class in which they are enrolled.
+ 
+
+Write a solution to find all the classes that have at least five students.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Courses table:
++---------+----------+
+| student | class    |
++---------+----------+
+| A       | Math     |
+| B       | English  |
+| C       | Math     |
+| D       | Biology  |
+| E       | Math     |
+| F       | Computer |
+| G       | Math     |
+| H       | Math     |
+| I       | Math     |
++---------+----------+
+Output: 
++---------+
+| class   |
++---------+
+| Math    |
++---------+
+Explanation: 
+- Math has 6 students, so we include it.
+- English has 1 student, so we do not include it.
+- Biology has 1 student, so we do not include it.
+- Computer has 1 student, so we do not include it.
+
+
+# Write your MySQL query statement below
+SELECT class
+FROM Courses
+GROUP BY class
+HAVING COUNT(student) >= 5;
\ No newline at end of file

From c405f85e7f038157057977a41d0a95758a7bfcc4 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 17:24:41 +0530
Subject: [PATCH 023/353] Create 1729. Find Followers Count.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1729. Find Followers Count.sql | 51 ++++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 1729. Find Followers Count.sql

diff --git a/1729. Find Followers Count.sql b/1729. Find Followers Count.sql
new file mode 100644
index 0000000..79fca7a
--- /dev/null
+++ b/1729. Find Followers Count.sql	
@@ -0,0 +1,51 @@
+1729. Find Followers Count
+
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| user_id     | int  |
+| follower_id | int  |
++-------------+------+
+(user_id, follower_id) is the primary key (combination of columns with unique values) for this table.
+This table contains the IDs of a user and a follower in a social media app where the follower follows the user.
+ 
+
+Write a solution that will, for each user, return the number of followers.
+
+Return the result table ordered by user_id in ascending order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Followers table:
++---------+-------------+
+| user_id | follower_id |
++---------+-------------+
+| 0       | 1           |
+| 1       | 0           |
+| 2       | 0           |
+| 2       | 1           |
++---------+-------------+
+Output: 
++---------+----------------+
+| user_id | followers_count|
++---------+----------------+
+| 0       | 1              |
+| 1       | 1              |
+| 2       | 2              |
++---------+----------------+
+Explanation: 
+The followers of 0 are {1}
+The followers of 1 are {0}
+The followers of 2 are {0,1}
+
+
+# Write your MySQL query statement below
+SELECT user_id, COUNT(follower_id) AS followers_count
+FROM Followers
+GROUP BY user_id
+ORDER BY user_id;
\ No newline at end of file

From 129da038b776156c76077d8b9b40387b2f5a69df Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 17:28:00 +0530
Subject: [PATCH 024/353] Create 619. Biggest Single Number.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 619. Biggest Single Number.sql | 77 ++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 619. Biggest Single Number.sql

diff --git a/619. Biggest Single Number.sql b/619. Biggest Single Number.sql
new file mode 100644
index 0000000..0d98df2
--- /dev/null
+++ b/619. Biggest Single Number.sql	
@@ -0,0 +1,77 @@
+619. Biggest Single Number
+
+Table: MyNumbers
+
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| num         | int  |
++-------------+------+
+This table may contain duplicates (In other words, there is no primary key for this table in SQL).
+Each row of this table contains an integer.
+ 
+
+A single number is a number that appeared only once in the MyNumbers table.
+
+Find the largest single number. If there is no single number, report null.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+MyNumbers table:
++-----+
+| num |
++-----+
+| 8   |
+| 8   |
+| 3   |
+| 3   |
+| 1   |
+| 4   |
+| 5   |
+| 6   |
++-----+
+Output: 
++-----+
+| num |
++-----+
+| 6   |
++-----+
+Explanation: The single numbers are 1, 4, 5, and 6.
+Since 6 is the largest single number, we return it.
+Example 2:
+
+Input: 
+MyNumbers table:
++-----+
+| num |
++-----+
+| 8   |
+| 8   |
+| 7   |
+| 7   |
+| 3   |
+| 3   |
+| 3   |
++-----+
+Output: 
++------+
+| num  |
++------+
+| null |
++------+
+Explanation: There are no single numbers in the input table so we return null.
+
+
+# Write your MySQL query statement below
+SELECT MAX(num) AS num
+FROM (
+    SELECT num
+    FROM MyNumbers
+    GROUP BY num
+    HAVING COUNT(num) = 1
+) AS unique_numbers;
\ No newline at end of file

From fe939032abeb279cbefff2b757b1c4c9b1812632 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 14 Mar 2025 17:38:06 +0530
Subject: [PATCH 025/353] Create 1045. Customers Who Bought All Products.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 1045. Customers Who Bought All Products.sql | 68 +++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 1045. Customers Who Bought All Products.sql

diff --git a/1045. Customers Who Bought All Products.sql b/1045. Customers Who Bought All Products.sql
new file mode 100644
index 0000000..15c730d
--- /dev/null
+++ b/1045. Customers Who Bought All Products.sql	
@@ -0,0 +1,68 @@
+1045. Customers Who Bought All Products
+
+Table: Customer
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| customer_id | int     |
+| product_key | int     |
++-------------+---------+
+This table may contain duplicates rows. 
+customer_id is not NULL.
+product_key is a foreign key (reference column) to Product table.
+ 
+
+Table: Product
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| product_key | int     |
++-------------+---------+
+product_key is the primary key (column with unique values) for this table.
+ 
+
+Write a solution to report the customer ids from the Customer table that bought all the products in the Product table.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Customer table:
++-------------+-------------+
+| customer_id | product_key |
++-------------+-------------+
+| 1           | 5           |
+| 2           | 6           |
+| 3           | 5           |
+| 3           | 6           |
+| 1           | 6           |
++-------------+-------------+
+Product table:
++-------------+
+| product_key |
++-------------+
+| 5           |
+| 6           |
++-------------+
+Output: 
++-------------+
+| customer_id |
++-------------+
+| 1           |
+| 3           |
++-------------+
+Explanation: 
+The customers who bought all the products (5 and 6) are customers with IDs 1 and 3.
+
+
+
+# Write your MySQL query statement below
+SELECT  customer_id FROM Customer GROUP BY customer_id
+HAVING COUNT(distinct product_key) = (SELECT COUNT(product_key) FROM Product);
\ No newline at end of file

From 520b5eb34aa8bf1d4f8658518e91371a7206f5fe Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Tue, 18 Mar 2025 00:14:16 +0530
Subject: [PATCH 026/353] create a folder and add all fils in that

---
 .../1045. Customers Who Bought All Products.sql             | 0
 .../1068. Product Sales Analysis I.sql                      | 0
 .../1070. Product Sales Analysis III.sql                    | 0
 .../1075. Project Employees I.sql                           | 0
 .../1141. User Activity for the Past 30 Days I.sql          | 0
 .../1148. Article Views I.sql                               | 0
 .../1174. Immediate Food Delivery II.sql                    | 0
 .../1193. Monthly Transactions I.sql                        | 0
 .../1211. Queries Quality and Percentage.sql                | 0
 .../1251. Average Selling Price.sql                         | 0
 .../1280. Students and Examinations.sql                     | 0
 ...1378. Replace Employee ID With The Unique Identifier.sql | 0
 .../1633. Percentage of Users Attended a Contest.sql        | 0
 .../1683. Invalid Tweets.sql                                | 0
 .../1729. Find Followers Count.sql                          | 0
 .../1757. Recyclable and Low Fat Products.sql               | 6 +++---
 .../176. Second heighest salary.sql                         | 0
 .../177. Nth Highest Salary.sql                             | 0
 .../185. Department Top Three Salaries.md                   | 0
 .../185. Department Top Three Salaries.sql                  | 0
 .../1934. Confirmation Rate.sql                             | 0
 ...56. Number of Unique Subjects Taught by Each Teacher.sql | 0
 .../550. Game Play Analysis IV.sql                          | 0
 .../570. Managers with at Least 5 Direct Reports.sql        | 0
 .../584. Find Customer Referee.sql                          | 0
 .../595. Big Countries.sql                                  | 0
 .../596. Classes More Than 5 Students.sql                   | 0
 .../619. Biggest Single Number.sql                          | 0
 .../620. Not Boring Movies.sql                              | 0
 .../prosnal_database.sql                                    | 0
 30 files changed, 3 insertions(+), 3 deletions(-)
 rename 1045. Customers Who Bought All Products.sql => LeetCode SQL 50 Solution/1045. Customers Who Bought All Products.sql (100%)
 rename 1068. Product Sales Analysis I.sql => LeetCode SQL 50 Solution/1068. Product Sales Analysis I.sql (100%)
 rename 1070. Product Sales Analysis III.sql => LeetCode SQL 50 Solution/1070. Product Sales Analysis III.sql (100%)
 rename 1075. Project Employees I.sql => LeetCode SQL 50 Solution/1075. Project Employees I.sql (100%)
 rename 1141. User Activity for the Past 30 Days I.sql => LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I.sql (100%)
 rename 1148. Article Views I.sql => LeetCode SQL 50 Solution/1148. Article Views I.sql (100%)
 rename 1174. Immediate Food Delivery II.sql => LeetCode SQL 50 Solution/1174. Immediate Food Delivery II.sql (100%)
 rename 1193. Monthly Transactions I.sql => LeetCode SQL 50 Solution/1193. Monthly Transactions I.sql (100%)
 rename 1211. Queries Quality and Percentage.sql => LeetCode SQL 50 Solution/1211. Queries Quality and Percentage.sql (100%)
 rename 1251. Average Selling Price.sql => LeetCode SQL 50 Solution/1251. Average Selling Price.sql (100%)
 rename 1280. Students and Examinations.sql => LeetCode SQL 50 Solution/1280. Students and Examinations.sql (100%)
 rename 1378. Replace Employee ID With The Unique Identifier.sql => LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier.sql (100%)
 rename 1633. Percentage of Users Attended a Contest.sql => LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest.sql (100%)
 rename 1683. Invalid Tweets.sql => LeetCode SQL 50 Solution/1683. Invalid Tweets.sql (100%)
 rename 1729. Find Followers Count.sql => LeetCode SQL 50 Solution/1729. Find Followers Count.sql (100%)
 rename 1757. Recyclable and Low Fat Products.sql => LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products.sql (97%)
 rename 176. Second heighest salary.sql => LeetCode SQL 50 Solution/176. Second heighest salary.sql (100%)
 rename 177. Nth Highest Salary.sql => LeetCode SQL 50 Solution/177. Nth Highest Salary.sql (100%)
 rename 185. Department Top Three Salaries.md => LeetCode SQL 50 Solution/185. Department Top Three Salaries.md (100%)
 rename 185. Department Top Three Salaries.sql => LeetCode SQL 50 Solution/185. Department Top Three Salaries.sql (100%)
 rename 1934. Confirmation Rate.sql => LeetCode SQL 50 Solution/1934. Confirmation Rate.sql (100%)
 rename 2356. Number of Unique Subjects Taught by Each Teacher.sql => LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher.sql (100%)
 rename 550. Game Play Analysis IV.sql => LeetCode SQL 50 Solution/550. Game Play Analysis IV.sql (100%)
 rename 570. Managers with at Least 5 Direct Reports.sql => LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports.sql (100%)
 rename 584. Find Customer Referee.sql => LeetCode SQL 50 Solution/584. Find Customer Referee.sql (100%)
 rename 595. Big Countries.sql => LeetCode SQL 50 Solution/595. Big Countries.sql (100%)
 rename 596. Classes More Than 5 Students.sql => LeetCode SQL 50 Solution/596. Classes More Than 5 Students.sql (100%)
 rename 619. Biggest Single Number.sql => LeetCode SQL 50 Solution/619. Biggest Single Number.sql (100%)
 rename 620. Not Boring Movies.sql => LeetCode SQL 50 Solution/620. Not Boring Movies.sql (100%)
 rename prosnal_database.sql => LeetCode SQL 50 Solution/prosnal_database.sql (100%)

diff --git a/1045. Customers Who Bought All Products.sql b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products.sql
similarity index 100%
rename from 1045. Customers Who Bought All Products.sql
rename to LeetCode SQL 50 Solution/1045. Customers Who Bought All Products.sql
diff --git a/1068. Product Sales Analysis I.sql b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I.sql
similarity index 100%
rename from 1068. Product Sales Analysis I.sql
rename to LeetCode SQL 50 Solution/1068. Product Sales Analysis I.sql
diff --git a/1070. Product Sales Analysis III.sql b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III.sql
similarity index 100%
rename from 1070. Product Sales Analysis III.sql
rename to LeetCode SQL 50 Solution/1070. Product Sales Analysis III.sql
diff --git a/1075. Project Employees I.sql b/LeetCode SQL 50 Solution/1075. Project Employees I.sql
similarity index 100%
rename from 1075. Project Employees I.sql
rename to LeetCode SQL 50 Solution/1075. Project Employees I.sql
diff --git a/1141. User Activity for the Past 30 Days I.sql b/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I.sql
similarity index 100%
rename from 1141. User Activity for the Past 30 Days I.sql
rename to LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I.sql
diff --git a/1148. Article Views I.sql b/LeetCode SQL 50 Solution/1148. Article Views I.sql
similarity index 100%
rename from 1148. Article Views I.sql
rename to LeetCode SQL 50 Solution/1148. Article Views I.sql
diff --git a/1174. Immediate Food Delivery II.sql b/LeetCode SQL 50 Solution/1174. Immediate Food Delivery II.sql
similarity index 100%
rename from 1174. Immediate Food Delivery II.sql
rename to LeetCode SQL 50 Solution/1174. Immediate Food Delivery II.sql
diff --git a/1193. Monthly Transactions I.sql b/LeetCode SQL 50 Solution/1193. Monthly Transactions I.sql
similarity index 100%
rename from 1193. Monthly Transactions I.sql
rename to LeetCode SQL 50 Solution/1193. Monthly Transactions I.sql
diff --git a/1211. Queries Quality and Percentage.sql b/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage.sql
similarity index 100%
rename from 1211. Queries Quality and Percentage.sql
rename to LeetCode SQL 50 Solution/1211. Queries Quality and Percentage.sql
diff --git a/1251. Average Selling Price.sql b/LeetCode SQL 50 Solution/1251. Average Selling Price.sql
similarity index 100%
rename from 1251. Average Selling Price.sql
rename to LeetCode SQL 50 Solution/1251. Average Selling Price.sql
diff --git a/1280. Students and Examinations.sql b/LeetCode SQL 50 Solution/1280. Students and Examinations.sql
similarity index 100%
rename from 1280. Students and Examinations.sql
rename to LeetCode SQL 50 Solution/1280. Students and Examinations.sql
diff --git a/1378. Replace Employee ID With The Unique Identifier.sql b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier.sql
similarity index 100%
rename from 1378. Replace Employee ID With The Unique Identifier.sql
rename to LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier.sql
diff --git a/1633. Percentage of Users Attended a Contest.sql b/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest.sql
similarity index 100%
rename from 1633. Percentage of Users Attended a Contest.sql
rename to LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest.sql
diff --git a/1683. Invalid Tweets.sql b/LeetCode SQL 50 Solution/1683. Invalid Tweets.sql
similarity index 100%
rename from 1683. Invalid Tweets.sql
rename to LeetCode SQL 50 Solution/1683. Invalid Tweets.sql
diff --git a/1729. Find Followers Count.sql b/LeetCode SQL 50 Solution/1729. Find Followers Count.sql
similarity index 100%
rename from 1729. Find Followers Count.sql
rename to LeetCode SQL 50 Solution/1729. Find Followers Count.sql
diff --git a/1757. Recyclable and Low Fat Products.sql b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products.sql
similarity index 97%
rename from 1757. Recyclable and Low Fat Products.sql
rename to LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products.sql
index fd2c848..65f4ac5 100644
--- a/1757. Recyclable and Low Fat Products.sql	
+++ b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products.sql	
@@ -1,4 +1,4 @@
-# Write your MySQL query statement below
-select product_id
-from products
+# Write your MySQL query statement below
+select product_id
+from products
 where low_fats = 'Y' and recyclable = 'Y'
\ No newline at end of file
diff --git a/176. Second heighest salary.sql b/LeetCode SQL 50 Solution/176. Second heighest salary.sql
similarity index 100%
rename from 176. Second heighest salary.sql
rename to LeetCode SQL 50 Solution/176. Second heighest salary.sql
diff --git a/177. Nth Highest Salary.sql b/LeetCode SQL 50 Solution/177. Nth Highest Salary.sql
similarity index 100%
rename from 177. Nth Highest Salary.sql
rename to LeetCode SQL 50 Solution/177. Nth Highest Salary.sql
diff --git a/185. Department Top Three Salaries.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries.md
similarity index 100%
rename from 185. Department Top Three Salaries.md
rename to LeetCode SQL 50 Solution/185. Department Top Three Salaries.md
diff --git a/185. Department Top Three Salaries.sql b/LeetCode SQL 50 Solution/185. Department Top Three Salaries.sql
similarity index 100%
rename from 185. Department Top Three Salaries.sql
rename to LeetCode SQL 50 Solution/185. Department Top Three Salaries.sql
diff --git a/1934. Confirmation Rate.sql b/LeetCode SQL 50 Solution/1934. Confirmation Rate.sql
similarity index 100%
rename from 1934. Confirmation Rate.sql
rename to LeetCode SQL 50 Solution/1934. Confirmation Rate.sql
diff --git a/2356. Number of Unique Subjects Taught by Each Teacher.sql b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher.sql
similarity index 100%
rename from 2356. Number of Unique Subjects Taught by Each Teacher.sql
rename to LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher.sql
diff --git a/550. Game Play Analysis IV.sql b/LeetCode SQL 50 Solution/550. Game Play Analysis IV.sql
similarity index 100%
rename from 550. Game Play Analysis IV.sql
rename to LeetCode SQL 50 Solution/550. Game Play Analysis IV.sql
diff --git a/570. Managers with at Least 5 Direct Reports.sql b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports.sql
similarity index 100%
rename from 570. Managers with at Least 5 Direct Reports.sql
rename to LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports.sql
diff --git a/584. Find Customer Referee.sql b/LeetCode SQL 50 Solution/584. Find Customer Referee.sql
similarity index 100%
rename from 584. Find Customer Referee.sql
rename to LeetCode SQL 50 Solution/584. Find Customer Referee.sql
diff --git a/595. Big Countries.sql b/LeetCode SQL 50 Solution/595. Big Countries.sql
similarity index 100%
rename from 595. Big Countries.sql
rename to LeetCode SQL 50 Solution/595. Big Countries.sql
diff --git a/596. Classes More Than 5 Students.sql b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students.sql
similarity index 100%
rename from 596. Classes More Than 5 Students.sql
rename to LeetCode SQL 50 Solution/596. Classes More Than 5 Students.sql
diff --git a/619. Biggest Single Number.sql b/LeetCode SQL 50 Solution/619. Biggest Single Number.sql
similarity index 100%
rename from 619. Biggest Single Number.sql
rename to LeetCode SQL 50 Solution/619. Biggest Single Number.sql
diff --git a/620. Not Boring Movies.sql b/LeetCode SQL 50 Solution/620. Not Boring Movies.sql
similarity index 100%
rename from 620. Not Boring Movies.sql
rename to LeetCode SQL 50 Solution/620. Not Boring Movies.sql
diff --git a/prosnal_database.sql b/LeetCode SQL 50 Solution/prosnal_database.sql
similarity index 100%
rename from prosnal_database.sql
rename to LeetCode SQL 50 Solution/prosnal_database.sql

From 8ea64ac0b627ff3ae54e63083963431bdc6abb25 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Thu, 20 Mar 2025 22:52:07 +0530
Subject: [PATCH 027/353] today solution sql

---
 ...mployees Which Report to Each Employee.sql | 88 +++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee.sql

diff --git a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee.sql b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee.sql
new file mode 100644
index 0000000..fabb660
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee.sql	
@@ -0,0 +1,88 @@
+1731. The Number of Employees Which Report to Each Employee
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Employees
+
++-------------+----------+
+| Column Name | Type     |
++-------------+----------+
+| employee_id | int      |
+| name        | varchar  |
+| reports_to  | int      |
+| age         | int      |
++-------------+----------+
+employee_id is the column with unique values for this table.
+This table contains information about the employees and the id of the manager they report to. Some employees do not report to anyone (reports_to is null). 
+ 
+
+For this problem, we will consider a manager an employee who has at least 1 other employee reporting to them.
+
+Write a solution to report the ids and the names of all managers, the number of employees who report directly to them, and the average age of the reports rounded to the nearest integer.
+
+Return the result table ordered by employee_id.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Employees table:
++-------------+---------+------------+-----+
+| employee_id | name    | reports_to | age |
++-------------+---------+------------+-----+
+| 9           | Hercy   | null       | 43  |
+| 6           | Alice   | 9          | 41  |
+| 4           | Bob     | 9          | 36  |
+| 2           | Winston | null       | 37  |
++-------------+---------+------------+-----+
+Output: 
++-------------+-------+---------------+-------------+
+| employee_id | name  | reports_count | average_age |
++-------------+-------+---------------+-------------+
+| 9           | Hercy | 2             | 39          |
++-------------+-------+---------------+-------------+
+Explanation: Hercy has 2 people report directly to him, Alice and Bob. Their average age is (41+36)/2 = 38.5, which is 39 after rounding it to the nearest integer.
+Example 2:
+
+Input: 
+Employees table:
++-------------+---------+------------+-----+ 
+| employee_id | name    | reports_to | age |
+|-------------|---------|------------|-----|
+| 1           | Michael | null       | 45  |
+| 2           | Alice   | 1          | 38  |
+| 3           | Bob     | 1          | 42  |
+| 4           | Charlie | 2          | 34  |
+| 5           | David   | 2          | 40  |
+| 6           | Eve     | 3          | 37  |
+| 7           | Frank   | null       | 50  |
+| 8           | Grace   | null       | 48  |
++-------------+---------+------------+-----+ 
+Output: 
++-------------+---------+---------------+-------------+
+| employee_id | name    | reports_count | average_age |
+| ----------- | ------- | ------------- | ----------- |
+| 1           | Michael | 2             | 40          |
+| 2           | Alice   | 2             | 37          |
+| 3           | Bob     | 1             | 37          |
++-------------+---------+---------------+-------------+
+
+
+# Write your MySQL query statement below
+
+SELECT
+  Manager.employee_id,
+  Manager.name,
+  COUNT(Employee.employee_id) AS reports_count,
+  ROUND(AVG(Employee.age)) AS average_age
+FROM Employees AS Manager
+INNER JOIN Employees AS Employee
+  ON (Employee.reports_to = Manager.employee_id)
+GROUP BY 1
+ORDER BY 1;

From 9af7a3e048ae8e527bfd9cec3bc928610a56f00c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Thu, 20 Mar 2025 22:56:38 +0530
Subject: [PATCH 028/353] Create 1789. Primary Department for Each Employee.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .... Primary Department for Each Employee.sql | 70 +++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/1789. Primary Department for Each Employee.sql

diff --git a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee.sql b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee.sql
new file mode 100644
index 0000000..9d88082
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee.sql	
@@ -0,0 +1,70 @@
+1789. Primary Department for Each Employee
+
+
++---------------+---------+
+| Column Name   |  Type   |
++---------------+---------+
+| employee_id   | int     |
+| department_id | int     |
+| primary_flag  | varchar |
++---------------+---------+
+(employee_id, department_id) is the primary key (combination of columns with unique values) for this table.
+employee_id is the id of the employee.
+department_id is the id of the department to which the employee belongs.
+primary_flag is an ENUM (category) of type ('Y', 'N'). If the flag is 'Y', the department is the primary department for the employee. If the flag is 'N', the department is not the primary.
+ 
+
+Employees can belong to multiple departments. When the employee joins other departments, they need to decide which department is their primary department. Note that when an employee belongs to only one department, their primary column is 'N'.
+
+Write a solution to report all the employees with their primary department. For employees who belong to one department, report their only department.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Employee table:
++-------------+---------------+--------------+
+| employee_id | department_id | primary_flag |
++-------------+---------------+--------------+
+| 1           | 1             | N            |
+| 2           | 1             | Y            |
+| 2           | 2             | N            |
+| 3           | 3             | N            |
+| 4           | 2             | N            |
+| 4           | 3             | Y            |
+| 4           | 4             | N            |
++-------------+---------------+--------------+
+Output: 
++-------------+---------------+
+| employee_id | department_id |
++-------------+---------------+
+| 1           | 1             |
+| 2           | 1             |
+| 3           | 3             |
+| 4           | 3             |
++-------------+---------------+
+Explanation: 
+- The Primary department for employee 1 is 1.
+- The Primary department for employee 2 is 1.
+- The Primary department for employee 3 is 3.
+- The Primary department for employee 4 is 3.
+
+
+# Write your MySQL query statement below
+SELECT
+  employee_id,
+  department_id
+FROM Employee
+WHERE primary_flag = 'Y'
+UNION DISTINCT
+SELECT
+  employee_id,
+  department_id
+FROM Employee
+GROUP BY 1
+HAVING COUNT(*) = 1;
\ No newline at end of file

From 277cb6a266311ecec28af82910d4503bbbbeba0b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 01:34:26 +0530
Subject: [PATCH 029/353] Create 180. Consecutive Numbers.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../180. Consecutive Numbers.sql              | 112 ++++++++++++++++++
 1 file changed, 112 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/180. Consecutive Numbers.sql

diff --git a/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql b/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql
new file mode 100644
index 0000000..04a0b6c
--- /dev/null
+++ b/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql	
@@ -0,0 +1,112 @@
+180. Consecutive Numbers
+"""
+# Write your MySQL query statement below
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| num         | varchar |
++-------------+---------+
+In SQL, id is the primary key for this table.
+id is an autoincrement column starting from 1.
+ 
+
+Find all numbers that appear at least three times consecutively.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Logs table:
++----+-----+
+| id | num |
++----+-----+
+| 1  | 1   |
+| 2  | 1   |
+| 3  | 1   |
+| 4  | 2   |
+| 5  | 1   |
+| 6  | 2   |
+| 7  | 2   |
++----+-----+
+Output: 
++-----------------+
+| ConsecutiveNums |
++-----------------+
+| 1               |
++-----------------+
+
+"""
+Explanation: 1 is the only number that appears consecutively for at least three times.
+
+# Write your MySQL query statement below
+with consecutive_runs as (
+    select id, num, lead(num, 1) over (order by id) as lead_num, lag(num, 1) over (order by id) as lag_num
+    from Logs
+)
+select distinct num as ConsecutiveNums
+from consecutive_runs 
+where num = lead_num and num = lag_num
+
+"""
+Explanation
+ Common Table Expression (CTE): consecutive_runs
+sql
+Copy
+Edit
+with consecutive_runs as (
+    select 
+        id, 
+        num, 
+        lead(num, 1) over (order by id) as lead_num, 
+        lag(num, 1) over (order by id) as lag_num
+    from Logs
+)
+Purpose of the CTE:
+This part creates a temporary result set named consecutive_runs that enriches each row from the Logs table with two extra columns:
+
+lead_num: The value of the num column in the next row (based on the id order).
+lag_num: The value of the num column in the previous row (based on the id order).
+Window Functions:
+
+lead(num, 1) over (order by id):
+This function returns the value of num from the row immediately following the current one when sorted by id.
+lag(num, 1) over (order by id):
+This function returns the value of num from the row immediately preceding the current one when sorted by id.
+This setup allows us to compare each row with its immediate neighbors.
+
+2. Final SELECT Query
+sql
+Copy
+Edit
+select distinct num as ConsecutiveNums
+from consecutive_runs 
+where num = lead_num and num = lag_num
+Filtering Condition:
+The WHERE clause checks if the current row's num value is equal to both its next (lead_num) and previous (lag_num) values:
+
+num = lead_num
+num = lag_num
+This ensures that the number appears consecutively (at least three times in a row).
+
+DISTINCT Keyword:
+The distinct keyword makes sure that each number is listed only once in the final output, even if it occurs in multiple consecutive sequences.
+
+Result Column Alias:
+The output column is renamed to ConsecutiveNums for clarity.
+
+Summary
+CTE Usage:
+The query first computes additional columns using window functions (lead and lag) to look at neighboring rows.
+
+Consecutive Check:
+It then filters out rows where the current value is the same as both the previous and next value, meaning there are at least three consecutive occurrences of that number.
+
+Final Output:
+The final result is a list of distinct numbers that appear consecutively in the Logs table.
+"""
\ No newline at end of file

From 85ee08b9a621e60df60b2c009a1c9f69059209b7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:03:09 +0530
Subject: [PATCH 030/353] 1164. Product Price at a Given Date

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../1164. Product Price at a Given Date.sql   | 46 +++++++++++++++++++
 .../180. Consecutive Numbers.sql              |  2 +-
 2 files changed, 47 insertions(+), 1 deletion(-)
 create mode 100644 LeetCode SQL 50 Solution/1164. Product Price at a Given Date.sql

diff --git a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date.sql b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date.sql
new file mode 100644
index 0000000..6fa8bd6
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date.sql	
@@ -0,0 +1,46 @@
+
+1164. Product Price at a Given Date
+
+Table: Products
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| product_id    | int     |
+| new_price     | int     |
+| change_date   | date    |
++---------------+---------+
+(product_id, change_date) is the primary key (combination of columns with unique values) of this table.
+Each row of this table indicates that the price of some product was changed to a new price at some date.
+ 
+
+Write a solution to find the prices of all products on 2019-08-16. Assume the price of all products before any change is 10.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Products table:
++------------+-----------+-------------+
+| product_id | new_price | change_date |
++------------+-----------+-------------+
+| 1          | 20        | 2019-08-14  |
+| 2          | 50        | 2019-08-14  |
+| 1          | 30        | 2019-08-15  |
+| 1          | 35        | 2019-08-16  |
+| 2          | 65        | 2019-08-17  |
+| 3          | 20        | 2019-08-18  |
++------------+-----------+-------------+
+Output: 
++------------+-------+
+| product_id | price |
++------------+-------+
+| 2          | 50    |
+| 1          | 35    |
+| 3          | 10    |
++------------+-------+
\ No newline at end of file
diff --git a/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql b/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql
index 04a0b6c..2ef1d2c 100644
--- a/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql	
+++ b/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql	
@@ -109,4 +109,4 @@ It then filters out rows where the current value is the same as both the previou
 
 Final Output:
 The final result is a list of distinct numbers that appear consecutively in the Logs table.
-"""
\ No newline at end of file
+"""

From d5abc631b27eb46c1548a16e8253b413bf860b1e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:03:30 +0530
Subject: [PATCH 031/353] Update 180. Consecutive Numbers.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../180. Consecutive Numbers.sql              | 21 +++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql b/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql
index 2ef1d2c..3a3943a 100644
--- a/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql	
+++ b/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql	
@@ -110,3 +110,24 @@ It then filters out rows where the current value is the same as both the previou
 Final Output:
 The final result is a list of distinct numbers that appear consecutively in the Logs table.
 """
+
+
+# Write your MySQL query statement below
+# Write your MySQL query statement below
+WITH
+    T AS (SELECT DISTINCT product_id FROM Products),
+    P AS (
+        SELECT product_id, new_price AS price
+        FROM Products
+        WHERE
+            (product_id, change_date) IN (
+                SELECT product_id, MAX(change_date) AS change_date
+                FROM Products
+                WHERE change_date <= '2019-08-16'
+                GROUP BY 1
+            )
+    )
+SELECT product_id, IFNULL(price, 10) AS price
+FROM
+    T
+    LEFT JOIN P USING (product_id);

From c6830720a87276cbcf156c0f1b0223251e4a0353 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:24:38 +0530
Subject: [PATCH 032/353] create file

---
 LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql
new file mode 100644
index 0000000..e69de29

From a304fd1a4f3d27880f9021895b86ef4a537cbcd4 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:25:05 +0530
Subject: [PATCH 033/353] Update 1204. Last Person to Fit in the Bus.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../1204. Last Person to Fit in the Bus.sql   | 60 +++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql
index e69de29..47f400c 100644
--- a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql	
+++ b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql	
@@ -0,0 +1,60 @@
+1204. Last Person to Fit in the Bus
+
+Table: Queue
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| person_id   | int     |
+| person_name | varchar |
+| weight      | int     |
+| turn        | int     |
++-------------+---------+
+person_id column contains unique values.
+This table has the information about all people waiting for a bus.
+The person_id and turn columns will contain all numbers from 1 to n, where n is the number of rows in the table.
+turn determines the order of which the people will board the bus, where turn=1 denotes the first person to board and turn=n denotes the last person to board.
+weight is the weight of the person in kilograms.
+ 
+
+There is a queue of people waiting to board a bus. However, the bus has a weight limit of 1000 kilograms, so there may be some people who cannot board.
+
+Write a solution to find the person_name of the last person that can fit on the bus without exceeding the weight limit. The test cases are generated such that the first person does not exceed the weight limit.
+
+Note that only one person can board the bus at any given turn.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Queue table:
++-----------+-------------+--------+------+
+| person_id | person_name | weight | turn |
++-----------+-------------+--------+------+
+| 5         | Alice       | 250    | 1    |
+| 4         | Bob         | 175    | 5    |
+| 3         | Alex        | 350    | 2    |
+| 6         | John Cena   | 400    | 3    |
+| 1         | Winston     | 500    | 6    |
+| 2         | Marie       | 200    | 4    |
++-----------+-------------+--------+------+
+Output: 
++-------------+
+| person_name |
++-------------+
+| John Cena   |
++-------------+
+Explanation: The folowing table is ordered by the turn for simplicity.
++------+----+-----------+--------+--------------+
+| Turn | ID | Name      | Weight | Total Weight |
++------+----+-----------+--------+--------------+
+| 1    | 5  | Alice     | 250    | 250          |
+| 2    | 3  | Alex      | 350    | 600          |
+| 3    | 6  | John Cena | 400    | 1000         | (last person to board)
+| 4    | 2  | Marie     | 200    | 1200         | (cannot board)
+| 5    | 4  | Bob       | 175    | ___          |
+| 6    | 1  | Winston   | 500    | ___          |
++------+----+-----------+--------+--------------+
\ No newline at end of file

From 51cfc2fc1f683790b39b56fca4a2b7bd2af2decf Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:26:13 +0530
Subject: [PATCH 034/353] Update 1204. Last Person to Fit in the Bus.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../1204. Last Person to Fit in the Bus.sql     | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql
index 47f400c..9b7eaef 100644
--- a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql	
+++ b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql	
@@ -57,4 +57,19 @@ Explanation: The folowing table is ordered by the turn for simplicity.
 | 4    | 2  | Marie     | 200    | 1200         | (cannot board)
 | 5    | 4  | Bob       | 175    | ___          |
 | 6    | 1  | Winston   | 500    | ___          |
-+------+----+-----------+--------+--------------+
\ No newline at end of file
++------+----+-----------+--------+--------------+
+
+
+
+# Write your MySQL query statement below
+SELECT a.person_name
+FROM
+    Queue AS a,
+    Queue AS b
+WHERE a.turn >= b.turn
+GROUP BY a.person_id
+HAVING SUM(b.weight) <= 1000
+ORDER BY a.turn DESC
+LIMIT 1;
+
+

From 8ca3a97830bd32574abf3fa08aba9b7d9977fe10 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:27:24 +0530
Subject: [PATCH 035/353] Update 1204. Last Person to Fit in the Bus.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../1204. Last Person to Fit in the Bus.sql   | 73 +++++++++++++++++++
 1 file changed, 73 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql
index 9b7eaef..7fe1a3c 100644
--- a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql	
+++ b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql	
@@ -73,3 +73,76 @@ ORDER BY a.turn DESC
 LIMIT 1;
 
 
+Let's break down the query step by step to understand what it does:
+
+---
+
+### 1. Self-Join of the Table
+
+```sql
+FROM
+    Queue AS a,
+    Queue AS b
+WHERE a.turn >= b.turn
+```
+
+- **Self-Join:**  
+  The query treats the `Queue` table as two separate aliases: `a` and `b`. This is a self-join, meaning each row in `a` is paired with rows in `b`.
+
+- **Join Condition (`a.turn >= b.turn`):**  
+  For each row in alias `a`, the query pairs it with every row in alias `b` that has a `turn` value less than or equal to `a.turn`.  
+  - **Purpose:** This setup is used to accumulate data from the start of the queue up to the current person's turn.
+
+---
+
+### 2. Grouping by Person
+
+```sql
+GROUP BY a.person_id
+```
+
+- **Grouping:**  
+  The query groups the resulting joined rows by `a.person_id`.  
+  - **Effect:** For each person in the queue (represented by alias `a`), all rows (from `b`) where `b.turn` is less than or equal to `a.turn` are aggregated together.
+
+---
+
+### 3. Calculating the Cumulative Weight
+
+```sql
+HAVING SUM(b.weight) <= 1000
+```
+
+- **Cumulative Sum:**  
+  Within each group, the query calculates the sum of `b.weight`.  
+  - **Condition:** The `HAVING` clause filters out groups where the cumulative weight (i.e., the sum of weights from the start of the queue up to the current person's turn) exceeds 1000.
+  - **Interpretation:** Only those persons for whom the cumulative weight of all people before and including them is **less than or equal to 1000** are kept.
+
+---
+
+### 4. Selecting the Result
+
+```sql
+SELECT a.person_name
+```
+
+- **Result Column:**  
+  After filtering, the query selects the `person_name` from alias `a` for each group that passed the `HAVING` condition.
+
+---
+
+### 5. Ordering and Limiting the Result
+
+```sql
+ORDER BY a.turn DESC
+LIMIT 1;
+```
+
+- **Ordering:**  
+  The results are ordered by `a.turn` in descending order.  
+  - **Purpose:** This ensures that among all persons whose cumulative weight is ≤ 1000, the one with the **latest (highest) turn** is at the top.
+  
+- **Limiting:**  
+  The `LIMIT 1` clause restricts the output to only the top result, effectively returning **one person**.
+
+---

From 79aeb8fab2eb2a08c1296e28b171529b8e4b9f4f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:34:37 +0530
Subject: [PATCH 036/353] Create 1907. Count Salary Categories.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 LeetCode SQL 50 Solution/1907. Count Salary Categories.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1907. Count Salary Categories.sql

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql b/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql
new file mode 100644
index 0000000..e69de29

From c26af549ef8eb2f7705e88cf3a5046fffd7b3728 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:35:27 +0530
Subject: [PATCH 037/353] Update 1907. Count Salary Categories.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../1907. Count Salary Categories.sql         | 83 +++++++++++++++++++
 1 file changed, 83 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql b/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql
index e69de29..f3923cf 100644
--- a/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql	
+++ b/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql	
@@ -0,0 +1,83 @@
+907. Count Salary Categories
+Solved
+Medium
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Accounts
+
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| account_id  | int  |
+| income      | int  |
++-------------+------+
+account_id is the primary key (column with unique values) for this table.
+Each row contains information about the monthly income for one bank account.
+ 
+
+Write a solution to calculate the number of bank accounts for each salary category. The salary categories are:
+
+"Low Salary": All the salaries strictly less than $20000.
+"Average Salary": All the salaries in the inclusive range [$20000, $50000].
+"High Salary": All the salaries strictly greater than $50000.
+The result table must contain all three categories. If there are no accounts in a category, return 0.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Accounts table:
++------------+--------+
+| account_id | income |
++------------+--------+
+| 3          | 108939 |
+| 2          | 12747  |
+| 8          | 87709  |
+| 6          | 91796  |
++------------+--------+
+Output: 
++----------------+----------------+
+| category       | accounts_count |
++----------------+----------------+
+| Low Salary     | 1              |
+| Average Salary | 0              |
+| High Salary    | 3              |
++----------------+----------------+
+Explanation: 
+Low Salary: Account 2.
+Average Salary: No accounts.
+High Salary: Accounts 3, 6, and 8.
+
+
+# Write your MySQL query statement below
+WITH
+    S AS (
+        SELECT 'Low Salary' AS category
+        UNION
+        SELECT 'Average Salary'
+        UNION
+        SELECT 'High Salary'
+    ),
+    T AS (
+        SELECT
+            CASE
+                WHEN income < 20000 THEN "Low Salary"
+                WHEN income > 50000 THEN 'High Salary'
+                ELSE 'Average Salary'
+            END AS category,
+            COUNT(1) AS accounts_count
+        FROM Accounts
+        GROUP BY 1
+    )
+SELECT category, IFNULL(accounts_count, 0) AS accounts_count
+FROM
+    S
+    LEFT JOIN T USING (category);
+

From 494a96840ef3722044c087f5586f029bde5e197e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:37:18 +0530
Subject: [PATCH 038/353] Update 1907. Count Salary Categories.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 .../1907. Count Salary Categories.sql         | 87 +++++++++++++++++++
 1 file changed, 87 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql b/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql
index f3923cf..65786e1 100644
--- a/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql	
+++ b/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql	
@@ -81,3 +81,90 @@ FROM
     S
     LEFT JOIN T USING (category);
 
+
+
+### 1. Common Table Expression (CTE) "S"
+
+
+WITH
+    S AS (
+        SELECT 'Low Salary' AS category
+        UNION
+        SELECT 'Average Salary'
+        UNION
+        SELECT 'High Salary'
+    ),
+
+
+- **Purpose:**  
+  This CTE defines a static list of salary categories.  
+- **How it works:**  
+  - The `SELECT` statements with `UNION` combine three rows, each containing one of the categories: `'Low Salary'`, `'Average Salary'`, and `'High Salary'`.
+- **Result:**  
+  The result of `S` is a temporary table with one column (`category`) and three rows.
+
+---
+
+### 2. Common Table Expression (CTE) "T"
+
+```sql
+    T AS (
+        SELECT
+            CASE
+                WHEN income < 20000 THEN "Low Salary"
+                WHEN income > 50000 THEN 'High Salary'
+                ELSE 'Average Salary'
+            END AS category,
+            COUNT(1) AS accounts_count
+        FROM Accounts
+        GROUP BY 1
+    )
+```
+
+- **Purpose:**  
+  This CTE categorizes each account from the `Accounts` table based on the `income` value, then counts the number of accounts in each category.
+- **How it works:**  
+  - **CASE Statement:**  
+    - If `income` is less than 20000, it labels the row as `"Low Salary"`.
+    - If `income` is greater than 50000, it labels the row as `"High Salary"`.
+    - Otherwise, it labels the row as `"Average Salary"`.
+  - **COUNT(1):**  
+    - It counts the number of rows (accounts) in each category.
+  - **GROUP BY 1:**  
+    - It groups the results by the first column in the SELECT list, which is the computed `category`.
+- **Result:**  
+  The result of `T` is a temporary table that contains two columns: `category` and `accounts_count`. It holds the count of accounts for each salary category that exists in the `Accounts` table.
+
+---
+
+### 3. Final SELECT with LEFT JOIN
+
+```sql
+SELECT category, IFNULL(accounts_count, 0) AS accounts_count
+FROM
+    S
+    LEFT JOIN T USING (category);
+```
+
+- **Purpose:**  
+  This part combines the two CTEs (`S` and `T`) to ensure that every salary category from `S` is included in the final result, even if there are no corresponding accounts in `T`.
+- **How it works:**
+  - **LEFT JOIN:**  
+    - It joins `S` (all predefined categories) with `T` (the computed counts) on the `category` column.
+    - If a category from `S` does not exist in `T` (i.e., there were no accounts that fell into that category), the join will produce a `NULL` value for `accounts_count`.
+  - **IFNULL(accounts_count, 0):**  
+    - This function replaces any `NULL` in `accounts_count` with `0`, ensuring that the final output shows 0 for categories with no accounts.
+- **Result:**  
+  The final output is a list of salary categories along with the corresponding count of accounts. If a category has no accounts, it will show as 0.
+
+---
+
+### Summary
+
+- **CTE "S":** Defines a static list of salary categories.
+- **CTE "T":** Categorizes and counts accounts from the `Accounts` table based on income.
+- **LEFT JOIN:** Combines both CTEs so every predefined category appears in the final result, with missing counts defaulting to 0.
+- **Final Output:**  
+  A table with two columns:
+  - `category`: The salary category (Low Salary, Average Salary, High Salary).
+  - `accounts_count`: The number of accounts in that category (or 0 if there are none).

From 3b1d770946e4eb1b69c520f05e7163faa7858b1f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:45:09 +0530
Subject: [PATCH 039/353] Create 1978. Employees Whose Manager Left the
 Company.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 ...ployees Whose Manager Left the Company.sql | 65 +++++++++++++++++++
 1 file changed, 65 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql

diff --git a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql
new file mode 100644
index 0000000..f2d91a8
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql	
@@ -0,0 +1,65 @@
+1978. Employees Whose Manager Left the Company
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Employees
+
++-------------+----------+
+| Column Name | Type     |
++-------------+----------+
+| employee_id | int      |
+| name        | varchar  |
+| manager_id  | int      |
+| salary      | int      |
++-------------+----------+
+In SQL, employee_id is the primary key for this table.
+This table contains information about the employees, their salary, and the ID of their manager. Some employees do not have a manager (manager_id is null). 
+ 
+
+Find the IDs of the employees whose salary is strictly less than $30000 and whose manager left the company. When a manager leaves the company, their information is deleted from the Employees table, but the reports still have their manager_id set to the manager that left.
+
+Return the result table ordered by employee_id.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input:  
+Employees table:
++-------------+-----------+------------+--------+
+| employee_id | name      | manager_id | salary |
++-------------+-----------+------------+--------+
+| 3           | Mila      | 9          | 60301  |
+| 12          | Antonella | null       | 31000  |
+| 13          | Emery     | null       | 67084  |
+| 1           | Kalel     | 11         | 21241  |
+| 9           | Mikaela   | null       | 50937  |
+| 11          | Joziah    | 6          | 28485  |
++-------------+-----------+------------+--------+
+Output: 
++-------------+
+| employee_id |
++-------------+
+| 11          |
++-------------+
+
+Explanation: 
+The employees with a salary less than $30000 are 1 (Kalel) and 11 (Joziah).
+Kalel's manager is employee 11, who is still in the company (Joziah).
+Joziah's manager is employee 6, who left the company because there is no row for employee 6 as it was deleted.
+
+
+
+# Write your MySQL query statement below
+SELECT e1.employee_id
+FROM
+    Employees AS e1
+    LEFT JOIN Employees AS e2 ON e1.manager_id = e2.employee_id
+WHERE e1.salary < 30000 AND e1.manager_id IS NOT NULL AND e2.employee_id IS NULL
+ORDER BY 1;
+

From f727c07b2df135b6cab7e08bfaef521fb269cdad Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 22 Mar 2025 02:46:41 +0530
Subject: [PATCH 040/353] Update 1978. Employees Whose Manager Left the
 Company.sql

Co-Authored-By: Antim-IWP <203163676+Antim-IWP@users.noreply.github.com>
---
 ...ployees Whose Manager Left the Company.sql | 117 ++++++++++++++++++
 1 file changed, 117 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql
index f2d91a8..b0014c3 100644
--- a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql	
+++ b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql	
@@ -63,3 +63,120 @@ FROM
 WHERE e1.salary < 30000 AND e1.manager_id IS NOT NULL AND e2.employee_id IS NULL
 ORDER BY 1;
 
+
+
+
+
+Sure! Let's break down the given MySQL query step by step, explain each part, and analyze its purpose.
+
+---
+
+## **Understanding the Problem Statement**
+We want to find **employees** whose:
+1. **Salary is less than 30,000.**
+2. **Have a manager (i.e., `manager_id IS NOT NULL`).**
+3. **Their manager does not exist in the `Employees` table.** (i.e., the `manager_id` they refer to does not match any existing `employee_id`).
+
+---
+
+## **Database Schema**
+Assume we have a table called `Employees` with the following structure:
+
+| employee_id | name    | salary | manager_id |
+|------------|--------|--------|------------|
+| 1          | Alice  | 50000  | NULL       |
+| 2          | Bob    | 20000  | 1          |
+| 3          | Charlie| 25000  | 4          |
+| 4          | David  | 60000  | NULL       |
+| 5          | Emma   | 27000  | 10         |
+
+- **`employee_id`**: Unique ID for each employee.
+- **`name`**: Employee’s name.
+- **`salary`**: Salary of the employee.
+- **`manager_id`**: The `employee_id` of their manager. `NULL` means the employee has no manager.
+
+---
+
+## **Step-by-Step Explanation of the Query**
+```sql
+SELECT e1.employee_id
+```
+- We are selecting the `employee_id` of employees who satisfy the given conditions.
+
+```sql
+FROM Employees AS e1
+```
+- We define the alias `e1` for the `Employees` table to refer to employees.
+
+```sql
+LEFT JOIN Employees AS e2 ON e1.manager_id = e2.employee_id
+```
+- We perform a **LEFT JOIN** to check if the `manager_id` of `e1` exists in the `employee_id` column of another instance of `Employees` (aliased as `e2`).
+- If there is **no match**, it means the manager does not exist in the table.
+
+```sql
+WHERE e1.salary < 30000
+```
+- We **filter employees** whose salary is **less than 30,000**.
+
+```sql
+AND e1.manager_id IS NOT NULL
+```
+- Ensures that the employee **has a manager** (`manager_id` should not be `NULL`).
+
+```sql
+AND e2.employee_id IS NULL
+```
+- This is the key condition!
+- Since we did a **LEFT JOIN**, `e2.employee_id` will be `NULL` if the manager **does not exist** in the `Employees` table.
+
+```sql
+ORDER BY 1;
+```
+- Orders the result by `employee_id` in ascending order.
+
+---
+
+## **Example Execution**
+Using the above `Employees` table, let's analyze the output:
+
+| employee_id | name   | salary | manager_id |
+|------------|--------|--------|------------|
+| 1          | Alice  | 50000  | NULL       |
+| 2          | Bob    | 20000  | 1          |
+| 3          | Charlie| 25000  | 4          |
+| 4          | David  | 60000  | NULL       |
+| 5          | Emma   | 27000  | 10         |
+
+### **Step 1: LEFT JOIN**
+We join the table on `manager_id = employee_id`:
+
+| e1.employee_id | e1.salary | e1.manager_id | e2.employee_id |
+|---------------|----------|--------------|---------------|
+| 2             | 20000    | 1            | 1             |
+| 3             | 25000    | 4            | 4             |
+| 5             | 27000    | 10           | NULL          |
+
+### **Step 2: Apply WHERE Conditions**
+- `e1.salary < 30000`: Only `Bob`, `Charlie`, and `Emma` qualify.
+- `e1.manager_id IS NOT NULL`: No issue.
+- `e2.employee_id IS NULL`: Only **Emma (employee_id = 5) qualifies** because `manager_id = 10`, and there is no `employee_id = 10`.
+
+### **Final Output**
+| employee_id |
+|------------|
+| 5          |
+
+So, the result is:
+```
+5
+```
+---
+## **Summary**
+### **Key Takeaways:**
+✔ We use **LEFT JOIN** to check if the manager exists.  
+✔ We filter employees whose **salary is below 30,000**.  
+✔ We ensure the manager **does not exist** in the database (`e2.employee_id IS NULL`).  
+✔ The query returns a **list of employees** who meet these conditions.  
+
+Would you like me to explain anything further or modify the query for additional scenarios? 😊
\ No newline at end of file

From a87e7a3a8f335de0b8edbd60e88633ac1b5b7795 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:44:30 +0530
Subject: [PATCH 041/353] Create 1517. Find Users With Valid E-Mails.SQL

---
 LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL
new file mode 100644
index 0000000..e69de29

From cb58a318bee5fbb0fbd386f3e87eb2f099c09393 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:44:46 +0530
Subject: [PATCH 042/353] Update 1517. Find Users With Valid E-Mails.SQL

---
 .../1517. Find Users With Valid E-Mails.SQL   | 62 +++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL
index e69de29..db4f01f 100644
--- a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL	
+++ b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL	
@@ -0,0 +1,62 @@
+1517. Find Users With Valid E-Mails
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Users
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| user_id       | int     |
+| name          | varchar |
+| mail          | varchar |
++---------------+---------+
+user_id is the primary key (column with unique values) for this table.
+This table contains information of the users signed up in a website. Some e-mails are invalid.
+ 
+
+Write a solution to find the users who have valid emails.
+
+A valid e-mail has a prefix name and a domain where:
+
+The prefix name is a string that may contain letters (upper or lower case), digits, underscore '_', period '.', and/or dash '-'. The prefix name must start with a letter.
+The domain is '@leetcode.com'.
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Users table:
++---------+-----------+-------------------------+
+| user_id | name      | mail                    |
++---------+-----------+-------------------------+
+| 1       | Winston   | winston@leetcode.com    |
+| 2       | Jonathan  | jonathanisgreat         |
+| 3       | Annabelle | bella-@leetcode.com     |
+| 4       | Sally     | sally.come@leetcode.com |
+| 5       | Marwan    | quarz#2020@leetcode.com |
+| 6       | David     | david69@gmail.com       |
+| 7       | Shapiro   | .shapo@leetcode.com     |
++---------+-----------+-------------------------+
+Output: 
++---------+-----------+-------------------------+
+| user_id | name      | mail                    |
++---------+-----------+-------------------------+
+| 1       | Winston   | winston@leetcode.com    |
+| 3       | Annabelle | bella-@leetcode.com     |
+| 4       | Sally     | sally.come@leetcode.com |
++---------+-----------+-------------------------+
+Explanation: 
+The mail of user 2 does not have a domain.
+The mail of user 5 has the # sign which is not allowed.
+The mail of user 6 does not have the leetcode domain.
+The mail of user 7 starts with a period.
+
+

From c312f35adb58f133ef1e0f8c92b478b632de83d1 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:45:35 +0530
Subject: [PATCH 043/353] Update 1517. Find Users With Valid E-Mails.SQL

---
 .../1517. Find Users With Valid E-Mails.SQL                   | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL
index db4f01f..07831f5 100644
--- a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL	
+++ b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL	
@@ -59,4 +59,8 @@ The mail of user 5 has the # sign which is not allowed.
 The mail of user 6 does not have the leetcode domain.
 The mail of user 7 starts with a period.
 
+SPLUTIONS:
 
+SELECT *
+FROM Users
+WHERE REGEXP_LIKE(mail, '^[A-Za-z]+[A-Za-z0-9\_\.\-]*@leetcode\\.com$');

From 51351b985737544f442865287080795c45e8b447 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:46:51 +0530
Subject: [PATCH 044/353] Create 1327. List the Products Ordered in a
 Period.SQL

---
 ... List the Products Ordered in a Period.SQL | 85 +++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL

diff --git a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL
new file mode 100644
index 0000000..a3f9c1d
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL	
@@ -0,0 +1,85 @@
+1327. List the Products Ordered in a Period
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Products
+
++------------------+---------+
+| Column Name      | Type    |
++------------------+---------+
+| product_id       | int     |
+| product_name     | varchar |
+| product_category | varchar |
++------------------+---------+
+product_id is the primary key (column with unique values) for this table.
+This table contains data about the company's products.
+ 
+
+Table: Orders
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| product_id    | int     |
+| order_date    | date    |
+| unit          | int     |
++---------------+---------+
+This table may have duplicate rows.
+product_id is a foreign key (reference column) to the Products table.
+unit is the number of products ordered in order_date.
+ 
+
+Write a solution to get the names of products that have at least 100 units ordered in February 2020 and their amount.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Products table:
++-------------+-----------------------+------------------+
+| product_id  | product_name          | product_category |
++-------------+-----------------------+------------------+
+| 1           | Leetcode Solutions    | Book             |
+| 2           | Jewels of Stringology | Book             |
+| 3           | HP                    | Laptop           |
+| 4           | Lenovo                | Laptop           |
+| 5           | Leetcode Kit          | T-shirt          |
++-------------+-----------------------+------------------+
+Orders table:
++--------------+--------------+----------+
+| product_id   | order_date   | unit     |
++--------------+--------------+----------+
+| 1            | 2020-02-05   | 60       |
+| 1            | 2020-02-10   | 70       |
+| 2            | 2020-01-18   | 30       |
+| 2            | 2020-02-11   | 80       |
+| 3            | 2020-02-17   | 2        |
+| 3            | 2020-02-24   | 3        |
+| 4            | 2020-03-01   | 20       |
+| 4            | 2020-03-04   | 30       |
+| 4            | 2020-03-04   | 60       |
+| 5            | 2020-02-25   | 50       |
+| 5            | 2020-02-27   | 50       |
+| 5            | 2020-03-01   | 50       |
++--------------+--------------+----------+
+Output: 
++--------------------+---------+
+| product_name       | unit    |
++--------------------+---------+
+| Leetcode Solutions | 130     |
+| Leetcode Kit       | 100     |
++--------------------+---------+
+Explanation: 
+Products with product_id = 1 is ordered in February a total of (60 + 70) = 130.
+Products with product_id = 2 is ordered in February a total of 80.
+Products with product_id = 3 is ordered in February a total of (2 + 3) = 5.
+Products with product_id = 4 was not ordered in February 2020.
+Products with product_id = 5 is ordered in February a total of (50 + 50) = 100.
\ No newline at end of file

From ea594c7f883c34da521f111e0a8ca4dfdfd69634 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:47:22 +0530
Subject: [PATCH 045/353] Update 1327. List the Products Ordered in a
 Period.SQL

---
 .../1327. List the Products Ordered in a Period.SQL  | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL
index a3f9c1d..68c34dc 100644
--- a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL	
+++ b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL	
@@ -82,4 +82,14 @@ Products with product_id = 1 is ordered in February a total of (60 + 70) = 130.
 Products with product_id = 2 is ordered in February a total of 80.
 Products with product_id = 3 is ordered in February a total of (2 + 3) = 5.
 Products with product_id = 4 was not ordered in February 2020.
-Products with product_id = 5 is ordered in February a total of (50 + 50) = 100.
\ No newline at end of file
+Products with product_id = 5 is ordered in February a total of (50 + 50) = 100.
+'''
+
+# Write your MySQL query statement below
+SELECT P.PRODUCT_NAME, SUM(O.UNIT) AS UNIT  
+FROM PRODUCTS P  
+INNER JOIN ORDERS O  
+ON P.PRODUCT_ID = O.PRODUCT_ID  
+WHERE O.ORDER_DATE BETWEEN '2020-02-01' AND '2020-02-29'  
+GROUP BY P.PRODUCT_NAME  
+HAVING SUM(O.UNIT) >= 100;
\ No newline at end of file

From 1d942a5d7ef5ff80ddb963cf6e853311096172bd Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:49:22 +0530
Subject: [PATCH 046/353] Create 1484. Group Sold Products By The Date.SQL

---
 .../1484. Group Sold Products By The Date.SQL                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL
new file mode 100644
index 0000000..e69de29

From 30cd2140526112de087159c1d0a038054312ee3c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:49:30 +0530
Subject: [PATCH 047/353] Update 1484. Group Sold Products By The Date.SQL

---
 .../1484. Group Sold Products By The Date.SQL | 56 +++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL
index e69de29..ccf0ab1 100644
--- a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL	
+++ b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL	
@@ -0,0 +1,56 @@
+1484. Group Sold Products By The Date
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table Activities:
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| sell_date   | date    |
+| product     | varchar |
++-------------+---------+
+There is no primary key (column with unique values) for this table. It may contain duplicates.
+Each row of this table contains the product name and the date it was sold in a market.
+ 
+
+Write a solution to find for each date the number of different products sold and their names.
+
+The sold products names for each date should be sorted lexicographically.
+
+Return the result table ordered by sell_date.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Activities table:
++------------+------------+
+| sell_date  | product     |
++------------+------------+
+| 2020-05-30 | Headphone  |
+| 2020-06-01 | Pencil     |
+| 2020-06-02 | Mask       |
+| 2020-05-30 | Basketball |
+| 2020-06-01 | Bible      |
+| 2020-06-02 | Mask       |
+| 2020-05-30 | T-Shirt    |
++------------+------------+
+Output: 
++------------+----------+------------------------------+
+| sell_date  | num_sold | products                     |
++------------+----------+------------------------------+
+| 2020-05-30 | 3        | Basketball,Headphone,T-shirt |
+| 2020-06-01 | 2        | Bible,Pencil                 |
+| 2020-06-02 | 1        | Mask                         |
++------------+----------+------------------------------+
+Explanation: 
+For 2020-05-30, Sold items were (Headphone, Basketball, T-shirt), we sort them lexicographically and separate them by a comma.
+For 2020-06-01, Sold items were (Pencil, Bible), we sort them lexicographically and separate them by a comma.
+For 2020-06-02, the Sold item is (Mask), we just return it.
\ No newline at end of file

From 93720734f89c8c71c3c2ea754cdea2a7e0877038 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:49:52 +0530
Subject: [PATCH 048/353] Update 1484. Group Sold Products By The Date.SQL

---
 .../1484. Group Sold Products By The Date.SQL          | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL
index ccf0ab1..b89bee9 100644
--- a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL	
+++ b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL	
@@ -53,4 +53,12 @@ Output:
 Explanation: 
 For 2020-05-30, Sold items were (Headphone, Basketball, T-shirt), we sort them lexicographically and separate them by a comma.
 For 2020-06-01, Sold items were (Pencil, Bible), we sort them lexicographically and separate them by a comma.
-For 2020-06-02, the Sold item is (Mask), we just return it.
\ No newline at end of file
+For 2020-06-02, the Sold item is (Mask), we just return it.
+
+
+
+select sell_date, count( DISTINCT product ) as num_sold ,
+    
+    GROUP_CONCAT( DISTINCT product order by product ASC separator ',' ) as products
+    
+        FROM Activities GROUP BY sell_date order by sell_date ASC;
\ No newline at end of file

From 7a369b6098e4142c90ecf267e6083c3829fe1acc Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:51:01 +0530
Subject: [PATCH 049/353] Create 176. Second Highest Salary.SQL

---
 LeetCode SQL 50 Solution/176. Second Highest Salary.SQL | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/176. Second Highest Salary.SQL

diff --git a/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL b/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL
new file mode 100644
index 0000000..e69de29

From 274504d9a3dfa0ef3a44098cf6fbd82333c8c854 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:51:32 +0530
Subject: [PATCH 050/353] Update 176. Second Highest Salary.SQL

---
 .../176. Second Highest Salary.SQL            | 57 +++++++++++++++++++
 1 file changed, 57 insertions(+)

diff --git a/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL b/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL
index e69de29..1698a45 100644
--- a/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL	
+++ b/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL	
@@ -0,0 +1,57 @@
+176. Second Highest Salary
+Solved
+Medium
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Employee
+
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| id          | int  |
+| salary      | int  |
++-------------+------+
+id is the primary key (column with unique values) for this table.
+Each row of this table contains information about the salary of an employee.
+ 
+
+Write a solution to find the second highest distinct salary from the Employee table. If there is no second highest salary, return null (return None in Pandas).
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Employee table:
++----+--------+
+| id | salary |
++----+--------+
+| 1  | 100    |
+| 2  | 200    |
+| 3  | 300    |
++----+--------+
+Output: 
++---------------------+
+| SecondHighestSalary |
++---------------------+
+| 200                 |
++---------------------+
+Example 2:
+
+Input: 
+Employee table:
++----+--------+
+| id | salary |
++----+--------+
+| 1  | 100    |
++----+--------+
+Output: 
++---------------------+
+| SecondHighestSalary |
++---------------------+
+| null                |
++---------------------+ 
\ No newline at end of file

From d3f57475d9be4f648e3ffe95b013691d4983b303 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:52:03 +0530
Subject: [PATCH 051/353] Update 176. Second Highest Salary.SQL

---
 .../176. Second Highest Salary.SQL                    | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL b/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL
index 1698a45..e8f8028 100644
--- a/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL	
+++ b/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL	
@@ -54,4 +54,13 @@ Output:
 | SecondHighestSalary |
 +---------------------+
 | null                |
-+---------------------+ 
\ No newline at end of file
++---------------------+ 
+
+WITH
+  RankedEmployees AS (
+    SELECT *, DENSE_RANK() OVER(ORDER BY salary DESC) AS `rank`
+    FROM Employee
+  )
+SELECT MAX(salary) AS SecondHighestSalary
+FROM RankedEmployees
+WHERE `rank` = 2;
\ No newline at end of file

From e231d028f571ff1f3ab13bb4a8d8e286f606a0f8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:53:19 +0530
Subject: [PATCH 052/353] Create 196. Delete Duplicate Emails.sql

---
 .../196. Delete Duplicate Emails.sql          | 53 +++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql
new file mode 100644
index 0000000..a81f41a
--- /dev/null
+++ b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql	
@@ -0,0 +1,53 @@
+196. Delete Duplicate Emails
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Person
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| email       | varchar |
++-------------+---------+
+id is the primary key (column with unique values) for this table.
+Each row of this table contains an email. The emails will not contain uppercase letters.
+ 
+
+Write a solution to delete all duplicate emails, keeping only one unique email with the smallest id.
+
+For SQL users, please note that you are supposed to write a DELETE statement and not a SELECT one.
+
+For Pandas users, please note that you are supposed to modify Person in place.
+
+After running your script, the answer shown is the Person table. The driver will first compile and run your piece of code and then show the Person table. The final order of the Person table does not matter.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Person table:
++----+------------------+
+| id | email            |
++----+------------------+
+| 1  | john@example.com |
+| 2  | bob@example.com  |
+| 3  | john@example.com |
++----+------------------+
+Output: 
++----+------------------+
+| id | email            |
++----+------------------+
+| 1  | john@example.com |
+| 2  | bob@example.com  |
++----+------------------+
+Explanation: john@example.com is repeated two times. We keep the row with the smallest Id = 1.
+
+
+

From ead49ee35b22341ca7771ab30e917fbb331645b9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:53:27 +0530
Subject: [PATCH 053/353] Update 196. Delete Duplicate Emails.sql

---
 LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql
index a81f41a..214afb4 100644
--- a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql	
+++ b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql	
@@ -51,3 +51,8 @@ Explanation: john@example.com is repeated two times. We keep the row with the sm
 
 
 
+# Write your MySQL query statement below
+# Write your MySQL query statement below
+DELETE p2 FROM Person p1 
+JOIN Person p2 
+ON p1.email = p2.email AND p1.id < p2.id;
\ No newline at end of file

From c120ccb8e1b4e700d0f3679db6ff6697a10e656c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:54:48 +0530
Subject: [PATCH 054/353] Create 1527. Patients With a Condition.sql

---
 .../1527. Patients With a Condition.sql       | 57 +++++++++++++++++++
 1 file changed, 57 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/1527. Patients With a Condition.sql

diff --git a/LeetCode SQL 50 Solution/1527. Patients With a Condition.sql b/LeetCode SQL 50 Solution/1527. Patients With a Condition.sql
new file mode 100644
index 0000000..e51a43e
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1527. Patients With a Condition.sql	
@@ -0,0 +1,57 @@
+1527. Patients With a Condition
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Patients
+
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| patient_id   | int     |
+| patient_name | varchar |
+| conditions   | varchar |
++--------------+---------+
+patient_id is the primary key (column with unique values) for this table.
+'conditions' contains 0 or more code separated by spaces. 
+This table contains information of the patients in the hospital.
+ 
+
+Write a solution to find the patient_id, patient_name, and conditions of the patients who have Type I Diabetes. Type I Diabetes always starts with DIAB1 prefix.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Patients table:
++------------+--------------+--------------+
+| patient_id | patient_name | conditions   |
++------------+--------------+--------------+
+| 1          | Daniel       | YFEV COUGH   |
+| 2          | Alice        |              |
+| 3          | Bob          | DIAB100 MYOP |
+| 4          | George       | ACNE DIAB100 |
+| 5          | Alain        | DIAB201      |
++------------+--------------+--------------+
+Output: 
++------------+--------------+--------------+
+| patient_id | patient_name | conditions   |
++------------+--------------+--------------+
+| 3          | Bob          | DIAB100 MYOP |
+| 4          | George       | ACNE DIAB100 | 
++------------+--------------+--------------+
+Explanation: Bob and George both have a condition that starts with DIAB1.
+
+
+
+# Write your MySQL query statement below
+SELECT patient_id, patient_name, conditions
+FROM Patients
+WHERE conditions LIKE 'DIAB1%' OR conditions LIKE '% DIAB1%'
\ No newline at end of file

From 4404a7646e4c582e4641158ef02a53dcd9f08683 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:57:12 +0530
Subject: [PATCH 055/353] Create 1667. Fix Names in a Table.sql

---
 LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql b/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql
new file mode 100644
index 0000000..e69de29

From 35ca53b686296f4ac2f95d840cb96731a6f10b42 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:57:25 +0530
Subject: [PATCH 056/353] Update 1667. Fix Names in a Table.sql

---
 .../1667. Fix Names in a Table.sql            | 44 +++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql b/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql
index e69de29..0730af5 100644
--- a/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql	
+++ b/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql	
@@ -0,0 +1,44 @@
+1667. Fix Names in a Table
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Users
+
++----------------+---------+
+| Column Name    | Type    |
++----------------+---------+
+| user_id        | int     |
+| name           | varchar |
++----------------+---------+
+user_id is the primary key (column with unique values) for this table.
+This table contains the ID and the name of the user. The name consists of only lowercase and uppercase characters.
+ 
+
+Write a solution to fix the names so that only the first character is uppercase and the rest are lowercase.
+
+Return the result table ordered by user_id.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Users table:
++---------+-------+
+| user_id | name  |
++---------+-------+
+| 1       | aLice |
+| 2       | bOB   |
++---------+-------+
+Output: 
++---------+-------+
+| user_id | name  |
++---------+-------+
+| 1       | Alice |
+| 2       | Bob   |
++---------+-------+
\ No newline at end of file

From 39d9bdbc93042f51258818834a76722757a60528 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 22:57:37 +0530
Subject: [PATCH 057/353] Update 1667. Fix Names in a Table.sql

---
 .../1667. Fix Names in a Table.sql                    | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql b/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql
index 0730af5..a092a51 100644
--- a/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql	
+++ b/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql	
@@ -41,4 +41,13 @@ Output:
 +---------+-------+
 | 1       | Alice |
 | 2       | Bob   |
-+---------+-------+
\ No newline at end of file
++---------+-------+
+
+
+
+
+# Write your MySQL query statement below
+SELECT user_id, 
+       CONCAT(UPPER(LEFT(name, 1)), LOWER(SUBSTRING(name, 2))) AS name 
+FROM Users 
+ORDER BY user_id;
\ No newline at end of file

From 53401384e294b8296f5fc5434b5562482939260b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:00:16 +0530
Subject: [PATCH 058/353] Create 585. Investments in 2016.sql

---
 .../585. Investments in 2016.sql              | 61 +++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/585. Investments in 2016.sql

diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016.sql b/LeetCode SQL 50 Solution/585. Investments in 2016.sql
new file mode 100644
index 0000000..427fb1e
--- /dev/null
+++ b/LeetCode SQL 50 Solution/585. Investments in 2016.sql	
@@ -0,0 +1,61 @@
+585. Investments in 2016
+Solved
+Medium
+Topics
+Companies
+Hint
+SQL Schema
+Pandas Schema
+Table: Insurance
+
++-------------+-------+
+| Column Name | Type  |
++-------------+-------+
+| pid         | int   |
+| tiv_2015    | float |
+| tiv_2016    | float |
+| lat         | float |
+| lon         | float |
++-------------+-------+
+pid is the primary key (column with unique values) for this table.
+Each row of this table contains information about one policy where:
+pid is the policyholder's policy ID.
+tiv_2015 is the total investment value in 2015 and tiv_2016 is the total investment value in 2016.
+lat is the latitude of the policy holder's city. It's guaranteed that lat is not NULL.
+lon is the longitude of the policy holder's city. It's guaranteed that lon is not NULL.
+ 
+
+Write a solution to report the sum of all total investment values in 2016 tiv_2016, for all policyholders who:
+
+have the same tiv_2015 value as one or more other policyholders, and
+are not located in the same city as any other policyholder (i.e., the (lat, lon) attribute pairs must be unique).
+Round tiv_2016 to two decimal places.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Insurance table:
++-----+----------+----------+-----+-----+
+| pid | tiv_2015 | tiv_2016 | lat | lon |
++-----+----------+----------+-----+-----+
+| 1   | 10       | 5        | 10  | 10  |
+| 2   | 20       | 20       | 20  | 20  |
+| 3   | 10       | 30       | 20  | 20  |
+| 4   | 10       | 40       | 40  | 40  |
++-----+----------+----------+-----+-----+
+Output: 
++----------+
+| tiv_2016 |
++----------+
+| 45.00    |
++----------+
+Explanation: 
+The first record in the table, like the last record, meets both of the two criteria.
+The tiv_2015 value 10 is the same as the third and fourth records, and its location is unique.
+
+The second record does not meet any of the two criteria. Its tiv_2015 is not like any other policyholders and its location is the same as the third record, which makes the third record fail, too.
+So, the result is the sum of tiv_2016 of the first and last record, which is 45.
\ No newline at end of file

From 97ad079196dd996889fbb63d4551904b9aeba0e2 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:01:05 +0530
Subject: [PATCH 059/353] Update 585. Investments in 2016.sql

---
 .../585. Investments in 2016.sql              | 25 +++++++++++++------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016.sql b/LeetCode SQL 50 Solution/585. Investments in 2016.sql
index 427fb1e..abad2d8 100644
--- a/LeetCode SQL 50 Solution/585. Investments in 2016.sql	
+++ b/LeetCode SQL 50 Solution/585. Investments in 2016.sql	
@@ -1,11 +1,5 @@
 585. Investments in 2016
-Solved
-Medium
-Topics
-Companies
-Hint
-SQL Schema
-Pandas Schema
+"""
 Table: Insurance
 
 +-------------+-------+
@@ -58,4 +52,19 @@ The first record in the table, like the last record, meets both of the two crite
 The tiv_2015 value 10 is the same as the third and fourth records, and its location is unique.
 
 The second record does not meet any of the two criteria. Its tiv_2015 is not like any other policyholders and its location is the same as the third record, which makes the third record fail, too.
-So, the result is the sum of tiv_2016 of the first and last record, which is 45.
\ No newline at end of file
+So, the result is the sum of tiv_2016 of the first and last record, which is 45.
+
+"""
+
+WITH
+  InsuranceWithCounts AS (
+    SELECT
+      tiv_2016,
+      COUNT(*) OVER(PARTITION by tiv_2015) AS tiv_2015_count,
+      COUNT(*) OVER(PARTITION by lat, lon) AS city_count
+    FROM Insurance
+  )
+SELECT ROUND(SUM(tiv_2016), 2) AS tiv_2016
+FROM InsuranceWithCounts
+WHERE tiv_2015_count > 1
+  AND city_count = 1;
\ No newline at end of file

From 18559ef3700c974d8fec3cce1fcaf1461d6e6385 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:10:23 +0530
Subject: [PATCH 060/353] Create 1341. Movie Rating.sql

---
 .../1341. Movie Rating.sql                    | 98 +++++++++++++++++++
 1 file changed, 98 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/1341. Movie Rating.sql

diff --git a/LeetCode SQL 50 Solution/1341. Movie Rating.sql b/LeetCode SQL 50 Solution/1341. Movie Rating.sql
new file mode 100644
index 0000000..bb083f8
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1341. Movie Rating.sql	
@@ -0,0 +1,98 @@
+1341. Movie Rating
+Solved
+Medium
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Movies
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| movie_id      | int     |
+| title         | varchar |
++---------------+---------+
+movie_id is the primary key (column with unique values) for this table.
+title is the name of the movie.
+ 
+
+Table: Users
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| user_id       | int     |
+| name          | varchar |
++---------------+---------+
+user_id is the primary key (column with unique values) for this table.
+The column 'name' has unique values.
+Table: MovieRating
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| movie_id      | int     |
+| user_id       | int     |
+| rating        | int     |
+| created_at    | date    |
++---------------+---------+
+(movie_id, user_id) is the primary key (column with unique values) for this table.
+This table contains the rating of a movie by a user in their review.
+created_at is the user's review date. 
+ 
+
+Write a solution to:
+
+Find the name of the user who has rated the greatest number of movies. In case of a tie, return the lexicographically smaller user name.
+Find the movie name with the highest average rating in February 2020. In case of a tie, return the lexicographically smaller movie name.
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Movies table:
++-------------+--------------+
+| movie_id    |  title       |
++-------------+--------------+
+| 1           | Avengers     |
+| 2           | Frozen 2     |
+| 3           | Joker        |
++-------------+--------------+
+Users table:
++-------------+--------------+
+| user_id     |  name        |
++-------------+--------------+
+| 1           | Daniel       |
+| 2           | Monica       |
+| 3           | Maria        |
+| 4           | James        |
++-------------+--------------+
+MovieRating table:
++-------------+--------------+--------------+-------------+
+| movie_id    | user_id      | rating       | created_at  |
++-------------+--------------+--------------+-------------+
+| 1           | 1            | 3            | 2020-01-12  |
+| 1           | 2            | 4            | 2020-02-11  |
+| 1           | 3            | 2            | 2020-02-12  |
+| 1           | 4            | 1            | 2020-01-01  |
+| 2           | 1            | 5            | 2020-02-17  | 
+| 2           | 2            | 2            | 2020-02-01  | 
+| 2           | 3            | 2            | 2020-03-01  |
+| 3           | 1            | 3            | 2020-02-22  | 
+| 3           | 2            | 4            | 2020-02-25  | 
++-------------+--------------+--------------+-------------+
+Output: 
++--------------+
+| results      |
++--------------+
+| Daniel       |
+| Frozen 2     |
++--------------+
+Explanation: 
+Daniel and Monica have rated 3 movies ("Avengers", "Frozen 2" and "Joker") but Daniel is smaller lexicographically.
+Frozen 2 and Joker have a rating average of 3.5 in February but Frozen 2 is smaller lexicographically.
+
+

From b02ef66c0d67d270149f8ed827b127e3df30f0c5 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:11:06 +0530
Subject: [PATCH 061/353] Update 1341. Movie Rating.sql

---
 .../1341. Movie Rating.sql                    | 29 +++++++++++++++----
 1 file changed, 23 insertions(+), 6 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1341. Movie Rating.sql b/LeetCode SQL 50 Solution/1341. Movie Rating.sql
index bb083f8..d5af570 100644
--- a/LeetCode SQL 50 Solution/1341. Movie Rating.sql	
+++ b/LeetCode SQL 50 Solution/1341. Movie Rating.sql	
@@ -1,10 +1,5 @@
 1341. Movie Rating
-Solved
-Medium
-Topics
-Companies
-SQL Schema
-Pandas Schema
+"""
 Table: Movies
 
 +---------------+---------+
@@ -95,4 +90,26 @@ Explanation:
 Daniel and Monica have rated 3 movies ("Avengers", "Frozen 2" and "Joker") but Daniel is smaller lexicographically.
 Frozen 2 and Joker have a rating average of 3.5 in February but Frozen 2 is smaller lexicographically.
 
+"""
 
+# Write your MySQL query statement below
+(
+    SELECT name AS results
+    FROM
+        Users
+        JOIN MovieRating USING (user_id)
+    GROUP BY user_id
+    ORDER BY COUNT(1) DESC, name
+    LIMIT 1
+)
+UNION ALL
+(
+    SELECT title
+    FROM
+        MovieRating
+        JOIN Movies USING (movie_id)
+    WHERE DATE_FORMAT(created_at, '%Y-%m') = '2020-02'
+    GROUP BY movie_id
+    ORDER BY AVG(rating) DESC, title
+    LIMIT 1
+);
\ No newline at end of file

From c06955e21caeda8b9095117b446ec3706d433b5d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:11:53 +0530
Subject: [PATCH 062/353] Create 1321. Restaurant Growth.sql

---
 LeetCode SQL 50 Solution/1321. Restaurant Growth.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1321. Restaurant Growth.sql

diff --git a/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql b/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql
new file mode 100644
index 0000000..e69de29

From 510ad9708262ce0634a9f4658ad96cb5d5b6af70 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:12:13 +0530
Subject: [PATCH 063/353] Update 1321. Restaurant Growth.sql

---
 .../1321. Restaurant Growth.sql               | 68 +++++++++++++++++++
 1 file changed, 68 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql b/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql
index e69de29..6c7127c 100644
--- a/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql	
+++ b/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql	
@@ -0,0 +1,68 @@
+321. Restaurant Growth
+Solved
+Medium
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Customer
+
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| customer_id   | int     |
+| name          | varchar |
+| visited_on    | date    |
+| amount        | int     |
++---------------+---------+
+In SQL,(customer_id, visited_on) is the primary key for this table.
+This table contains data about customer transactions in a restaurant.
+visited_on is the date on which the customer with ID (customer_id) has visited the restaurant.
+amount is the total paid by a customer.
+ 
+
+You are the restaurant owner and you want to analyze a possible expansion (there will be at least one customer every day).
+
+Compute the moving average of how much the customer paid in a seven days window (i.e., current day + 6 days before). average_amount should be rounded to two decimal places.
+
+Return the result table ordered by visited_on in ascending order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Customer table:
++-------------+--------------+--------------+-------------+
+| customer_id | name         | visited_on   | amount      |
++-------------+--------------+--------------+-------------+
+| 1           | Jhon         | 2019-01-01   | 100         |
+| 2           | Daniel       | 2019-01-02   | 110         |
+| 3           | Jade         | 2019-01-03   | 120         |
+| 4           | Khaled       | 2019-01-04   | 130         |
+| 5           | Winston      | 2019-01-05   | 110         | 
+| 6           | Elvis        | 2019-01-06   | 140         | 
+| 7           | Anna         | 2019-01-07   | 150         |
+| 8           | Maria        | 2019-01-08   | 80          |
+| 9           | Jaze         | 2019-01-09   | 110         | 
+| 1           | Jhon         | 2019-01-10   | 130         | 
+| 3           | Jade         | 2019-01-10   | 150         | 
++-------------+--------------+--------------+-------------+
+Output: 
++--------------+--------------+----------------+
+| visited_on   | amount       | average_amount |
++--------------+--------------+----------------+
+| 2019-01-07   | 860          | 122.86         |
+| 2019-01-08   | 840          | 120            |
+| 2019-01-09   | 840          | 120            |
+| 2019-01-10   | 1000         | 142.86         |
++--------------+--------------+----------------+
+Explanation: 
+1st moving average from 2019-01-01 to 2019-01-07 has an average_amount of (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86
+2nd moving average from 2019-01-02 to 2019-01-08 has an average_amount of (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120
+3rd moving average from 2019-01-03 to 2019-01-09 has an average_amount of (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120
+4th moving average from 2019-01-04 to 2019-01-10 has an average_amount of (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86
+
+

From 748fc9ec9de422b69388e30284fd3974467a40c4 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:13:00 +0530
Subject: [PATCH 064/353] Update 1321. Restaurant Growth.sql

---
 .../1321. Restaurant Growth.sql               | 39 +++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql b/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql
index 6c7127c..384399e 100644
--- a/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql	
+++ b/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql	
@@ -66,3 +66,42 @@ Explanation:
 4th moving average from 2019-01-04 to 2019-01-10 has an average_amount of (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86
 
 
+Solution 1:
+
+# Write your MySQL query statement below
+WITH
+    t AS (
+        SELECT
+            visited_on,
+            SUM(amount) OVER (
+                ORDER BY visited_on
+                ROWS 6 PRECEDING
+            ) AS amount,
+            RANK() OVER (
+                ORDER BY visited_on
+                ROWS 6 PRECEDING
+            ) AS rk
+        FROM
+            (
+                SELECT visited_on, SUM(amount) AS amount
+                FROM Customer
+                GROUP BY visited_on
+            ) AS tt
+    )
+SELECT visited_on, amount, ROUND(amount / 7, 2) AS average_amount
+FROM t
+WHERE rk > 6;
+
+
+Solution 2:
+# Write your MySQL query statement below
+SELECT
+    a.visited_on,
+    SUM(b.amount) AS amount,
+    ROUND(SUM(b.amount) / 7, 2) AS average_amount
+FROM
+    (SELECT DISTINCT visited_on FROM customer) AS a
+    JOIN customer AS b ON DATEDIFF(a.visited_on, b.visited_on) BETWEEN 0 AND 6
+WHERE a.visited_on >= (SELECT MIN(visited_on) FROM customer) + 6
+GROUP BY 1
+ORDER BY 1;
\ No newline at end of file

From fef63874c0b2ffaadd8aee19bc8019f50cc511ca Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:13:48 +0530
Subject: [PATCH 065/353] Create 626. Exchange Seats.sql

---
 LeetCode SQL 50 Solution/626. Exchange Seats.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/626. Exchange Seats.sql

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats.sql b/LeetCode SQL 50 Solution/626. Exchange Seats.sql
new file mode 100644
index 0000000..e69de29

From bcc48be3fc718d67d5e69f68f1513b4be35109a3 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:14:01 +0530
Subject: [PATCH 066/353] Update 626. Exchange Seats.sql

---
 .../626. Exchange Seats.sql                   | 55 +++++++++++++++++++
 1 file changed, 55 insertions(+)

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats.sql b/LeetCode SQL 50 Solution/626. Exchange Seats.sql
index e69de29..8f2ad0d 100644
--- a/LeetCode SQL 50 Solution/626. Exchange Seats.sql	
+++ b/LeetCode SQL 50 Solution/626. Exchange Seats.sql	
@@ -0,0 +1,55 @@
+626. Exchange Seats
+Solved
+Medium
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Seat
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| student     | varchar |
++-------------+---------+
+id is the primary key (unique value) column for this table.
+Each row of this table indicates the name and the ID of a student.
+The ID sequence always starts from 1 and increments continuously.
+ 
+
+Write a solution to swap the seat id of every two consecutive students. If the number of students is odd, the id of the last student is not swapped.
+
+Return the result table ordered by id in ascending order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Seat table:
++----+---------+
+| id | student |
++----+---------+
+| 1  | Abbot   |
+| 2  | Doris   |
+| 3  | Emerson |
+| 4  | Green   |
+| 5  | Jeames  |
++----+---------+
+Output: 
++----+---------+
+| id | student |
++----+---------+
+| 1  | Doris   |
+| 2  | Abbot   |
+| 3  | Green   |
+| 4  | Emerson |
+| 5  | Jeames  |
++----+---------+
+Explanation: 
+Note that if the number of students is odd, there is no need to change the last one's seat.
+
+

From 574fe8bbf4206d1282887ac7e314969457ed3e1e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:14:20 +0530
Subject: [PATCH 067/353] Update 626. Exchange Seats.sql

---
 LeetCode SQL 50 Solution/626. Exchange Seats.sql | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats.sql b/LeetCode SQL 50 Solution/626. Exchange Seats.sql
index 8f2ad0d..d4ba56e 100644
--- a/LeetCode SQL 50 Solution/626. Exchange Seats.sql	
+++ b/LeetCode SQL 50 Solution/626. Exchange Seats.sql	
@@ -51,5 +51,15 @@ Output:
 +----+---------+
 Explanation: 
 Note that if the number of students is odd, there is no need to change the last one's seat.
+'''
 
+# MYSQL Query Accepted
 
+SELECT ( CASE
+            WHEN id%2 != 0 AND id != counts THEN id+1
+            WHEN id%2 != 0 AND id = counts THEN id
+            ELSE id-1
+        END) AS id, student
+FROM seat, (select count(*) as counts from seat) 
+AS seat_counts
+ORDER BY id ASC
\ No newline at end of file

From 62345d5212f0a9a87b95074a65f71aa396c437ef Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:17:04 +0530
Subject: [PATCH 068/353] Create 602. Friend Requests II.sql

---
 .../602. Friend Requests II.sql               | 55 +++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/602. Friend Requests II.sql

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II.sql b/LeetCode SQL 50 Solution/602. Friend Requests II.sql
new file mode 100644
index 0000000..48592ae
--- /dev/null
+++ b/LeetCode SQL 50 Solution/602. Friend Requests II.sql	
@@ -0,0 +1,55 @@
+602. Friend Requests II: Who Has the Most Friends
+Solved
+Medium
+Topics
+Companies
+Hint
+SQL Schema
+Pandas Schema
+Table: RequestAccepted
+
++----------------+---------+
+| Column Name    | Type    |
++----------------+---------+
+| requester_id   | int     |
+| accepter_id    | int     |
+| accept_date    | date    |
++----------------+---------+
+(requester_id, accepter_id) is the primary key (combination of columns with unique values) for this table.
+This table contains the ID of the user who sent the request, the ID of the user who received the request, and the date when the request was accepted.
+ 
+
+Write a solution to find the people who have the most friends and the most friends number.
+
+The test cases are generated so that only one person has the most friends.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+RequestAccepted table:
++--------------+-------------+-------------+
+| requester_id | accepter_id | accept_date |
++--------------+-------------+-------------+
+| 1            | 2           | 2016/06/03  |
+| 1            | 3           | 2016/06/08  |
+| 2            | 3           | 2016/06/08  |
+| 3            | 4           | 2016/06/09  |
++--------------+-------------+-------------+
+Output: 
++----+-----+
+| id | num |
++----+-----+
+| 3  | 3   |
++----+-----+
+Explanation: 
+The person with id 3 is a friend of people 1, 2, and 4, so he has three friends in total, which is the most number than any others.
+ 
+
+Follow up: In the real world, multiple people could have the same most number of friends. Could you find all these people in this case?
+
+
+

From 0b2ec0603cf4b71e6546722cd4a8bd424b29f65e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:17:22 +0530
Subject: [PATCH 069/353] Update 602. Friend Requests II.sql

---
 LeetCode SQL 50 Solution/602. Friend Requests II.sql | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II.sql b/LeetCode SQL 50 Solution/602. Friend Requests II.sql
index 48592ae..99c5e2c 100644
--- a/LeetCode SQL 50 Solution/602. Friend Requests II.sql	
+++ b/LeetCode SQL 50 Solution/602. Friend Requests II.sql	
@@ -53,3 +53,15 @@ Follow up: In the real world, multiple people could have the same most number of
 
 
 
+# Write your MySQL query statement below
+WITH
+    T AS (
+        SELECT requester_id, accepter_id FROM RequestAccepted
+        UNION ALL
+        SELECT accepter_id, requester_id FROM RequestAccepted
+    )
+SELECT requester_id AS id, COUNT(1) AS num
+FROM T
+GROUP BY 1
+ORDER BY 2 DESC
+LIMIT 1;
\ No newline at end of file

From 9a83bc899efef2cd5f9cc293e4de211a65782b8d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:22:12 +0530
Subject: [PATCH 070/353] Create 1204. Last Person to Fit in the Bus.md

---
 .../1204. Last Person to Fit in the Bus.md    | 77 +++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.md

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.md b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.md
new file mode 100644
index 0000000..24fb197
--- /dev/null
+++ b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.md	
@@ -0,0 +1,77 @@
+# 1204. Last Person to Fit in the Bus
+```
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| person_id   | int     |
+| person_name | varchar |
+| weight      | int     |
+| turn        | int     |
++-------------+---------+
+
+```
+
+person_id column contains unique values.
+This table has the information about all people waiting for a bus.
+The person_id and turn columns will contain all numbers from 1 to n, where n is the number of rows in the table.
+turn determines the order of which the people will board the bus, where turn=1 denotes the first person to board and turn=n denotes the last person to board.
+weight is the weight of the person in kilograms.
+ 
+
+There is a queue of people waiting to board a bus. However, the bus has a weight limit of 1000 kilograms, so there may be some people who cannot board.
+
+Write a solution to find the person_name of the last person that can fit on the bus without exceeding the weight limit. The test cases are generated such that the first person does not exceed the weight limit.
+
+Note that only one person can board the bus at any given turn.
+
+## The result format is in the following example.
+
+```table[]
+
+Example 1:
+
+Input: 
+Queue table:
++-----------+-------------+--------+------+
+| person_id | person_name | weight | turn |
++-----------+-------------+--------+------+
+| 5         | Alice       | 250    | 1    |
+| 4         | Bob         | 175    | 5    |
+| 3         | Alex        | 350    | 2    |
+| 6         | John Cena   | 400    | 3    |
+| 1         | Winston     | 500    | 6    |
+| 2         | Marie       | 200    | 4    |
++-----------+-------------+--------+------+
+Output: 
++-------------+
+| person_name |
++-------------+
+| John Cena   |
++-------------+
+Explanation: The folowing table is ordered by the turn for simplicity.
++------+----+-----------+--------+--------------+
+| Turn | ID | Name      | Weight | Total Weight |
++------+----+-----------+--------+--------------+
+| 1    | 5  | Alice     | 250    | 250          |
+| 2    | 3  | Alex      | 350    | 600          |
+| 3    | 6  | John Cena | 400    | 1000         | (last person to board)
+| 4    | 2  | Marie     | 200    | 1200         | (cannot board)
+| 5    | 4  | Bob       | 175    | ___          |
+| 6    | 1  | Winston   | 500    | ___          |
++------+----+-----------+--------+--------------+
+```
+
+```SQL[]
+# Write your MySQL query statement below
+# Write your MySQL query statement below
+SELECT a.person_name
+FROM
+    Queue AS a,
+    Queue AS b
+WHERE a.turn >= b.turn
+GROUP BY a.person_id
+HAVING SUM(b.weight) <= 1000
+ORDER BY a.turn DESC
+LIMIT 1;
+```
\ No newline at end of file

From 1a1cf5749f8e760a6ff90b99cf59466d1b61d5cf Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 23 Mar 2025 23:25:38 +0530
Subject: [PATCH 071/353] Create 610. Triangle Judgement.sql

---
 .../610. Triangle Judgement.sql               | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/610. Triangle Judgement.sql

diff --git a/LeetCode SQL 50 Solution/610. Triangle Judgement.sql b/LeetCode SQL 50 Solution/610. Triangle Judgement.sql
new file mode 100644
index 0000000..3e353f0
--- /dev/null
+++ b/LeetCode SQL 50 Solution/610. Triangle Judgement.sql	
@@ -0,0 +1,52 @@
+610. Triangle Judgement
+Solved
+Easy
+Topics
+Companies
+SQL Schema
+Pandas Schema
+Table: Triangle
+
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| x           | int  |
+| y           | int  |
+| z           | int  |
++-------------+------+
+In SQL, (x, y, z) is the primary key column for this table.
+Each row of this table contains the lengths of three line segments.
+ 
+
+Report for every three line segments whether they can form a triangle.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+ 
+
+Example 1:
+
+Input: 
+Triangle table:
++----+----+----+
+| x  | y  | z  |
++----+----+----+
+| 13 | 15 | 30 |
+| 10 | 20 | 15 |
++----+----+----+
+Output: 
++----+----+----+----------+
+| x  | y  | z  | triangle |
++----+----+----+----------+
+| 13 | 15 | 30 | No       |
+| 10 | 20 | 15 | Yes      |
++----+----+----+----------+
+
+
+# Write your MySQL query statement below
+SELECT
+  *,
+  IF(x + y > z AND x + z > y AND y + z > x, 'Yes', 'No') AS triangle
+FROM Triangle;
\ No newline at end of file

From f545ac5c0780e56338e24ac06dd9d5404785680c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 01:50:37 +0530
Subject: [PATCH 072/353] ef

---
 .gitignore |  1 +
 README.md  | 13 +++++++++++++
 2 files changed, 14 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b4c093a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+profile.md 
\ No newline at end of file
diff --git a/README.md b/README.md
index 3fc30a1..752731c 100644
--- a/README.md
+++ b/README.md
@@ -43,3 +43,16 @@ Problem 3: Description of the problem and the SQL solution.
 ### Contributing:-
 
 Feel free to fork this repository, suggest improvements, or submit your own solutions via pull requests. Let's learn and grow together!
+
+
+### Progress
+<progress value="75" max="100"></progress>
+### Progress
+🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜⬜ 75%
+
+
+
+update-date.yml
+
+
+## Last Updated: 2023-10-25
\ No newline at end of file

From 828a0d44244f98819625b0b2a2d00f2969cb10ce Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 01:51:41 +0530
Subject: [PATCH 073/353] Create update-date.yml

---
 .github/workflows/update-date.yml | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 .github/workflows/update-date.yml

diff --git a/.github/workflows/update-date.yml b/.github/workflows/update-date.yml
new file mode 100644
index 0000000..309720d
--- /dev/null
+++ b/.github/workflows/update-date.yml
@@ -0,0 +1,29 @@
+name: Update Date in README
+
+on:
+  schedule:
+    - cron: "0 0 * * *"  # Runs daily at midnight UTC
+  workflow_dispatch:     # Allows manual triggering
+
+jobs:
+  update-date:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+
+      - name: Update date in README.md
+        run: |
+          # Get current date (customize format below)
+          CURRENT_DATE=$(date +'%Y-%m-%d')
+          
+          # Replace placeholder with the current date
+          sed -i "s/<!-- DATE -->/${CURRENT_DATE}/g" README.md
+
+      - name: Commit and push changes
+        run: |
+          git config user.name "github-actions"
+          git config user.email "actions@github.com"
+          git add README.md
+          git commit -m "Update date in README" || echo "No changes to commit"
+          git push

From 171527dfbdc55cbb5bf983114c41da27a85b9886 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 14:55:13 +0530
Subject: [PATCH 074/353] Update README.md

---
 README.md | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/README.md b/README.md
index 752731c..3696c07 100644
--- a/README.md
+++ b/README.md
@@ -45,14 +45,6 @@ Problem 3: Description of the problem and the SQL solution.
 Feel free to fork this repository, suggest improvements, or submit your own solutions via pull requests. Let's learn and grow together!
 
 
-### Progress
-<progress value="75" max="100"></progress>
 ### Progress
 🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜⬜ 75%
 
-
-
-update-date.yml
-
-
-## Last Updated: 2023-10-25
\ No newline at end of file

From 2585b7adfd954842eeffa1615cb1125ec960d227 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 15:01:34 +0530
Subject: [PATCH 075/353] Create CONTTRIBUTE.MD

---
 CONTTRIBUTE.MD | 170 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 170 insertions(+)
 create mode 100644 CONTTRIBUTE.MD

diff --git a/CONTTRIBUTE.MD b/CONTTRIBUTE.MD
new file mode 100644
index 0000000..7bc5c5c
--- /dev/null
+++ b/CONTTRIBUTE.MD
@@ -0,0 +1,170 @@
+# Contributing to LeetCode_Daily_Task
+
+Thank you for your interest in contributing to **LeetCode_Daily_Task**!  
+
+---
+
+## Table of Contents
+- [Contributing to LeetCode\_Daily\_Task](#contributing-to-leetcode_daily_task)
+  - [Table of Contents](#table-of-contents)
+  - [How to Contribute](#how-to-contribute)
+  - [Impressive Contributions \& Highlights](#impressive-contributions--highlights)
+  - [Contributor Recognition Process](#contributor-recognition-process)
+  - [Share Your Thoughts](#share-your-thoughts)
+  - [Issue Reporting](#issue-reporting)
+  - [Pull Request Process](#pull-request-process)
+  - [Coding Guidelines](#coding-guidelines)
+  - [Commit Message Guidelines](#commit-message-guidelines)
+  - [Acknowledgments](#acknowledgments)
+
+---
+
+## How to Contribute
+
+1. **Fork the Repository:**  
+   Click the **Fork** button on the repository page to create your own copy.
+
+2. **Clone Your Fork:**  
+   ```bash
+   git clone https://github.com/your-username/LeetCode_Daily_Task.git
+   ```
+
+3. **Create a Branch:**  
+   Create a new branch for your feature, solution, or bug fix.
+   ```bash
+   git checkout -b feature/your-feature-name
+   ```
+
+4. **Implement Your Changes:**  
+   - Add your solution or enhancements.
+   - Include any discussion points or explanations in your code comments or PR description.
+   - Follow the coding guidelines (see below).
+   - Update or add test cases as needed.
+
+5. **Commit Your Changes:**  
+   Use clear and descriptive commit messages (see Commit Message Guidelines).
+
+6. **Open a Pull Request:**  
+   Submit a pull request (PR) with a clear title and description outlining your changes, and reference any related issues.
+
+---
+
+## Impressive Contributions & Highlights
+
+We love to see innovative and creative solutions! This section is dedicated to showcasing impressive contributions that go beyond the daily task:
+
+- **What to Share:**  
+  - **Enhanced Solutions:** Share optimized, creative, or particularly elegant approaches to LeetCode problems.
+  - **Detailed Explanations:** Provide in-depth explanations and insights that help others understand your reasoning.
+  - **Additional Context:** Include performance comparisons, alternative methods, or advanced techniques.
+  
+- **How to Get Recognized:**  
+  The first contributor to submit an impressive solution for a given challenge will have their name highlighted in this section. For example:
+  ```md
+  **Impressive Contribution by:** @username
+  ```
+  To be recognized, please send an email with your GitHub username and Gmail address to **optimismeducator@gmail.com**. This helps us track and celebrate innovative contributions.
+
+- **Discussion:**  
+  Engage with others by discussing alternative approaches or asking questions in the issue tracker or PR comments. Your input can help refine the solution and inspire further improvements.
+
+*Feel free to update this section with new highlights, alternative methods, or breakthrough approaches that push the boundaries of problem-solving.*
+
+---
+
+## Contributor Recognition Process
+
+- **How It Works:**  
+  Exceptional contributions are recognized to encourage innovative thinking.
+  
+- **Steps to Get Recognized:**  
+  1. Submit your solution via a PR.
+  2. Once your PR is merged, send an email with your GitHub username and Gmail address to **optimismeducator@gmail.com**.
+  3. Your details will be added to the "Impressive Contributions & Highlights" section for that challenge.
+
+This process helps build an active community and rewards creativity and excellence.
+
+---
+
+## Share Your Thoughts
+
+We welcome your feedback and ideas!
+- **Questions & Suggestions:** Open an issue or start a discussion if you have ideas for improvements, new challenges, or if you need help.
+- **Solution Explanations:** Share your approach and insights to help others understand the problem better.
+- **Improvements:** If you see a more efficient or elegant solution, feel free to contribute your version.
+
+---
+
+## Issue Reporting
+
+- **Found a Bug or Have a Suggestion?**  
+  Open a new issue on GitHub with a descriptive title and details on how to reproduce the problem or suggest improvements.
+- **Include the Following Information:**  
+  - A clear explanation of the issue.
+  - Steps to reproduce the problem.
+  - Environment details if applicable (e.g., OS, specific LeetCode problem IDs).
+
+---
+
+## Pull Request Process
+
+1. **Ensure Tests Pass:**  
+   Verify that your changes pass all existing tests. If you’re adding new functionality, please include corresponding tests.
+
+2. **Provide a Descriptive Title:**  
+   Your PR title should succinctly describe your changes (e.g., "Add optimized solution for Two Sum challenge").
+
+3. **Detailed Description:**  
+   In your PR, include:
+   - The purpose of your changes.
+   - Instructions on how to test your changes.
+   - Additional context, screenshots, or references to related issues.
+   - If you're submitting an impressive solution, include a note so we can update the recognition section.
+
+4. **Review & Feedback:**  
+   Your PR will be reviewed by the maintainers. Please address any feedback or requested changes promptly.
+
+---
+
+## Coding Guidelines
+
+- **Consistent Formatting:**  
+  Follow the repository’s established style for code formatting, naming conventions, and folder structure.
+
+- **Comment Your Code:**  
+  Provide clear comments explaining your logic, especially for complex solutions.
+
+- **Efficiency and Readability:**  
+  Ensure your solutions are optimized for performance and are easy to understand.
+
+- **Documentation:**  
+  Update or add documentation if your changes affect existing functionality.
+
+---
+
+## Commit Message Guidelines
+
+- **Short & Descriptive:**  
+  The first line of your commit message should be a brief summary (50 characters or less).
+
+- **Imperative Mood:**  
+  Use the imperative mood (e.g., "Add", "Fix", "Update").
+
+- **Detailed Description:**  
+  If necessary, include a detailed description after the first line, separated by a blank line.
+
+Example:
+```txt
+Add solution for Two Sum problem
+
+This commit introduces an optimized solution for the Two Sum problem, along with test cases and documentation.
+```
+
+---
+
+## Acknowledgments
+
+Thank you for contributing to **LeetCode_Daily_Task**!  
+Your help and commitment make this project a valuable resource for the community. If you have any questions or need further assistance, feel free to open an issue or contact the maintainers.
+
+> ## Happy coding and happy LeetCoding! 🚀

From d3e46a0cc3c67cd47cbbf4780aa9a8be7d344892 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 15:06:06 +0530
Subject: [PATCH 076/353] Update CONTTRIBUTE.MD

---
 CONTTRIBUTE.MD | 2 --
 1 file changed, 2 deletions(-)

diff --git a/CONTTRIBUTE.MD b/CONTTRIBUTE.MD
index 7bc5c5c..7a1832c 100644
--- a/CONTTRIBUTE.MD
+++ b/CONTTRIBUTE.MD
@@ -68,7 +68,6 @@ We love to see innovative and creative solutions! This section is dedicated to s
 - **Discussion:**  
   Engage with others by discussing alternative approaches or asking questions in the issue tracker or PR comments. Your input can help refine the solution and inspire further improvements.
 
-*Feel free to update this section with new highlights, alternative methods, or breakthrough approaches that push the boundaries of problem-solving.*
 
 ---
 
@@ -82,7 +81,6 @@ We love to see innovative and creative solutions! This section is dedicated to s
   2. Once your PR is merged, send an email with your GitHub username and Gmail address to **optimismeducator@gmail.com**.
   3. Your details will be added to the "Impressive Contributions & Highlights" section for that challenge.
 
-This process helps build an active community and rewards creativity and excellence.
 
 ---
 

From 24c319947fb775b3bde3bd0681bdc261b58c82fd Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 15:07:33 +0530
Subject: [PATCH 077/353] Delete contributing.md

---
 contributing.md | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 contributing.md

diff --git a/contributing.md b/contributing.md
deleted file mode 100644
index 65c67f9..0000000
--- a/contributing.md
+++ /dev/null
@@ -1 +0,0 @@
-# Contribute everyone

From 15b0a872eaba6a94acda0d7bf59e9c097f9033db Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 15:12:47 +0530
Subject: [PATCH 078/353] adding bedge

---
 README.md      |   2 ++
 img/sql 50.gif | Bin 0 -> 1036362 bytes
 2 files changed, 2 insertions(+)
 create mode 100644 img/sql 50.gif

diff --git a/README.md b/README.md
index 3696c07..9c7ff2d 100644
--- a/README.md
+++ b/README.md
@@ -44,6 +44,8 @@ Problem 3: Description of the problem and the SQL solution.
 
 Feel free to fork this repository, suggest improvements, or submit your own solutions via pull requests. Let's learn and grow together!
 
+## Solved LeetCode SQL 50 Question
+<img src="./img/sql 50.gif">
 
 ### Progress
 🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜⬜ 75%
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HcmV?d00001


From 55d198531c74dfb8e3f310c7f1721679050936ff Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 15:17:05 +0530
Subject: [PATCH 079/353] Update README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 9c7ff2d..1c7497f 100644
--- a/README.md
+++ b/README.md
@@ -45,6 +45,7 @@ Problem 3: Description of the problem and the SQL solution.
 Feel free to fork this repository, suggest improvements, or submit your own solutions via pull requests. Let's learn and grow together!
 
 ## Solved LeetCode SQL 50 Question
+
 <img src="./img/sql 50.gif">
 
 ### Progress

From e03ed1e0ad6f903ca3aaba2f2455b03c87f9cf18 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 15:18:59 +0530
Subject: [PATCH 080/353] adding qr

---
 README.md     |   1 +
 img/sql50.png | Bin 0 -> 5915 bytes
 2 files changed, 1 insertion(+)
 create mode 100644 img/sql50.png

diff --git a/README.md b/README.md
index 1c7497f..842b454 100644
--- a/README.md
+++ b/README.md
@@ -47,6 +47,7 @@ Feel free to fork this repository, suggest improvements, or submit your own solu
 ## Solved LeetCode SQL 50 Question
 
 <img src="./img/sql 50.gif">
+<img src="./img/sql50.png">
 
 ### Progress
 🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜⬜ 75%
diff --git a/img/sql50.png b/img/sql50.png
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From f0bb144573b2a982708cb25ec887b96199f457ef Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 24 Mar 2025 15:20:11 +0530
Subject: [PATCH 081/353] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 842b454..52adbb5 100644
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ Feel free to fork this repository, suggest improvements, or submit your own solu
 ## Solved LeetCode SQL 50 Question
 
 <img src="./img/sql 50.gif">
-<img src="./img/sql50.png">
+<img height="200px" src="./img/sql50.png">
 
 ### Progress
 🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜⬜ 75%

From 3cbd51109dfd711fe08e2ac7e43c0b7821918112 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:24:34 +0530
Subject: [PATCH 082/353] ciorrect

---
 .../176. Second Highest Salary.SQL                                | 0
 .../176. Second heighest salary.sql                               | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 176. Second heighest salary}/176. Second Highest Salary.SQL (100%)
 rename LeetCode SQL 50 Solution/{ => 176. Second heighest salary}/176. Second heighest salary.sql (100%)

diff --git a/LeetCode SQL 50 Solution/176. Second Highest Salary.SQL b/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.SQL
similarity index 100%
rename from LeetCode SQL 50 Solution/176. Second Highest Salary.SQL
rename to LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.SQL
diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary.sql b/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second heighest salary.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/176. Second heighest salary.sql
rename to LeetCode SQL 50 Solution/176. Second heighest salary/176. Second heighest salary.sql

From c692400846ed9708862228b9235d1ea6ab4658fd Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:26:15 +0530
Subject: [PATCH 083/353] 196. Delete Duplicate Emails

---
 .../{ => 177. Nth Highest Salary}/177. Nth Highest Salary.sql     | 0
 .../{ => 180. Consecutive Numbers}/180. Consecutive Numbers.sql   | 0
 .../185. Department Top Three Salaries.md                         | 0
 .../185. Department Top Three Salaries.sql                        | 0
 .../196. Delete Duplicate Emails.sql                              | 0
 5 files changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 177. Nth Highest Salary}/177. Nth Highest Salary.sql (100%)
 rename LeetCode SQL 50 Solution/{ => 180. Consecutive Numbers}/180. Consecutive Numbers.sql (100%)
 rename LeetCode SQL 50 Solution/{ => 185. Department Top Three Salaries}/185. Department Top Three Salaries.md (100%)
 rename LeetCode SQL 50 Solution/{ => 185. Department Top Three Salaries}/185. Department Top Three Salaries.sql (100%)
 rename LeetCode SQL 50 Solution/{ => 196. Delete Duplicate Emails}/196. Delete Duplicate Emails.sql (100%)

diff --git a/LeetCode SQL 50 Solution/177. Nth Highest Salary.sql b/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/177. Nth Highest Salary.sql
rename to LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.sql
diff --git a/LeetCode SQL 50 Solution/180. Consecutive Numbers.sql b/LeetCode SQL 50 Solution/180. Consecutive Numbers/180. Consecutive Numbers.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/180. Consecutive Numbers.sql
rename to LeetCode SQL 50 Solution/180. Consecutive Numbers/180. Consecutive Numbers.sql
diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md
similarity index 100%
rename from LeetCode SQL 50 Solution/185. Department Top Three Salaries.md
rename to LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md
diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries.sql b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/185. Department Top Three Salaries.sql
rename to LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.sql
diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/196. Delete Duplicate Emails.sql
rename to LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql

From ba98d4bb5e2ae8edbd1cfdc27356c12afc6339b0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:26:45 +0530
Subject: [PATCH 084/353] 550. Game Play Analysis IV

---
 .../550. Game Play Analysis IV.sql                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
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diff --git a/LeetCode SQL 50 Solution/550. Game Play Analysis IV.sql b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/550. Game Play Analysis IV.sql
rename to LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.sql

From bdc6475aa08211092532eb6f6457d6f3701dd879 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:27:08 +0530
Subject: [PATCH 085/353] 570. Managers with at Least 5 Direct Reports

---
 .../570. Managers with at Least 5 Direct Reports.sql              | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 570. Managers with at Least 5 Direct Reports}/570. Managers with at Least 5 Direct Reports.sql (100%)

diff --git a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports.sql b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports.sql
rename to LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.sql

From c52704aa0c97dcd47e9e586f2d903e6c929b4821 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:27:26 +0530
Subject: [PATCH 086/353] 584. Find Customer Referee

---
 .../584. Find Customer Referee.sql                                | 0
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 rename LeetCode SQL 50 Solution/{ => 584. Find Customer Referee}/584. Find Customer Referee.sql (100%)

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee.sql b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/584. Find Customer Referee.sql
rename to LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.sql

From 1ddb9babb2e580609e034a38248d16fff4e4407e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:27:43 +0530
Subject: [PATCH 087/353] 585. Investments in 2016

---
 .../{ => 585. Investments in 2016}/585. Investments in 2016.sql   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 585. Investments in 2016}/585. Investments in 2016.sql (100%)

diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016.sql b/LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/585. Investments in 2016.sql
rename to LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.sql

From 41c4fd691e6b1acb342ade3d7f56d2cc7c248ec7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:28:10 +0530
Subject: [PATCH 088/353] 595. Big Countries

---
 .../{ => 595. Big Countries}/595. Big Countries.sql               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 595. Big Countries}/595. Big Countries.sql (100%)

diff --git a/LeetCode SQL 50 Solution/595. Big Countries.sql b/LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/595. Big Countries.sql
rename to LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.sql

From 26b188c583a58783eca0ef73459b54c498b621e0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:28:30 +0530
Subject: [PATCH 089/353] 596. Classes More Than 5 Students

---
 .../596. Classes More Than 5 Students.sql                         | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 596. Classes More Than 5 Students}/596. Classes More Than 5 Students.sql (100%)

diff --git a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students.sql b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/596. Classes More Than 5 Students.sql
rename to LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.sql

From 3a6fc991c02d493e4ff8e02e73f6a85d0ada0669 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:28:55 +0530
Subject: [PATCH 090/353] 602. Friend Requests II

---
 .../{ => 602. Friend Requests II}/602. Friend Requests II.sql     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 602. Friend Requests II}/602. Friend Requests II.sql (100%)

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II.sql b/LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/602. Friend Requests II.sql
rename to LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.sql

From 6ed46c5dd44c0eec2538f4516337a7cd4dc6bf79 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:29:10 +0530
Subject: [PATCH 091/353] 610. Triangle Judgement

---
 .../{ => 610. Triangle Judgement}/610. Triangle Judgement.sql     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 610. Triangle Judgement}/610. Triangle Judgement.sql (100%)

diff --git a/LeetCode SQL 50 Solution/610. Triangle Judgement.sql b/LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/610. Triangle Judgement.sql
rename to LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.sql

From b3cfa2484188d1a410f6040530cf362b7c2f76ee Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:29:46 +0530
Subject: [PATCH 092/353] 619. Biggest Single Number

---
 .../619. Biggest Single Number.sql                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 619. Biggest Single Number}/619. Biggest Single Number.sql (100%)

diff --git a/LeetCode SQL 50 Solution/619. Biggest Single Number.sql b/LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/619. Biggest Single Number.sql
rename to LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.sql

From 5f6201bf7f5555e489c20669263b7d454db8e768 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:30:04 +0530
Subject: [PATCH 093/353] 620. Not Boring Movies

---
 .../{ => 620. Not Boring Movies}/620. Not Boring Movies.sql       | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 620. Not Boring Movies}/620. Not Boring Movies.sql (100%)

diff --git a/LeetCode SQL 50 Solution/620. Not Boring Movies.sql b/LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/620. Not Boring Movies.sql
rename to LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.sql

From da212c2f906781802ad567c8f231f25a19bcbf74 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:30:22 +0530
Subject: [PATCH 094/353] 626. Exchange Seats

---
 .../{ => 626. Exchange Seats}/626. Exchange Seats.sql             | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 626. Exchange Seats}/626. Exchange Seats.sql (100%)

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats.sql b/LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/626. Exchange Seats.sql
rename to LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.sql

From 471673ddabfcca048c03895c4a743d81ff6ac4a5 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:30:39 +0530
Subject: [PATCH 095/353] 1045. Customers Who Bought All Products

---
 .../1045. Customers Who Bought All Products.sql                   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1045. Customers Who Bought All Products}/1045. Customers Who Bought All Products.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products.sql b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/1045. Customers Who Bought All Products.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1045. Customers Who Bought All Products.sql
rename to LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/1045. Customers Who Bought All Products.sql

From 621b0b670faeaffd96dadace43467c62d602b764 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:30:57 +0530
Subject: [PATCH 096/353] 1068. Product Sales Analysis I

---
 .../1068. Product Sales Analysis I.sql                            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1068. Product Sales Analysis I}/1068. Product Sales Analysis I.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1068. Product Sales Analysis I.sql b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/1068. Product Sales Analysis I.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1068. Product Sales Analysis I.sql
rename to LeetCode SQL 50 Solution/1068. Product Sales Analysis I/1068. Product Sales Analysis I.sql

From 16bf314755868598c2a2f62019164303e38c0768 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:31:18 +0530
Subject: [PATCH 097/353] 1070. Product Sales Analysis III

---
 .../1070. Product Sales Analysis III.sql                          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1070. Product Sales Analysis III}/1070. Product Sales Analysis III.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1070. Product Sales Analysis III.sql b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/1070. Product Sales Analysis III.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1070. Product Sales Analysis III.sql
rename to LeetCode SQL 50 Solution/1070. Product Sales Analysis III/1070. Product Sales Analysis III.sql

From 1a3d1b1e3b29f10446f1465cfff08de3b3601323 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:31:55 +0530
Subject: [PATCH 098/353] 1075. Project Employees I

---
 .../{ => 1075. Project Employees I}/1075. Project Employees I.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1075. Project Employees I}/1075. Project Employees I.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1075. Project Employees I.sql b/LeetCode SQL 50 Solution/1075. Project Employees I/1075. Project Employees I.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1075. Project Employees I.sql
rename to LeetCode SQL 50 Solution/1075. Project Employees I/1075. Project Employees I.sql

From 16070d792052b80c4538d07c76e7a85a152310d2 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:32:10 +0530
Subject: [PATCH 099/353] 1141. User Activity for the Past 30 Days I

---
 .../1141. User Activity for the Past 30 Days I.sql                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1141. User Activity for the Past 30 Days I}/1141. User Activity for the Past 30 Days I.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I.sql b/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/1141. User Activity for the Past 30 Days I.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I.sql
rename to LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/1141. User Activity for the Past 30 Days I.sql

From 523a5331d946e64f9eff8578e5336915477b984a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:32:31 +0530
Subject: [PATCH 100/353] 1148. Article Views I

---
 .../{ => 1148. Article Views I}/1148. Article Views I.sql         | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1148. Article Views I}/1148. Article Views I.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1148. Article Views I.sql b/LeetCode SQL 50 Solution/1148. Article Views I/1148. Article Views I.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1148. Article Views I.sql
rename to LeetCode SQL 50 Solution/1148. Article Views I/1148. Article Views I.sql

From b96686420c3bb2c177c4d0f81eae91e0d41eca62 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:32:44 +0530
Subject: [PATCH 101/353] 1164. Product Price at a Given Date

---
 .../1164. Product Price at a Given Date.sql                       | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1164. Product Price at a Given Date}/1164. Product Price at a Given Date.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date.sql b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/1164. Product Price at a Given Date.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1164. Product Price at a Given Date.sql
rename to LeetCode SQL 50 Solution/1164. Product Price at a Given Date/1164. Product Price at a Given Date.sql

From 1d2291d6bb822d7a32fbb1add4c7d9e98a20af92 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:33:37 +0530
Subject: [PATCH 102/353] 1174. Immediate Food Delivery II

---
 .../1174. Immediate Food Delivery II.sql                          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1174. Immediate Food Delivery II}/1174. Immediate Food Delivery II.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1174. Immediate Food Delivery II.sql b/LeetCode SQL 50 Solution/1174. Immediate Food Delivery II/1174. Immediate Food Delivery II.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1174. Immediate Food Delivery II.sql
rename to LeetCode SQL 50 Solution/1174. Immediate Food Delivery II/1174. Immediate Food Delivery II.sql

From 457656916825a47f2ad440f6940625ffc8ea8efb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:34:01 +0530
Subject: [PATCH 103/353] Update 185. Department Top Three Salaries.md

---
 .../185. Department Top Three Salaries.md                        | 1 +
 1 file changed, 1 insertion(+)

diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md
index 3b15680..289efac 100644
--- a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md	
+++ b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md	
@@ -96,6 +96,7 @@ There are no employees with the exact same name, salary and department.
 
 ## Solution
 
+
 ```MySQL
 # Write your MySQL query statement below
 SELECT d.Name as Department,

From 72eb145ec8a7db27733f94136131009c7f17b9ff Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:44:39 +0530
Subject: [PATCH 104/353] 1193. Monthly Transactions I

---
 .../1193. Monthly Transactions I.sql                              | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1193. Monthly Transactions I}/1193. Monthly Transactions I.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1193. Monthly Transactions I.sql b/LeetCode SQL 50 Solution/1193. Monthly Transactions I/1193. Monthly Transactions I.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1193. Monthly Transactions I.sql
rename to LeetCode SQL 50 Solution/1193. Monthly Transactions I/1193. Monthly Transactions I.sql

From 57dc5dd63e131525dc1a7a933c694f4e515af915 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:45:05 +0530
Subject: [PATCH 105/353] 1204. Last Person to Fit in the Bus

---
 .../1204. Last Person to Fit in the Bus.md                        | 0
 .../1204. Last Person to Fit in the Bus.sql                       | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1204. Last Person to Fit in the Bus}/1204. Last Person to Fit in the Bus.md (100%)
 rename LeetCode SQL 50 Solution/{ => 1204. Last Person to Fit in the Bus}/1204. Last Person to Fit in the Bus.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.md b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/1204. Last Person to Fit in the Bus.md
similarity index 100%
rename from LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.md
rename to LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/1204. Last Person to Fit in the Bus.md
diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/1204. Last Person to Fit in the Bus.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus.sql
rename to LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/1204. Last Person to Fit in the Bus.sql

From 7639c27108650d6ba42f3343403ed18622d60147 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:45:23 +0530
Subject: [PATCH 106/353] 1211. Queries Quality and Percentage

---
 .../1211. Queries Quality and Percentage.sql                      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1211. Queries Quality and Percentage}/1211. Queries Quality and Percentage.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage.sql b/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/1211. Queries Quality and Percentage.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1211. Queries Quality and Percentage.sql
rename to LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/1211. Queries Quality and Percentage.sql

From 22f146c559b77b8244be3b71ca5f4c6e27412142 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:45:42 +0530
Subject: [PATCH 107/353] 1251. Average Selling Price

---
 .../1251. Average Selling Price.sql                               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1251. Average Selling Price}/1251. Average Selling Price.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1251. Average Selling Price.sql b/LeetCode SQL 50 Solution/1251. Average Selling Price/1251. Average Selling Price.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1251. Average Selling Price.sql
rename to LeetCode SQL 50 Solution/1251. Average Selling Price/1251. Average Selling Price.sql

From eb471215421a2b88c821d9056b220f5b89acd3d6 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:46:00 +0530
Subject: [PATCH 108/353] 1280. Students and Examinations

---
 .../1280. Students and Examinations.sql                           | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1280. Students and Examinations}/1280. Students and Examinations.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1280. Students and Examinations.sql b/LeetCode SQL 50 Solution/1280. Students and Examinations/1280. Students and Examinations.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1280. Students and Examinations.sql
rename to LeetCode SQL 50 Solution/1280. Students and Examinations/1280. Students and Examinations.sql

From a01a6509b21bd5df6dd748ae0486cb56b70afd58 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:46:33 +0530
Subject: [PATCH 109/353] 1321. Restaurant Growth

---
 .../{ => 1321. Restaurant Growth}/1321. Restaurant Growth.sql     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1321. Restaurant Growth}/1321. Restaurant Growth.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1321. Restaurant Growth.sql b/LeetCode SQL 50 Solution/1321. Restaurant Growth/1321. Restaurant Growth.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1321. Restaurant Growth.sql
rename to LeetCode SQL 50 Solution/1321. Restaurant Growth/1321. Restaurant Growth.sql

From a5882cbfab4c8d0e50b115b31c10bca2b249aa17 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:47:01 +0530
Subject: [PATCH 110/353] 1327. List the Products Ordered in a Period

---
 .../1327. List the Products Ordered in a Period.SQL               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1327. List the Products Ordered in a Period}/1327. List the Products Ordered in a Period.SQL (100%)

diff --git a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/1327. List the Products Ordered in a Period.SQL
similarity index 100%
rename from LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period.SQL
rename to LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/1327. List the Products Ordered in a Period.SQL

From f454b831bce8173c79432b87e9e53ee21ad58bb4 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:47:22 +0530
Subject: [PATCH 111/353] 1341. Movie Rating

---
 .../{ => 1341. Movie Rating}/1341. Movie Rating.sql               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1341. Movie Rating}/1341. Movie Rating.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1341. Movie Rating.sql b/LeetCode SQL 50 Solution/1341. Movie Rating/1341. Movie Rating.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1341. Movie Rating.sql
rename to LeetCode SQL 50 Solution/1341. Movie Rating/1341. Movie Rating.sql

From caad5d1cd5e2cafc2978bd95500d76c17e6e6836 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:47:48 +0530
Subject: [PATCH 112/353] 1378. Replace Employee ID With The Unique Identifier

---
 .../1378. Replace Employee ID With The Unique Identifier.sql      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1378. Replace Employee ID With The Unique Identifier}/1378. Replace Employee ID With The Unique Identifier.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier.sql b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/1378. Replace Employee ID With The Unique Identifier.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier.sql
rename to LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/1378. Replace Employee ID With The Unique Identifier.sql

From 4cc5918220a1ddf88f9de4bc68f45374f5e96792 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:48:10 +0530
Subject: [PATCH 113/353] 1484. Group Sold Products By The Date

---
 .../1484. Group Sold Products By The Date.SQL                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1484. Group Sold Products By The Date}/1484. Group Sold Products By The Date.SQL (100%)

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/1484. Group Sold Products By The Date.SQL
similarity index 100%
rename from LeetCode SQL 50 Solution/1484. Group Sold Products By The Date.SQL
rename to LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/1484. Group Sold Products By The Date.SQL

From f864fbf0e9bcccc15af725dfbd8860b39bfe923a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:49:41 +0530
Subject: [PATCH 114/353] 1517. Find Users With Valid E-Mails

---
 .../1517. Find Users With Valid E-Mails.SQL                       | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1517. Find Users With Valid E-Mails}/1517. Find Users With Valid E-Mails.SQL (100%)

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/1517. Find Users With Valid E-Mails.SQL
similarity index 100%
rename from LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails.SQL
rename to LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/1517. Find Users With Valid E-Mails.SQL

From f63fa77f6d5215dd4f873522cc9a69e62ad96d3c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:49:56 +0530
Subject: [PATCH 115/353] 1527. Patients With a Condition

---
 .../1527. Patients With a Condition.sql                           | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1527. Patients With a Condition}/1527. Patients With a Condition.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1527. Patients With a Condition.sql b/LeetCode SQL 50 Solution/1527. Patients With a Condition/1527. Patients With a Condition.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1527. Patients With a Condition.sql
rename to LeetCode SQL 50 Solution/1527. Patients With a Condition/1527. Patients With a Condition.sql

From 963bf2f772ca68f1762b534189536f29b232be2d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:50:16 +0530
Subject: [PATCH 116/353] 1633. Percentage of Users Attended a Contest

---
 .../1633. Percentage of Users Attended a Contest.sql              | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1633. Percentage of Users Attended a Contest}/1633. Percentage of Users Attended a Contest.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest.sql b/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/1633. Percentage of Users Attended a Contest.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest.sql
rename to LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/1633. Percentage of Users Attended a Contest.sql

From 6fd557e187e6a3f2af0ed1925fb0eb14aa7ed230 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:50:34 +0530
Subject: [PATCH 117/353] 1667. Fix Names in a Table

---
 .../1667. Fix Names in a Table.sql                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1667. Fix Names in a Table}/1667. Fix Names in a Table.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1667. Fix Names in a Table.sql
rename to LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table.sql

From 96957b074b6590f844a0f64e43683b7a4149d237 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:51:03 +0530
Subject: [PATCH 118/353] 1683. Invalid Tweets

---
 .../{ => 1683. Invalid Tweets}/1683. Invalid Tweets.sql           | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1683. Invalid Tweets}/1683. Invalid Tweets.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1683. Invalid Tweets.sql b/LeetCode SQL 50 Solution/1683. Invalid Tweets/1683. Invalid Tweets.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1683. Invalid Tweets.sql
rename to LeetCode SQL 50 Solution/1683. Invalid Tweets/1683. Invalid Tweets.sql

From 20606e3bde6b25bc149ec474d9981f8942376ad8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:52:37 +0530
Subject: [PATCH 119/353] 1729. Find Followers Count

---
 .../1729. Find Followers Count.sql                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1729. Find Followers Count}/1729. Find Followers Count.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1729. Find Followers Count.sql b/LeetCode SQL 50 Solution/1729. Find Followers Count/1729. Find Followers Count.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1729. Find Followers Count.sql
rename to LeetCode SQL 50 Solution/1729. Find Followers Count/1729. Find Followers Count.sql

From 716dc8af0a341c3bc7a210ec74cbd68ff990f5fc Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:52:55 +0530
Subject: [PATCH 120/353] 1731. The Number of Employees Which Report to Each
 Employee

---
 ...731. The Number of Employees Which Report to Each Employee.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1731. The Number of Employees Which Report to Each Employee}/1731. The Number of Employees Which Report to Each Employee.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee.sql b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/1731. The Number of Employees Which Report to Each Employee.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee.sql
rename to LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/1731. The Number of Employees Which Report to Each Employee.sql

From 1f3aa265de16d53221cd3b8a2de81c81019ccc9d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:53:13 +0530
Subject: [PATCH 121/353] 1757. Recyclable and Low Fat Products

---
 .../1757. Recyclable and Low Fat Products.sql                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1757. Recyclable and Low Fat Products}/1757. Recyclable and Low Fat Products.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products.sql b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/1757. Recyclable and Low Fat Products.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products.sql
rename to LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/1757. Recyclable and Low Fat Products.sql

From 694081c889347df89b673792830e8f375cfac4fe Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:53:32 +0530
Subject: [PATCH 122/353] 1789. Primary Department for Each Employee

---
 .../1789. Primary Department for Each Employee.sql                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1789. Primary Department for Each Employee}/1789. Primary Department for Each Employee.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee.sql b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/1789. Primary Department for Each Employee.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1789. Primary Department for Each Employee.sql
rename to LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/1789. Primary Department for Each Employee.sql

From 9215f645814f77208f29f403ff734374de376fe1 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:53:49 +0530
Subject: [PATCH 123/353] 1907. Count Salary Categories

---
 .../1907. Count Salary Categories.sql                             | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1907. Count Salary Categories}/1907. Count Salary Categories.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories.sql b/LeetCode SQL 50 Solution/1907. Count Salary Categories/1907. Count Salary Categories.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1907. Count Salary Categories.sql
rename to LeetCode SQL 50 Solution/1907. Count Salary Categories/1907. Count Salary Categories.sql

From 32528a9106bbaf0ffa24998aff8421797beb379f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:54:25 +0530
Subject: [PATCH 124/353] 1978. Employees Whose Manager Left the Company

---
 .../{ => 1934. Confirmation Rate}/1934. Confirmation Rate.sql     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1934. Confirmation Rate}/1934. Confirmation Rate.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1934. Confirmation Rate.sql b/LeetCode SQL 50 Solution/1934. Confirmation Rate/1934. Confirmation Rate.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1934. Confirmation Rate.sql
rename to LeetCode SQL 50 Solution/1934. Confirmation Rate/1934. Confirmation Rate.sql

From 7b3921c3c17266ba4a3189aeb2530c114b839ab1 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:54:32 +0530
Subject: [PATCH 125/353] 1978. Employees Whose Manager Left the Company

---
 .../1978. Employees Whose Manager Left the Company.sql            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 1978. Employees Whose Manager Left the Company}/1978. Employees Whose Manager Left the Company.sql (100%)

diff --git a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/1978. Employees Whose Manager Left the Company.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company.sql
rename to LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/1978. Employees Whose Manager Left the Company.sql

From 7cb9675727125913416a2864d64e87eda1a560be Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:55:04 +0530
Subject: [PATCH 126/353] prosnal_database

---
 .../2356. Number of Unique Subjects Taught by Each Teacher.sql    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => 2356. Number of Unique Subjects Taught by Each Teacher}/2356. Number of Unique Subjects Taught by Each Teacher.sql (100%)

diff --git a/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher.sql b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/2356. Number of Unique Subjects Taught by Each Teacher.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher.sql
rename to LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/2356. Number of Unique Subjects Taught by Each Teacher.sql

From 5f35be12ce3a92d1fdc0f1788e08bcc5f634a621 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 11:55:16 +0530
Subject: [PATCH 127/353] prosnal_database

---
 .../{ => prosnal_database}/prosnal_database.sql                   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{ => prosnal_database}/prosnal_database.sql (100%)

diff --git a/LeetCode SQL 50 Solution/prosnal_database.sql b/LeetCode SQL 50 Solution/prosnal_database/prosnal_database.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/prosnal_database.sql
rename to LeetCode SQL 50 Solution/prosnal_database/prosnal_database.sql

From 25a9394ed8d07d42324159e70dc4a7e1269e88fb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 12:04:43 +0530
Subject: [PATCH 128/353] Create 1581. Customer Who Visited but Did Not Make
 Any Transactions.sql

---
 ...81. Customer Who Visited but Did Not Make Any Transactions.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql

diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql b/LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql
new file mode 100644
index 0000000..e69de29

From 618c58a9876366c29ff60feb43657f6995f5e9d2 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 12:04:51 +0530
Subject: [PATCH 129/353] Update 1581. Customer Who Visited but Did Not Make
 Any Transactions.sql

---
 ...stomer Who Visited but Did Not Make Any Transactions.sql | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql b/LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql
index e69de29..21b8124 100644
--- a/LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql	
+++ b/LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql	
@@ -0,0 +1,6 @@
+# Write your MySQL query statement below
+# Write your MySQL query statement below
+SELECT customer_id, COUNT(*) as count_no_trans
+FROM Visits
+WHERE visit_id NOT IN (SELECT DISTINCT visit_id FROM Transactions)
+GROUP BY customer_id;
\ No newline at end of file

From 91c08fb6444d24c422f86ddcbaeeb4f772fc1030 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 12:05:59 +0530
Subject: [PATCH 130/353] add

---
 ...81. Customer Who Visited but Did Not Make Any Transactions.sql | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/{176. Second heighest salary => }/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql (100%)

diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql
similarity index 100%
rename from LeetCode SQL 50 Solution/176. Second heighest salary/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql
rename to LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.sql

From e051ba74f5dbefc987825ed0dfd08c254b22ce39 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 12:29:27 +0530
Subject: [PATCH 131/353] Create Rising Temperature.sql

---
 .../Rising Temperature.sql                    | 21 +++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/197. Rising Temperature/Rising Temperature.sql

diff --git a/LeetCode SQL 50 Solution/197. Rising Temperature/Rising Temperature.sql b/LeetCode SQL 50 Solution/197. Rising Temperature/Rising Temperature.sql
new file mode 100644
index 0000000..72b2d7f
--- /dev/null
+++ b/LeetCode SQL 50 Solution/197. Rising Temperature/Rising Temperature.sql	
@@ -0,0 +1,21 @@
+"""197. Rising Temperature"""
+
+WITH PreviousWeatherData AS
+(
+    SELECT 
+        id,
+        recordDate,
+        temperature, 
+        LAG(temperature, 1) OVER (ORDER BY recordDate) AS PreviousTemperature,
+        LAG(recordDate, 1) OVER (ORDER BY recordDate) AS PreviousRecordDate
+    FROM 
+        Weather
+)
+SELECT 
+    id 
+FROM 
+    PreviousWeatherData
+WHERE 
+    temperature > PreviousTemperature
+AND 
+    recordDate = DATE_ADD(PreviousRecordDate, INTERVAL 1 DAY);
\ No newline at end of file

From 53a018813546379268e587c6bda16e3ec44290e5 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 14:31:43 +0530
Subject: [PATCH 132/353] assd

---
 .../176. Second Highest Salary.SQL            |  66 ----------
 .../176. Second Highest Salary.py             |   6 +
 .../176. Second heighest salary.sql           |  15 ++-
 .../176. Second heighest salary/readme.md     | 121 ++++++++++++++++++
 4 files changed, 141 insertions(+), 67 deletions(-)
 delete mode 100644 LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.SQL
 create mode 100644 LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.py
 create mode 100644 LeetCode SQL 50 Solution/176. Second heighest salary/readme.md

diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.SQL b/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.SQL
deleted file mode 100644
index e8f8028..0000000
--- a/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.SQL	
+++ /dev/null
@@ -1,66 +0,0 @@
-176. Second Highest Salary
-Solved
-Medium
-Topics
-Companies
-SQL Schema
-Pandas Schema
-Table: Employee
-
-+-------------+------+
-| Column Name | Type |
-+-------------+------+
-| id          | int  |
-| salary      | int  |
-+-------------+------+
-id is the primary key (column with unique values) for this table.
-Each row of this table contains information about the salary of an employee.
- 
-
-Write a solution to find the second highest distinct salary from the Employee table. If there is no second highest salary, return null (return None in Pandas).
-
-The result format is in the following example.
-
- 
-
-Example 1:
-
-Input: 
-Employee table:
-+----+--------+
-| id | salary |
-+----+--------+
-| 1  | 100    |
-| 2  | 200    |
-| 3  | 300    |
-+----+--------+
-Output: 
-+---------------------+
-| SecondHighestSalary |
-+---------------------+
-| 200                 |
-+---------------------+
-Example 2:
-
-Input: 
-Employee table:
-+----+--------+
-| id | salary |
-+----+--------+
-| 1  | 100    |
-+----+--------+
-Output: 
-+---------------------+
-| SecondHighestSalary |
-+---------------------+
-| null                |
-+---------------------+ 
-
-WITH
-  RankedEmployees AS (
-    SELECT *, DENSE_RANK() OVER(ORDER BY salary DESC) AS `rank`
-    FROM Employee
-  )
-SELECT MAX(salary) AS SecondHighestSalary
-FROM RankedEmployees
-WHERE `rank` = 2;
\ No newline at end of file
diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.py b/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.py
new file mode 100644
index 0000000..e50a809
--- /dev/null
+++ b/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second Highest Salary.py	
@@ -0,0 +1,6 @@
+import pandas as pd
+
+def second_highest_salary(employee: pd.DataFrame) -> pd.DataFrame:
+    unique_salaries = employee['salary'].drop_duplicates().sort_values(ascending=False)
+    second_highest = unique_salaries.iloc[1] if len(unique_salaries) > 1 else None
+    return pd.DataFrame({'SecondHighestSalary': [second_highest]})
diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second heighest salary.sql b/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second heighest salary.sql
index 3d17f1d..1ac922f 100644
--- a/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second heighest salary.sql	
+++ b/LeetCode SQL 50 Solution/176. Second heighest salary/176. Second heighest salary.sql	
@@ -1,4 +1,17 @@
-
+# Solution 1
 # Write your MSSQL query statement below
 select max(salary) as SecondHighestSalary from employee
 where salary not in (select max(salary) from employee)
+
+
+
+# Solution 2
+# Write your MSSQL query statement below
+# Write your MySQL query statement below
+SELECT
+    (
+        SELECT DISTINCT salary
+        FROM Employee
+        ORDER BY salary DESC
+        LIMIT 1, 1
+    ) AS SecondHighestSalary;
\ No newline at end of file
diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/readme.md b/LeetCode SQL 50 Solution/176. Second heighest salary/readme.md
new file mode 100644
index 0000000..0eb0146
--- /dev/null
+++ b/LeetCode SQL 50 Solution/176. Second heighest salary/readme.md	
@@ -0,0 +1,121 @@
+# 🏆 LeetCode 176: Second Highest Salary (Pandas) 🚀  
+
+## **Problem Statement**  
+You are given an **Employee** table with the following schema:  
+
+```
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| id          | int  |
+| salary      | int  |
++-------------+------+
+```
+- `id` is the **primary key** (unique for each employee).  
+- Each row contains information about an employee's salary.  
+
+Your task is to **find the second highest distinct salary**.  
+- If there is no second highest salary, return `None`.  
+
+---
+
+## **Example 1**  
+
+### **Input:**  
+```plaintext
++----+--------+
+| id | salary |
++----+--------+
+| 1  | 100    |
+| 2  | 200    |
+| 3  | 300    |
++----+--------+
+```
+### **Output:**  
+```plaintext
++---------------------+
+| SecondHighestSalary |
++---------------------+
+| 200                 |
++---------------------+
+```
+
+---
+
+## **Example 2**  
+
+### **Input:**  
+```plaintext
++----+--------+
+| id | salary |
++----+--------+
+| 1  | 100    |
++----+--------+
+```
+### **Output:**  
+```plaintext
++---------------------+
+| SecondHighestSalary |
++---------------------+
+| null                |
++---------------------+
+```
+
+---
+
+## **Approach**  
+
+1. **Remove duplicate salaries** using `.drop_duplicates()`.  
+2. **Sort salaries** in descending order.  
+3. **Retrieve the second highest salary**, if it exists.  
+4. If there is no second highest salary, return `None`.  
+
+---
+
+## **Code (Pandas Solution)**  
+
+```python
+import pandas as pd
+
+def second_highest_salary(employee: pd.DataFrame) -> pd.DataFrame:
+    # Get distinct salaries, sorted in descending order
+    unique_salaries = employee['salary'].drop_duplicates().sort_values(ascending=False)
+    
+    # Check if there is a second highest salary
+    second_highest = unique_salaries.iloc[1] if len(unique_salaries) > 1 else None
+    
+    # Return as a DataFrame with column name 'SecondHighestSalary'
+    return pd.DataFrame({'SecondHighestSalary': [second_highest]})
+```
+
+---
+
+## **Complexity Analysis**  
+- **Time Complexity:** $$O(n \log n)$$ due to sorting.  
+- **Space Complexity:** $$O(n)$$ for storing unique salaries.  
+
+---
+
+## **Why This Works?**  
+✅ Uses Pandas' `drop_duplicates()` for distinct values.  
+✅ Sorts efficiently using `sort_values()`.  
+✅ Handles cases where no second highest salary exists.  
+
+---
+
+## **SQL Equivalent Solution**  
+If solving in SQL, we can use **`DENSE_RANK()`**:  
+```sql
+WITH RankedEmployees AS (
+    SELECT salary, DENSE_RANK() OVER(ORDER BY salary DESC) AS `rank`
+    FROM Employee
+)
+SELECT MAX(salary) AS SecondHighestSalary
+FROM RankedEmployees
+WHERE `rank` = 2;
+```
+
+---
+
+### **Happy Coding! 🚀💡**  
+Hope this helps! Feel free to ⭐ **star** this repo if you found it useful. 😊
\ No newline at end of file

From 7ccbdf9a80f712692a798b973a3097b67ec8c59f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 14:36:06 +0530
Subject: [PATCH 133/353] Update 177. Nth Highest Salary.sql

---
 .../177. Nth Highest Salary.sql               | 36 ++++++-------------
 1 file changed, 10 insertions(+), 26 deletions(-)

diff --git a/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.sql b/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.sql
index a79d1d5..6dfb0ac 100644
--- a/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.sql	
+++ b/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.sql	
@@ -1,28 +1,12 @@
-/*
-* Order By Clause
-  * ORDER BY order_by_expression  
-    [ COLLATE collation_name ]   
-    [ ASC | DESC ]   
-    [ ,...n ]   
-[ <offset_fetch> ]  
-  
-<offset_fetch> ::=  
-{   
-    OFFSET { integer_constant | offset_row_count_expression } { ROW | ROWS }  
-    [  
-      FETCH { FIRST | NEXT } {integer_constant | fetch_row_count_expression } { ROW | ROWS } ONLY  
-    ]  
-} 
-*/
-
-Create FUNCTION getNthHighestSalary(@N INT) returns INT as 
+# Write your MySQL query statement below.
+CREATE FUNCTION getNthHighestSalary(N INT) RETURNS INT
 BEGIN
-  Return(
-  Select Salary
-  From Employee
-  Gourp By Salary
-  Order By Salary DESC
-  Offset @N-1 rows
-  Fetch First 1 Rows Only
+SET N = N-1;
+  RETURN (
+      SELECT DISTINCT(salary) from Employee order by salary DESC
+      LIMIT 1 OFFSET N
+      
   );
-  End
+END
+
+# pls upvote if you find solution easy to undestand....!! Thanks..!!!
\ No newline at end of file

From 3d2b663895761e866c3df6dc1f6a7f2d8f04fe9b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 14:40:47 +0530
Subject: [PATCH 134/353] creater file

---
 .../176. Second heighest salary/readme.md     | 152 ++++++++----------
 .../177. Nth Highest Salary/readme.md         | 121 ++++++++++++++
 2 files changed, 186 insertions(+), 87 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/177. Nth Highest Salary/readme.md

diff --git a/LeetCode SQL 50 Solution/176. Second heighest salary/readme.md b/LeetCode SQL 50 Solution/176. Second heighest salary/readme.md
index 0eb0146..76e32e9 100644
--- a/LeetCode SQL 50 Solution/176. Second heighest salary/readme.md	
+++ b/LeetCode SQL 50 Solution/176. Second heighest salary/readme.md	
@@ -1,113 +1,65 @@
-# 🏆 LeetCode 176: Second Highest Salary (Pandas) 🚀  
+# 176. Second Highest Salary
 
-## **Problem Statement**  
-You are given an **Employee** table with the following schema:  
+## Problem Statement
+You are given a table `Employee` containing the salaries of employees. The goal is to find the second highest distinct salary from this table. If there is no second highest salary, return `NULL` (or `None` in Pandas).
+
+### Table: Employee
 
-```
-+-------------+------+
 | Column Name | Type |
-+-------------+------+
+| ----------- | ---- |
 | id          | int  |
 | salary      | int  |
-+-------------+------+
-```
-- `id` is the **primary key** (unique for each employee).  
-- Each row contains information about an employee's salary.  
 
-Your task is to **find the second highest distinct salary**.  
-- If there is no second highest salary, return `None`.  
+- `id` is the primary key for this table.
+- Each row contains salary information for an employee.
 
----
+## Example 1:
 
-## **Example 1**  
-
-### **Input:**  
-```plaintext
-+----+--------+
-| id | salary |
-+----+--------+
-| 1  | 100    |
-| 2  | 200    |
-| 3  | 300    |
-+----+--------+
-```
-### **Output:**  
-```plaintext
-+---------------------+
-| SecondHighestSalary |
-+---------------------+
-| 200                 |
-+---------------------+
-```
+### **Input:**
 
----
+| id  | salary |
+| --- | ------ |
+| 1   | 100    |
+| 2   | 200    |
+| 3   | 300    |
 
-## **Example 2**  
+### **Output:**
 
-### **Input:**  
-```plaintext
-+----+--------+
-| id | salary |
-+----+--------+
-| 1  | 100    |
-+----+--------+
-```
-### **Output:**  
-```plaintext
-+---------------------+
 | SecondHighestSalary |
-+---------------------+
-| null                |
-+---------------------+
-```
-
----
-
-## **Approach**  
-
-1. **Remove duplicate salaries** using `.drop_duplicates()`.  
-2. **Sort salaries** in descending order.  
-3. **Retrieve the second highest salary**, if it exists.  
-4. If there is no second highest salary, return `None`.  
-
----
+| ------------------- |
+| 200                 |
 
-## **Code (Pandas Solution)**  
+## Example 2:
 
-```python
-import pandas as pd
+### **Input:**
 
-def second_highest_salary(employee: pd.DataFrame) -> pd.DataFrame:
-    # Get distinct salaries, sorted in descending order
-    unique_salaries = employee['salary'].drop_duplicates().sort_values(ascending=False)
-    
-    # Check if there is a second highest salary
-    second_highest = unique_salaries.iloc[1] if len(unique_salaries) > 1 else None
-    
-    # Return as a DataFrame with column name 'SecondHighestSalary'
-    return pd.DataFrame({'SecondHighestSalary': [second_highest]})
-```
+| id  | salary |
+| --- | ------ |
+| 1   | 100    |
 
----
+### **Output:**
 
-## **Complexity Analysis**  
-- **Time Complexity:** $$O(n \log n)$$ due to sorting.  
-- **Space Complexity:** $$O(n)$$ for storing unique salaries.  
+| SecondHighestSalary |
+| ------------------- |
+| NULL                |
 
 ---
+## **Approach**
 
-## **Why This Works?**  
-✅ Uses Pandas' `drop_duplicates()` for distinct values.  
-✅ Sorts efficiently using `sort_values()`.  
-✅ Handles cases where no second highest salary exists.  
+### **SQL Approach**
+1. **Use a Window Function:**
+   - Apply `DENSE_RANK()` to rank salaries in descending order.
+   - Assign rank `1` to the highest salary, `2` to the second highest, and so on.
+2. **Filter by Rank:**
+   - Select the salary where `rank = 2`.
+   - If no second highest salary exists, return `NULL`.
 
 ---
+## **Solution**
 
-## **SQL Equivalent Solution**  
-If solving in SQL, we can use **`DENSE_RANK()`**:  
 ```sql
 WITH RankedEmployees AS (
-    SELECT salary, DENSE_RANK() OVER(ORDER BY salary DESC) AS `rank`
+    SELECT *, DENSE_RANK() OVER(ORDER BY salary DESC) AS `rank`
     FROM Employee
 )
 SELECT MAX(salary) AS SecondHighestSalary
@@ -116,6 +68,32 @@ WHERE `rank` = 2;
 ```
 
 ---
+## **File Structure**
+```
+📂 SecondHighestSalary
+├── 📄 README.md  # Problem statement, approach, and solution
+├── 📄 solution.sql  # SQL query file
+├── 📄 solution_pandas.py  # Pandas solution file
+```
+
+---
+## **Alternative Pandas Approach**
+
+```python
+import pandas as pd
+
+def second_highest_salary(employee: pd.DataFrame) -> pd.DataFrame:
+    unique_salaries = employee['salary'].drop_duplicates().nlargest(2)
+    second_highest = unique_salaries.iloc[1] if len(unique_salaries) > 1 else None
+    return pd.DataFrame({'SecondHighestSalary': [second_highest]})
+```
+
+---
+## **Resources & References**
+- [LeetCode Problem Link](https://leetcode.com/problems/second-highest-salary/)
+- [SQL DENSE_RANK() Documentation](https://www.sqlservertutorial.net/sql-server-window-functions/sql-server-dense_rank-function/)
+
+---
+## **Contribute**
+Feel free to contribute by submitting an issue or a pull request!
 
-### **Happy Coding! 🚀💡**  
-Hope this helps! Feel free to ⭐ **star** this repo if you found it useful. 😊
\ No newline at end of file
diff --git a/LeetCode SQL 50 Solution/177. Nth Highest Salary/readme.md b/LeetCode SQL 50 Solution/177. Nth Highest Salary/readme.md
new file mode 100644
index 0000000..4d13d7e
--- /dev/null
+++ b/LeetCode SQL 50 Solution/177. Nth Highest Salary/readme.md	
@@ -0,0 +1,121 @@
+# 177. Nth Highest Salary
+
+## Problem Statement
+Given a table `Employee`, write a SQL query to find the `nth` highest salary. If there is no `nth` highest salary, return `null`.
+
+### Table Schema: `Employee`
+| Column Name | Type |
+| ----------- | ---- |
+| id          | int  |
+| salary      | int  |
+
+- `id` is the primary key (unique values for employees).
+- `salary` column contains employee salary details.
+
+### Example 1:
+#### **Input:**
+```sql
++----+--------+
+| id | salary |
++----+--------+
+| 1  | 100    |
+| 2  | 200    |
+| 3  | 300    |
++----+--------+
+n = 2
+```
+#### **Output:**
+```sql
++------------------------+
+| getNthHighestSalary(2) |
++------------------------+
+| 200                    |
++------------------------+
+```
+
+### Example 2:
+#### **Input:**
+```sql
++----+--------+
+| id | salary |
++----+--------+
+| 1  | 100    |
++----+--------+
+n = 2
+```
+#### **Output:**
+```sql
++------------------------+
+| getNthHighestSalary(2) |
++------------------------+
+| null                   |
++------------------------+
+```
+
+---
+
+## Approach
+1. Use the `DENSE_RANK()` function to rank salaries in descending order.
+2. Filter for the `nth` highest salary using a `WHERE` clause.
+3. If there is no `nth` highest salary, return `NULL`.
+
+---
+
+## SQL Solution
+```sql
+CREATE FUNCTION getNthHighestSalary(N INT) RETURNS INT AS
+BEGIN
+  RETURN (
+    SELECT DISTINCT salary FROM Employee
+    ORDER BY salary DESC
+    LIMIT 1 OFFSET N-1
+  );
+END;
+```
+
+### Explanation:
+- `ORDER BY salary DESC` sorts salaries in descending order.
+- `LIMIT 1 OFFSET N-1` fetches the `nth` highest salary.
+- If `N` is larger than the number of salaries, `NULL` is returned.
+
+---
+
+## Pandas Solution
+```python
+import pandas as pd
+
+def getNthHighestSalary(employee: pd.DataFrame, N: int) -> pd.DataFrame:
+    unique_salaries = employee['salary'].drop_duplicates().nlargest(N)
+    if len(unique_salaries) < N:
+        return pd.DataFrame({"getNthHighestSalary(N)": [None]})
+    return pd.DataFrame({"getNthHighestSalary(N)": [unique_salaries.iloc[-1]]})
+```
+
+### Explanation:
+- `drop_duplicates()` removes duplicate salaries.
+- `nlargest(N)` gets the `N` highest salaries.
+- If `N` is greater than available salaries, return `None`.
+
+---
+
+## File Structure
+```
+📂 nth_highest_salary
+ ├── 📄 README.md       # Problem statement, approach, solution
+ ├── 📄 nth_highest_salary.sql  # SQL Solution
+ ├── 📄 nth_highest_salary.py   # Pandas Solution
+ └── 📄 example_input_output.txt  # Sample input & expected output
+```
+
+---
+
+## References
+- [LeetCode Problem #177](https://leetcode.com/problems/nth-highest-salary/)
+- [MySQL Documentation - LIMIT & OFFSET](https://dev.mysql.com/doc/refman/8.0/en/select.html)
+- [Pandas Documentation](https://pandas.pydata.org/docs/)
+
+---
+
+### Contributors
+- **[Antim Pal]** 🚀
+

From 3db5147e08f230647b62ce0d0e64609e3e57b485 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 14:41:29 +0530
Subject: [PATCH 135/353] Create 177. Nth Highest Salary.py

---
 .../177. Nth Highest Salary/177. Nth Highest Salary.py     | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.py

diff --git a/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.py b/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.py
new file mode 100644
index 0000000..9136333
--- /dev/null
+++ b/LeetCode SQL 50 Solution/177. Nth Highest Salary/177. Nth Highest Salary.py	
@@ -0,0 +1,7 @@
+import pandas as pd
+
+def getNthHighestSalary(employee: pd.DataFrame, N: int) -> pd.DataFrame:
+    unique_salaries = employee['salary'].drop_duplicates().nlargest(N)
+    if len(unique_salaries) < N:
+        return pd.DataFrame({"getNthHighestSalary(N)": [None]})
+    return pd.DataFrame({"getNthHighestSalary(N)": [unique_salaries.iloc[-1]]})
\ No newline at end of file

From 739c547f87d23db14cf1ad6ff7b4a546a89a6764 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 14:46:31 +0530
Subject: [PATCH 136/353] Create readme.md

---
 .../180. Consecutive Numbers/readme.md        | 91 +++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md

diff --git a/LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md b/LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md
new file mode 100644
index 0000000..d85879d
--- /dev/null
+++ b/LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md	
@@ -0,0 +1,91 @@
+# 180. Consecutive Numbers
+
+## Problem Statement
+Table: `Logs`
+
+| Column Name | Type    |
+| ----------- | ------- |
+| id          | int     |
+| num         | varchar |
+
+- `id` is the primary key (auto-incremented starting from 1).
+- Find all numbers that appear **at least three times consecutively**.
+- Return the result table in **any order**.
+
+### Example 1:
+
+#### Input:
+| id  | num |
+| --- | --- |
+| 1   | 1   |
+| 2   | 1   |
+| 3   | 1   |
+| 4   | 2   |
+| 5   | 1   |
+| 6   | 2   |
+| 7   | 2   |
+
+#### Output:
+| ConsecutiveNums |
+| --------------- |
+| 1               |
+
+---
+
+## Solution
+
+```sql
+SELECT DISTINCT num AS ConsecutiveNums
+FROM Logs l1
+JOIN Logs l2 ON l1.id = l2.id - 1
+JOIN Logs l3 ON l2.id = l3.id - 1
+WHERE l1.num = l2.num AND l2.num = l3.num;
+```
+
+### Explanation:
+- We use **self-joins** to check three consecutive rows where `num` values are the same.
+- `l1`, `l2`, and `l3` represent three consecutive rows.
+- The condition `l1.num = l2.num AND l2.num = l3.num` ensures that we only select numbers appearing at least three times consecutively.
+- `DISTINCT` ensures we don't get duplicate results.
+
+---
+
+## Alternative Approach using `LAG()` (MySQL 8+)
+
+```sql
+WITH Consecutive AS (
+    SELECT num,
+           LAG(num, 1) OVER (ORDER BY id) AS prev1,
+           LAG(num, 2) OVER (ORDER BY id) AS prev2
+    FROM Logs
+)
+SELECT DISTINCT num AS ConsecutiveNums
+FROM Consecutive
+WHERE num = prev1 AND num = prev2;
+```
+
+### Explanation:
+- We use the `LAG()` function to check the previous two rows for the same `num` value.
+- If a `num` matches with its two previous values, it qualifies as a **consecutive number appearing at least three times**.
+
+---
+
+## File Structure
+
+```
+📂 ConsecutiveNumbers
+ ├── 📄 README.md  # Problem statement, approach, and solutions
+ ├── 📄 consecutive_numbers.sql  # SQL solution
+ ├── 📄 alternative_solution.sql  # Alternative solution using LAG()
+```
+
+---
+
+## Useful Links
+- [LeetCode Problem](https://leetcode.com/problems/consecutive-numbers/) 🚀
+- [SQL `JOIN` Explained](https://www.w3schools.com/sql/sql_join.asp)
+- [MySQL `LAG()` Window Function](https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html)
+
+---
+
+Feel free to contribute with optimized solutions! 💡
\ No newline at end of file

From 9fea8b3a32ac6340bf30cffe735a9737fab68512 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 14:51:21 +0530
Subject: [PATCH 137/353] Create 180. Consecutive Numbers.py

---
 .../180. Consecutive Numbers/180. Consecutive Numbers.py          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/180. Consecutive Numbers/180. Consecutive Numbers.py

diff --git a/LeetCode SQL 50 Solution/180. Consecutive Numbers/180. Consecutive Numbers.py b/LeetCode SQL 50 Solution/180. Consecutive Numbers/180. Consecutive Numbers.py
new file mode 100644
index 0000000..e69de29

From a839102942ad3b2f221f736a3c851202a18d87b2 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 15:02:47 +0530
Subject: [PATCH 138/353] Update readme.md

---
 .../180. Consecutive Numbers/readme.md        | 108 ++++++++++--------
 1 file changed, 59 insertions(+), 49 deletions(-)

diff --git a/LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md b/LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md
index d85879d..73e7b25 100644
--- a/LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md	
+++ b/LeetCode SQL 50 Solution/180. Consecutive Numbers/readme.md	
@@ -1,86 +1,96 @@
 # 180. Consecutive Numbers
 
 ## Problem Statement
-Table: `Logs`
+You are given a table `Logs` with the following structure:
 
+```
++-------------+---------+
 | Column Name | Type    |
-| ----------- | ------- |
++-------------+---------+
 | id          | int     |
 | num         | varchar |
-
-- `id` is the primary key (auto-incremented starting from 1).
++-------------+---------+
+```
+- `id` is the primary key and auto-increments starting from 1.
 - Find all numbers that appear **at least three times consecutively**.
 - Return the result table in **any order**.
 
-### Example 1:
+## Example 1:
 
-#### Input:
-| id  | num |
-| --- | --- |
-| 1   | 1   |
-| 2   | 1   |
-| 3   | 1   |
-| 4   | 2   |
-| 5   | 1   |
-| 6   | 2   |
-| 7   | 2   |
+**Input:**
+
+```
+Logs table:
++----+-----+
+| id | num |
++----+-----+
+| 1  | 1   |
+| 2  | 1   |
+| 3  | 1   |
+| 4  | 2   |
+| 5  | 1   |
+| 6  | 2   |
+| 7  | 2   |
++----+-----+
+```
 
-#### Output:
+**Output:**
+
+```
++-----------------+
 | ConsecutiveNums |
-| --------------- |
++-----------------+
 | 1               |
++-----------------+
+```
 
 ---
 
-## Solution
+## Solution Approaches
 
+### **SQL Solution (Using Self Join)**
 ```sql
-SELECT DISTINCT num AS ConsecutiveNums
+SELECT DISTINCT l1.num AS ConsecutiveNums
 FROM Logs l1
-JOIN Logs l2 ON l1.id = l2.id - 1
-JOIN Logs l3 ON l2.id = l3.id - 1
-WHERE l1.num = l2.num AND l2.num = l3.num;
+JOIN Logs l2 ON l1.id = l2.id - 1 AND l1.num = l2.num
+JOIN Logs l3 ON l1.id = l3.id - 2 AND l1.num = l3.num;
 ```
 
-### Explanation:
-- We use **self-joins** to check three consecutive rows where `num` values are the same.
-- `l1`, `l2`, and `l3` represent three consecutive rows.
-- The condition `l1.num = l2.num AND l2.num = l3.num` ensures that we only select numbers appearing at least three times consecutively.
-- `DISTINCT` ensures we don't get duplicate results.
-
----
-
-## Alternative Approach using `LAG()` (MySQL 8+)
-
+### **SQL Solution (Using Window Functions)**
 ```sql
-WITH Consecutive AS (
-    SELECT num,
-           LAG(num, 1) OVER (ORDER BY id) AS prev1,
-           LAG(num, 2) OVER (ORDER BY id) AS prev2
-    FROM Logs
-)
 SELECT DISTINCT num AS ConsecutiveNums
-FROM Consecutive
+FROM (
+    SELECT num, LAG(num,1) OVER (ORDER BY id) AS prev1,
+                LAG(num,2) OVER (ORDER BY id) AS prev2
+    FROM Logs
+) temp
 WHERE num = prev1 AND num = prev2;
 ```
 
-### Explanation:
-- We use the `LAG()` function to check the previous two rows for the same `num` value.
-- If a `num` matches with its two previous values, it qualifies as a **consecutive number appearing at least three times**.
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def consecutive_numbers(logs: pd.DataFrame) -> pd.DataFrame:
+    logs['prev1'] = logs['num'].shift(1)
+    logs['prev2'] = logs['num'].shift(2)
+    
+    result = logs[(logs['num'] == logs['prev1']) & (logs['num'] == logs['prev2'])]
+    return pd.DataFrame({'ConsecutiveNums': result['num'].unique()})
+```
 
 ---
 
-## File Structure
 
+## File Structure
 ```
-📂 ConsecutiveNumbers
- ├── 📄 README.md  # Problem statement, approach, and solutions
- ├── 📄 consecutive_numbers.sql  # SQL solution
- ├── 📄 alternative_solution.sql  # Alternative solution using LAG()
+📂 Problem Name
+ ├── 📄 README.md       # Problem statement, approach, solution
+ ├── 📄 sql_solution.sql  # SQL Solution
+ ├── 📄 pandas_solution.py   # Pandas Solution
+ └── 📄 example_input_output.txt  # Sample input & expected output
 ```
 
----
-
 ## Useful Links
 - [LeetCode Problem](https://leetcode.com/problems/consecutive-numbers/) 🚀
 - [SQL `JOIN` Explained](https://www.w3schools.com/sql/sql_join.asp)

From 4a354912f8fbc2cb3151d5393c3cbebdff77bc21 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 16:05:03 +0530
Subject: [PATCH 139/353] Create readme.md

---
 .../185. Department Top Three Salaries/readme.md                  | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md

diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md
new file mode 100644
index 0000000..e69de29

From 6a0767c7f7a4ecb547336eb53e46e92fe3e302bb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:20:38 +0530
Subject: [PATCH 140/353] Update readme.md

---
 .../readme.md                                 | 173 ++++++++++++++++++
 1 file changed, 173 insertions(+)

diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md
index e69de29..c5f802d 100644
--- a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md	
+++ b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md	
@@ -0,0 +1,173 @@
+Here’s the structured format for **LeetCode 185: Department Top Three Salaries**:
+
+---
+
+# **185. Department Top Three Salaries**
+
+## **Problem Statement**
+You are given two tables: `Employee` and `Department`.
+
+### **Employee Table**
+```
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| id           | int     |
+| name         | varchar |
+| salary       | int     |
+| departmentId | int     |
++--------------+---------+
+```
+- `id` is the primary key.
+- `departmentId` is a foreign key referencing `id` in the `Department` table.
+- Each row represents an employee with their `id`, `name`, `salary`, and `departmentId`.
+
+### **Department Table**
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| name        | varchar |
++-------------+---------+
+```
+- `id` is the primary key.
+- Each row represents a department with its `id` and `name`.
+
+### **Task:**
+Find employees who have a salary in the **top three unique salaries** in their respective departments.
+
+## **Example 1:**
+### **Input:**
+#### **Employee Table**
+```
++----+-------+--------+--------------+
+| id | name  | salary | departmentId |
++----+-------+--------+--------------+
+| 1  | Joe   | 85000  | 1            |
+| 2  | Henry | 80000  | 2            |
+| 3  | Sam   | 60000  | 2            |
+| 4  | Max   | 90000  | 1            |
+| 5  | Janet | 69000  | 1            |
+| 6  | Randy | 85000  | 1            |
+| 7  | Will  | 70000  | 1            |
++----+-------+--------+--------------+
+```
+#### **Department Table**
+```
++----+-------+
+| id | name  |
++----+-------+
+| 1  | IT    |
+| 2  | Sales |
++----+-------+
+```
+### **Output:**
+```
++------------+----------+--------+
+| Department | Employee | Salary |
++------------+----------+--------+
+| IT         | Max      | 90000  |
+| IT         | Joe      | 85000  |
+| IT         | Randy    | 85000  |
+| IT         | Will     | 70000  |
+| Sales      | Henry    | 80000  |
+| Sales      | Sam      | 60000  |
++------------+----------+--------+
+```
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using Self Join)**
+```sql
+SELECT d.Name as Department,
+       e.Name as Employee,
+       e.Salary as Salary
+FROM Department d, Employee e
+WHERE (
+    SELECT COUNT(DISTINCT Salary)
+    FROM Employee
+    WHERE Salary > e.Salary AND DepartmentId = d.Id
+) < 3 AND e.DepartmentId = d.Id
+ORDER BY d.Id, e.Salary DESC;
+```
+**Explanation:**
+- For each employee, we count how many distinct salaries are greater than theirs.
+- If fewer than 3 salaries are greater, the employee is in the **top three**.
+- We filter results by department and order by salary in descending order.
+
+---
+
+### **SQL Solution (Using Window Functions)**
+```sql
+WITH RankedSalaries AS (
+    SELECT e.name AS Employee, 
+           e.salary AS Salary, 
+           d.name AS Department,
+           DENSE_RANK() OVER (PARTITION BY e.departmentId ORDER BY e.salary DESC) AS rnk
+    FROM Employee e
+    JOIN Department d ON e.departmentId = d.id
+)
+SELECT Department, Employee, Salary
+FROM RankedSalaries
+WHERE rnk <= 3;
+```
+**Explanation:**
+- We use `DENSE_RANK()` to assign a rank to salaries within each department.
+- `PARTITION BY departmentId` ensures ranking is specific to each department.
+- Employees with `rnk <= 3` are returned.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def department_top_three_salaries(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame:
+    # Merge employee and department tables
+    employee = employee.merge(department, left_on='departmentId', right_on='id', suffixes=('', '_dept'))
+    
+    # Rank employees' salaries within each department
+    employee['rank'] = employee.groupby('departmentId')['salary'].rank(method='dense', ascending=False)
+    
+    # Filter top 3 salaries in each department
+    result = employee[employee['rank'] <= 3][['name_dept', 'name', 'salary']]
+    
+    # Rename columns to match the expected output
+    result.columns = ['Department', 'Employee', 'Salary']
+    
+    return result
+```
+**Explanation:**
+- Merge the `Employee` and `Department` tables.
+- Rank salaries within each department using `.rank()`.
+- Filter the top 3 ranked salaries per department.
+
+---
+
+## **File Structure**
+```
+📂 LeetCode185
+│── 📜 problem_statement.md
+│── 📜 sql_self_join_solution.sql
+│── 📜 sql_window_function_solution.sql
+│── 📜 pandas_solution.py
+│── 📜 README.md
+```
+- `problem_statement.md` → Contains the problem description and constraints.
+- `sql_self_join_solution.sql` → Contains the SQL solution using self-join.
+- `sql_window_function_solution.sql` → Contains the SQL solution using `DENSE_RANK()`.
+- `pandas_solution.py` → Contains the Pandas solution for Python users.
+- `README.md` → Provides an overview of the problem and solutions.
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 185](https://leetcode.com/problems/department-top-three-salaries/)
+- [SQL DENSE_RANK() Function](https://www.w3schools.com/sql/sql_functions.asp)
+- [Pandas Rank Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rank.html)
+
+---
+

From 1db3bab07df21e763bd2aa3efb412ce14c58eeab Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:21:57 +0530
Subject: [PATCH 141/353] Update readme.md

---
 .../185. Department Top Three Salaries/readme.md     | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md
index c5f802d..36117c3 100644
--- a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md	
+++ b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/readme.md	
@@ -1,14 +1,10 @@
-Here’s the structured format for **LeetCode 185: Department Top Three Salaries**:
-
----
-
 # **185. Department Top Three Salaries**
 
 ## **Problem Statement**
 You are given two tables: `Employee` and `Department`.
 
 ### **Employee Table**
-```
+```rb
 +--------------+---------+
 | Column Name  | Type    |
 +--------------+---------+
@@ -23,7 +19,7 @@ You are given two tables: `Employee` and `Department`.
 - Each row represents an employee with their `id`, `name`, `salary`, and `departmentId`.
 
 ### **Department Table**
-```
+```rb
 +-------------+---------+
 | Column Name | Type    |
 +-------------+---------+
@@ -54,7 +50,7 @@ Find employees who have a salary in the **top three unique salaries** in their r
 +----+-------+--------+--------------+
 ```
 #### **Department Table**
-```
+```rb
 +----+-------+
 | id | name  |
 +----+-------+
@@ -63,7 +59,7 @@ Find employees who have a salary in the **top three unique salaries** in their r
 +----+-------+
 ```
 ### **Output:**
-```
+```rb
 +------------+----------+--------+
 | Department | Employee | Salary |
 +------------+----------+--------+

From 02a3896ba38afe2db07b5bdf905b0b3626d1798b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:22:09 +0530
Subject: [PATCH 142/353] Update 185. Department Top Three Salaries.md

---
 .../185. Department Top Three Salaries.md     | 112 ------------------
 1 file changed, 112 deletions(-)

diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md
index 289efac..e69de29 100644
--- a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md	
+++ b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md	
@@ -1,112 +0,0 @@
-# 185. Department Top Three Salaries
-
-### Question
-```
- Table: Employee
-
-+--------------+---------+
-| Column Name  | Type    |
-+--------------+---------+
-| id           | int     |
-| name         | varchar |
-| salary       | int     |
-| departmentId | int     |
-+--------------+---------+
-```
-id is the primary key (column with unique values) for this table.
-departmentId is a foreign key (reference column) of the ID from the Department table.
-Each row of this table indicates the ID, name, and salary of an employee. It also contains the ID of their department.
- 
-
-Table: Department
-```
-+-------------+---------+
-| Column Name | Type    |
-+-------------+---------+
-| id          | int     |
-| name        | varchar |
-+-------------+---------+
-```
-id is the primary key (column with unique values) for this table.
-Each row of this table indicates the ID of a department and its name.
- 
-
-A company's executives are interested in seeing who earns the most money in each of the company's departments. A high earner in a department is an employee who has a salary in the top three unique salaries for that department.
-
-Write a solution to find the employees who are high earners in each of the departments.
-
-Return the result table in any order.
-
-The result format is in the following example.
-
- 
-
-Example 1:
-```
-Input: 
-Employee table:
-+----+-------+--------+--------------+
-| id | name  | salary | departmentId |
-+----+-------+--------+--------------+
-| 1  | Joe   | 85000  | 1            |
-| 2  | Henry | 80000  | 2            |
-| 3  | Sam   | 60000  | 2            |
-| 4  | Max   | 90000  | 1            |
-| 5  | Janet | 69000  | 1            |
-| 6  | Randy | 85000  | 1            |
-| 7  | Will  | 70000  | 1            |
-+----+-------+--------+--------------+
-```
-```
-Department table:
-+----+-------+
-| id | name  |
-+----+-------+
-| 1  | IT    |
-| 2  | Sales |
-+----+-------+
-Output: 
-+------------+----------+--------+
-| Department | Employee | Salary |
-+------------+----------+--------+
-| IT         | Max      | 90000  |
-| IT         | Joe      | 85000  |
-| IT         | Randy    | 85000  |
-| IT         | Will     | 70000  |
-| Sales      | Henry    | 80000  |
-| Sales      | Sam      | 60000  |
-+------------+----------+--------+
-```
-Explanation: 
-In the IT department:
-- Max earns the highest unique salary
-- Both Randy and Joe earn the second-highest unique salary
-- Will earns the third-highest unique salary
-
-In the Sales department:
-- Henry earns the highest salary
-- Sam earns the second-highest salary
-- There is no third-highest salary as there are only two employees
- 
-
-Constraints:
-
-There are no employees with the exact same name, salary and department.
-
-
-## Solution
-
-
-```MySQL
-# Write your MySQL query statement below
-SELECT d.Name as Department,
-e.Name as Employee,
-e.Salary as Salary
-FROM Department d, Employee e
-WHERE(
-    SELECT COUNT(distinct Salary)
-    FROM Employee
-    WHERE Salary > e.Salary AND DepartmentId = d.Id
-) < 3 AND e.DepartmentId = d.Id
-ORDER BY d.Id, e.Salary desc;
-```
\ No newline at end of file

From 31f2a4b8623dcf546962af96b9af572b03fb6bb8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:24:11 +0530
Subject: [PATCH 143/353] add

---
 .../185. Department Top Three Salaries.md      |  0
 .../185. Department Top Three Salaries.py      | 18 ++++++++++++++++++
 2 files changed, 18 insertions(+)
 delete mode 100644 LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md
 create mode 100644 LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.py

diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.md
deleted file mode 100644
index e69de29..0000000
diff --git a/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.py b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.py
new file mode 100644
index 0000000..ff2e501
--- /dev/null
+++ b/LeetCode SQL 50 Solution/185. Department Top Three Salaries/185. Department Top Three Salaries.py	
@@ -0,0 +1,18 @@
+### **Pandas Solution**
+
+import pandas as pd
+
+def department_top_three_salaries(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame:
+    # Merge employee and department tables
+    employee = employee.merge(department, left_on='departmentId', right_on='id', suffixes=('', '_dept'))
+    
+    # Rank employees' salaries within each department
+    employee['rank'] = employee.groupby('departmentId')['salary'].rank(method='dense', ascending=False)
+    
+    # Filter top 3 salaries in each department
+    result = employee[employee['rank'] <= 3][['name_dept', 'name', 'salary']]
+    
+    # Rename columns to match the expected output
+    result.columns = ['Department', 'Employee', 'Salary']
+    
+    return result
\ No newline at end of file

From 4c9e1269b9d94b5765bd6659911234a8885bdf55 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:24:33 +0530
Subject: [PATCH 144/353] Update 196. Delete Duplicate Emails.sql

---
 .../196. Delete Duplicate Emails.sql          | 50 -------------------
 1 file changed, 50 deletions(-)

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql
index 214afb4..1b08ad5 100644
--- a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql	
+++ b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql	
@@ -1,53 +1,3 @@
-196. Delete Duplicate Emails
-Solved
-Easy
-Topics
-Companies
-SQL Schema
-Pandas Schema
-Table: Person
-
-+-------------+---------+
-| Column Name | Type    |
-+-------------+---------+
-| id          | int     |
-| email       | varchar |
-+-------------+---------+
-id is the primary key (column with unique values) for this table.
-Each row of this table contains an email. The emails will not contain uppercase letters.
- 
-
-Write a solution to delete all duplicate emails, keeping only one unique email with the smallest id.
-
-For SQL users, please note that you are supposed to write a DELETE statement and not a SELECT one.
-
-For Pandas users, please note that you are supposed to modify Person in place.
-
-After running your script, the answer shown is the Person table. The driver will first compile and run your piece of code and then show the Person table. The final order of the Person table does not matter.
-
-The result format is in the following example.
-
- 
-
-Example 1:
-
-Input: 
-Person table:
-+----+------------------+
-| id | email            |
-+----+------------------+
-| 1  | john@example.com |
-| 2  | bob@example.com  |
-| 3  | john@example.com |
-+----+------------------+
-Output: 
-+----+------------------+
-| id | email            |
-+----+------------------+
-| 1  | john@example.com |
-| 2  | bob@example.com  |
-+----+------------------+
-Explanation: john@example.com is repeated two times. We keep the row with the smallest Id = 1.
 
 
 

From 34e43bc5cec3edebb190740218b6f74948b13baa Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:24:46 +0530
Subject: [PATCH 145/353] Update 196. Delete Duplicate Emails.sql

---
 .../196. Delete Duplicate Emails.sql                           | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql
index 1b08ad5..a262da8 100644
--- a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql	
+++ b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql	
@@ -1,6 +1,3 @@
-
-
-
 # Write your MySQL query statement below
 # Write your MySQL query statement below
 DELETE p2 FROM Person p1 

From ae50bc7b7d688270fdfac7eb88e69af62d8ab016 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:25:13 +0530
Subject: [PATCH 146/353] Create 196. Delete Duplicate Emails.py

---
 .../196. Delete Duplicate Emails/196. Delete Duplicate Emails.py  | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.py

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.py b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.py
new file mode 100644
index 0000000..e69de29

From 032647a48d6d28638171107feeeddebdae3e8c13 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:25:28 +0530
Subject: [PATCH 147/353] Create readme.md

---
 LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md
new file mode 100644
index 0000000..e69de29

From 5130e25a6f2e34619bab63290ccb4db7f130c8be Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:27:36 +0530
Subject: [PATCH 148/353] QER

---
 .../196. Delete Duplicate Emails.sql          |   1 +
 .../196. Delete Duplicate Emails/readme.md    | 118 ++++++++++++++++++
 2 files changed, 119 insertions(+)

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql
index a262da8..b9596df 100644
--- a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql	
+++ b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.sql	
@@ -1,3 +1,4 @@
+-- 196. Delete Duplicate Emails
 # Write your MySQL query statement below
 # Write your MySQL query statement below
 DELETE p2 FROM Person p1 
diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md
index e69de29..8e355db 100644
--- a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md	
+++ b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/readme.md	
@@ -0,0 +1,118 @@
+
+# **196. Delete Duplicate Emails**
+
+## **Problem Statement**
+You are given a table called `Person`, which stores email addresses.
+
+### **Person Table**
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| email       | varchar |
++-------------+---------+
+```
+- `id` is the **primary key**.
+- Each row contains an **email address**.
+- All emails are in **lowercase**.
+
+### **Task:**
+Delete all **duplicate emails**, keeping only **one unique email** with the **smallest id**.
+
+---
+
+## **Example 1:**
+### **Input:**
+#### **Person Table**
+```
++----+------------------+
+| id | email            |
++----+------------------+
+| 1  | john@example.com |
+| 2  | bob@example.com  |
+| 3  | john@example.com |
++----+------------------+
+```
+### **Output:**
+```
++----+------------------+
+| id | email            |
++----+------------------+
+| 1  | john@example.com |
+| 2  | bob@example.com  |
++----+------------------+
+```
+### **Explanation:**
+- `john@example.com` appears **twice**.
+- We keep the row with the **smallest `id`** (`id = 1`).
+- The duplicate (`id = 3`) is **deleted**.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using Self Join)**
+```sql
+DELETE p2 FROM Person p1 
+JOIN Person p2 
+ON p1.email = p2.email AND p1.id < p2.id;
+```
+**Explanation:**
+- `p1` and `p2` refer to the **same table** (`Person`).
+- We **join** them on `email` to find duplicates.
+- If `p1.id < p2.id`, we delete `p2`, keeping the row with the **smallest id**.
+
+---
+
+### **SQL Solution (Using Subquery)**
+```sql
+DELETE FROM Person
+WHERE id NOT IN (
+    SELECT MIN(id) FROM Person GROUP BY email
+);
+```
+**Explanation:**
+- We **group** by `email` and **select the smallest `id`** for each email.
+- The `DELETE` statement removes rows **not in** this list.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def delete_duplicate_emails(person: pd.DataFrame) -> None:
+    # Keep only the first occurrence of each email (smallest id)
+    person.drop_duplicates(subset=['email'], keep='first', inplace=True)
+```
+**Explanation:**
+- `drop_duplicates(subset=['email'], keep='first', inplace=True)`:
+  - Keeps only **the first occurrence** of each email.
+  - Ensures **modification happens in place**.
+
+---
+
+## **File Structure**
+```
+📂 LeetCode196
+│── 📜 problem_statement.md
+│── 📜 sql_self_join_solution.sql
+│── 📜 sql_subquery_solution.sql
+│── 📜 pandas_solution.py
+│── 📜 README.md
+```
+- `problem_statement.md` → Contains the problem description.
+- `sql_self_join_solution.sql` → Contains the SQL solution using **JOIN**.
+- `sql_subquery_solution.sql` → Contains the SQL solution using **Subquery**.
+- `pandas_solution.py` → Contains the Pandas solution for Python users.
+- `README.md` → Provides an overview of the problem and solutions.
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 196](https://leetcode.com/problems/delete-duplicate-emails/)
+- [SQL DELETE Statement](https://www.w3schools.com/sql/sql_delete.asp)
+- [Pandas drop_duplicates()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html)
+
+---

From bd92a631af4209f2a9bb04d614486ec8906d4c92 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:28:25 +0530
Subject: [PATCH 149/353] Update 196. Delete Duplicate Emails.py

---
 .../196. Delete Duplicate Emails.py                          | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.py b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.py
index e69de29..7bc23de 100644
--- a/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.py	
+++ b/LeetCode SQL 50 Solution/196. Delete Duplicate Emails/196. Delete Duplicate Emails.py	
@@ -0,0 +1,5 @@
+import pandas as pd
+
+def delete_duplicate_emails(person: pd.DataFrame) -> None:
+    # Keep only the first occurrence of each email (smallest id)
+    person.drop_duplicates(subset=['email'], keep='first', inplace=True)
\ No newline at end of file

From 32805b808815a38daf5e860aa25ab9fd3c6a8768 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:30:47 +0530
Subject: [PATCH 150/353] Create Rising Temperature.py

---
 .../197. Rising Temperature/Rising Temperature.py                 | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/197. Rising Temperature/Rising Temperature.py

diff --git a/LeetCode SQL 50 Solution/197. Rising Temperature/Rising Temperature.py b/LeetCode SQL 50 Solution/197. Rising Temperature/Rising Temperature.py
new file mode 100644
index 0000000..e69de29

From e657134505b650f2adea373794fb2c1448f440f3 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:31:27 +0530
Subject: [PATCH 151/353] Create readme.md

---
 .../197. Rising Temperature/readme.md         | 157 ++++++++++++++++++
 1 file changed, 157 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/197. Rising Temperature/readme.md

diff --git a/LeetCode SQL 50 Solution/197. Rising Temperature/readme.md b/LeetCode SQL 50 Solution/197. Rising Temperature/readme.md
new file mode 100644
index 0000000..79d81cd
--- /dev/null
+++ b/LeetCode SQL 50 Solution/197. Rising Temperature/readme.md	
@@ -0,0 +1,157 @@
+
+# **197. Rising Temperature**
+
+## **Problem Statement**
+You are given a table called `Weather`, which contains daily temperature records.
+
+### **Weather Table**
+```
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| id            | int     |
+| recordDate    | date    |
+| temperature   | int     |
++---------------+---------+
+```
+- `id` is the **primary key**.
+- Each row contains:
+  - `recordDate`: The **date** of the temperature record.
+  - `temperature`: The **temperature recorded** on that date.
+
+### **Task:**
+Find all `id`s where the **temperature** is **higher** than the **previous day's temperature**.
+
+---
+
+## **Example 1:**
+### **Input:**
+#### **Weather Table**
+```
++----+------------+-------------+
+| id | recordDate | temperature |
++----+------------+-------------+
+| 1  | 2024-08-01 | 30          |
+| 2  | 2024-08-02 | 32          |
+| 3  | 2024-08-03 | 31          |
+| 4  | 2024-08-04 | 35          |
+| 5  | 2024-08-05 | 36          |
++----+------------+-------------+
+```
+### **Output:**
+```
++----+
+| id |
++----+
+| 2  |
+| 4  |
+| 5  |
++----+
+```
+### **Explanation:**
+- `id = 2`: `32 > 30` (08-02 > 08-01 ✅)
+- `id = 3`: `31 < 32` (Skipped ❌)
+- `id = 4`: `35 > 31` (08-04 > 08-03 ✅)
+- `id = 5`: `36 > 35` (08-05 > 08-04 ✅)
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using `LAG()` Window Function)**
+```sql
+WITH PreviousWeatherData AS
+(
+    SELECT 
+        id,
+        recordDate,
+        temperature, 
+        LAG(temperature, 1) OVER (ORDER BY recordDate) AS PreviousTemperature,
+        LAG(recordDate, 1) OVER (ORDER BY recordDate) AS PreviousRecordDate
+    FROM 
+        Weather
+)
+SELECT 
+    id 
+FROM 
+    PreviousWeatherData
+WHERE 
+    temperature > PreviousTemperature
+AND 
+    recordDate = DATE_ADD(PreviousRecordDate, INTERVAL 1 DAY);
+```
+**Explanation:**
+- We use `LAG()` to fetch:
+  - The **previous day's temperature**.
+  - The **previous day's date**.
+- The `WHERE` clause filters rows where:
+  - The **temperature is higher than the previous day**.
+  - The **date difference is exactly 1 day**.
+
+---
+
+### **SQL Solution (Using Self Join)**
+```sql
+SELECT w1.id
+FROM Weather w1
+JOIN Weather w2
+ON DATEDIFF(w1.recordDate, w2.recordDate) = 1
+AND w1.temperature > w2.temperature;
+```
+**Explanation:**
+- We **self-join** the `Weather` table.
+- The condition `DATEDIFF(w1.recordDate, w2.recordDate) = 1` ensures:
+  - We are comparing **consecutive days**.
+- The condition `w1.temperature > w2.temperature` ensures:
+  - We select days where the **temperature increased**.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def rising_temperature(weather: pd.DataFrame) -> pd.DataFrame:
+    weather.sort_values(by="recordDate", inplace=True)
+    weather["PreviousTemp"] = weather["temperature"].shift(1)
+    weather["PreviousDate"] = weather["recordDate"].shift(1)
+    
+    result = weather[
+        (weather["temperature"] > weather["PreviousTemp"]) &
+        ((weather["recordDate"] - weather["PreviousDate"]).dt.days == 1)
+    ]
+    
+    return result[["id"]]
+```
+**Explanation:**
+- We **sort** by `recordDate`.
+- We **shift** the temperature and date to get the previous day's values.
+- We **filter** where:
+  - Temperature **increased**.
+  - Date difference is **1 day**.
+
+---
+
+## **File Structure**
+```
+📂 LeetCode197
+│── 📜 problem_statement.md
+│── 📜 sql_lag_solution.sql
+│── 📜 sql_self_join_solution.sql
+│── 📜 pandas_solution.py
+│── 📜 README.md
+```
+- `problem_statement.md` → Contains the problem description.
+- `sql_lag_solution.sql` → Contains the SQL solution using **LAG()**.
+- `sql_self_join_solution.sql` → Contains the SQL solution using **Self Join**.
+- `pandas_solution.py` → Contains the Pandas solution.
+- `README.md` → Provides an overview of the problem and solutions.
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 197](https://leetcode.com/problems/rising-temperature/)
+- [SQL LAG() Function](https://www.w3schools.com/sql/sql_ref_window_functions.asp)
+- [SQL JOIN](https://www.w3schools.com/sql/sql_join.asp)
+- [Pandas shift()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shift.html)
+

From e3c908172155e447548bdbc553b80f2f79f1465a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:35:55 +0530
Subject: [PATCH 152/353] oi

---
 .../550. Game Play Analysis IV/550. Game Play Analysis IV.sql   | 2 +-
 LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md   | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md

diff --git a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.sql b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.sql
index dda489d..6a83794 100644
--- a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.sql	
+++ b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.sql	
@@ -7,7 +7,7 @@ Table: Activity
 +--------------+---------+
 | player_id    | int     |
 | device_id    | int     |
-| event_date   | date    |
+-- | event_date   | date    |
 | games_played | int     |
 +--------------+---------+
 (player_id, event_date) is the primary key (combination of columns with unique values) of this table.
diff --git a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md
new file mode 100644
index 0000000..e69de29

From b7ea00aa90c012f00a3d9947a710c1dbf68af84b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:36:05 +0530
Subject: [PATCH 153/353] Update readme.md

---
 .../550. Game Play Analysis IV/readme.md      | 172 ++++++++++++++++++
 1 file changed, 172 insertions(+)

diff --git a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md
index e69de29..f6974b4 100644
--- a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md	
+++ b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md	
@@ -0,0 +1,172 @@
+Here’s the structured format for **LeetCode 550: Game Play Analysis IV**:
+
+---
+
+# **550. Game Play Analysis IV**
+
+## **Problem Statement**
+You are given a table named `Activity`, which logs the gaming activity of players.
+
+### **Activity Table**
+```
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| player_id    | int     |
+| device_id    | int     |
+| event_date   | date    |
+| games_played | int     |
++--------------+---------+
+```
+- **(player_id, event_date)** is the **primary key**.
+- Each row contains:
+  - `player_id`: The ID of the player.
+  - `event_date`: The date when the player logged in.
+  - `games_played`: The number of games played before logging out.
+
+### **Task:**
+Find the **fraction** of players who logged in **again** the day after their **first login date**, rounded to **2 decimal places**.
+
+---
+
+## **Example 1:**
+### **Input:**
+#### **Activity Table**
+```
++-----------+-----------+------------+--------------+
+| player_id | device_id | event_date | games_played |
++-----------+-----------+------------+--------------+
+| 1         | 2         | 2016-03-01 | 5            |
+| 1         | 2         | 2016-03-02 | 6            |
+| 2         | 3         | 2017-06-25 | 1            |
+| 3         | 1         | 2016-03-02 | 0            |
+| 3         | 4         | 2018-07-03 | 5            |
++-----------+-----------+------------+--------------+
+```
+### **Output:**
+```
++-----------+
+| fraction  |
++-----------+
+| 0.33      |
++-----------+
+```
+### **Explanation:**
+- `player_id = 1`: First login on **2016-03-01**, logs in again on **2016-03-02** ✅
+- `player_id = 2`: First login on **2017-06-25**, no next-day login ❌
+- `player_id = 3`: First login on **2016-03-02**, no next-day login ❌
+
+Total players = **3**, Players who logged in the next day = **1** → **1 / 3 = 0.33** ✅
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using `JOIN` & `DATEDIFF`)**
+```sql
+SELECT 
+    ROUND((
+        SELECT COUNT(DISTINCT a.player_id)
+        FROM Activity a
+        INNER JOIN (
+            SELECT player_id, MIN(event_date) AS first_login
+            FROM Activity
+            GROUP BY player_id
+        ) b
+        ON a.player_id = b.player_id
+        AND DATEDIFF(a.event_date, b.first_login) = 1
+    ) / 
+    (SELECT COUNT(DISTINCT player_id) FROM Activity), 2) AS fraction;
+```
+**Explanation:**
+1. **Find First Login Date per Player**
+   - `MIN(event_date) AS first_login`
+   - **Grouped by** `player_id`
+2. **Find Players Who Logged in on the Next Day**
+   - **Join** the table with itself.
+   - Use `DATEDIFF(a.event_date, b.first_login) = 1` to check next-day logins.
+   - Count unique `player_id`s.
+3. **Calculate Fraction**
+   - Divide by total distinct `player_id`s.
+   - Round to **2 decimal places**.
+
+---
+
+### **Alternative SQL Solution (Using `EXISTS`)**
+```sql
+SELECT ROUND(
+    (SELECT COUNT(DISTINCT player_id) 
+     FROM Activity a
+     WHERE EXISTS (
+         SELECT 1 FROM Activity b
+         WHERE a.player_id = b.player_id 
+         AND DATEDIFF(b.event_date, a.event_date) = 1
+     )) / 
+    (SELECT COUNT(DISTINCT player_id) FROM Activity), 2) AS fraction;
+```
+**Explanation:**
+- Checks if a player has **ANY** login exactly **one day after**.
+- Uses `EXISTS` to optimize performance.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def game_play_analysis(activity: pd.DataFrame) -> pd.DataFrame:
+    # Get first login date for each player
+    first_login = activity.groupby("player_id")["event_date"].min().reset_index()
+    first_login.columns = ["player_id", "first_login"]
+    
+    # Merge first login date with original table
+    merged = activity.merge(first_login, on="player_id")
+    
+    # Filter players who logged in the next day
+    next_day_logins = merged[
+        (merged["event_date"] - merged["first_login"]).dt.days == 1
+    ]["player_id"].nunique()
+    
+    # Total unique players
+    total_players = activity["player_id"].nunique()
+    
+    # Calculate fraction
+    fraction = round(next_day_logins / total_players, 2)
+    
+    return pd.DataFrame({"fraction": [fraction]})
+```
+**Explanation:**
+1. **Find First Login Date**
+   - Group by `player_id`, get `min(event_date)`.
+2. **Merge with Original Table**
+   - Check if `event_date - first_login = 1 day`.
+3. **Count Unique Players**
+   - Divide by total unique `player_id`s.
+
+---
+
+## **File Structure**
+```
+📂 LeetCode550
+│── 📜 problem_statement.md
+│── 📜 sql_solution.sql
+│── 📜 sql_exists_solution.sql
+│── 📜 pandas_solution.py
+│── 📜 README.md
+```
+- `problem_statement.md` → Contains the problem description.
+- `sql_solution.sql` → SQL solution using **JOIN & DATEDIFF**.
+- `sql_exists_solution.sql` → SQL solution using **EXISTS**.
+- `pandas_solution.py` → Pandas solution.
+- `README.md` → Overview of problem and solutions.
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 550](https://leetcode.com/problems/game-play-analysis-iv/)
+- [SQL `DATEDIFF()`](https://www.w3schools.com/sql/func_mysql_datediff.asp)
+- [Pandas `.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+
+---
+
+This structured format provides **clear problem understanding and efficient solutions**. 🚀
\ No newline at end of file

From 904d8b441f0cafec49add29906812655fec573d9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:37:47 +0530
Subject: [PATCH 154/353] Update readme.md

---
 .../550. Game Play Analysis IV/readme.md           | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md
index f6974b4..51b11bd 100644
--- a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md	
+++ b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md	
@@ -1,14 +1,10 @@
-Here’s the structured format for **LeetCode 550: Game Play Analysis IV**:
-
----
-
 # **550. Game Play Analysis IV**
 
 ## **Problem Statement**
 You are given a table named `Activity`, which logs the gaming activity of players.
 
 ### **Activity Table**
-```
+```rb
 +--------------+---------+
 | Column Name  | Type    |
 +--------------+---------+
@@ -32,7 +28,7 @@ Find the **fraction** of players who logged in **again** the day after their **f
 ## **Example 1:**
 ### **Input:**
 #### **Activity Table**
-```
+```rb
 +-----------+-----------+------------+--------------+
 | player_id | device_id | event_date | games_played |
 +-----------+-----------+------------+--------------+
@@ -44,7 +40,7 @@ Find the **fraction** of players who logged in **again** the day after their **f
 +-----------+-----------+------------+--------------+
 ```
 ### **Output:**
-```
+```rb
 +-----------+
 | fraction  |
 +-----------+
@@ -166,7 +162,3 @@ def game_play_analysis(activity: pd.DataFrame) -> pd.DataFrame:
 - [LeetCode Problem 550](https://leetcode.com/problems/game-play-analysis-iv/)
 - [SQL `DATEDIFF()`](https://www.w3schools.com/sql/func_mysql_datediff.asp)
 - [Pandas `.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-
----
-
-This structured format provides **clear problem understanding and efficient solutions**. 🚀
\ No newline at end of file

From d33a1c426c24b58375dca97f18937c3a9e9e444a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:38:25 +0530
Subject: [PATCH 155/353] Create 550. Game Play Analysis IV.py

---
 .../550. Game Play Analysis IV.py             | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.py

diff --git a/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.py b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.py
new file mode 100644
index 0000000..37cc030
--- /dev/null
+++ b/LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.py	
@@ -0,0 +1,22 @@
+import pandas as pd
+
+def game_play_analysis(activity: pd.DataFrame) -> pd.DataFrame:
+    # Get first login date for each player
+    first_login = activity.groupby("player_id")["event_date"].min().reset_index()
+    first_login.columns = ["player_id", "first_login"]
+    
+    # Merge first login date with original table
+    merged = activity.merge(first_login, on="player_id")
+    
+    # Filter players who logged in the next day
+    next_day_logins = merged[
+        (merged["event_date"] - merged["first_login"]).dt.days == 1
+    ]["player_id"].nunique()
+    
+    # Total unique players
+    total_players = activity["player_id"].nunique()
+    
+    # Calculate fraction
+    fraction = round(next_day_logins / total_players, 2)
+    
+    return pd.DataFrame({"fraction": [fraction]})
\ No newline at end of file

From 8fcb4beec9439d4df15533d956da1fd58d84aac0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:43:19 +0530
Subject: [PATCH 156/353] Create readme.md

---
 .../570. Managers with at Least 5 Direct Reports/readme.md        | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md

diff --git a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md
new file mode 100644
index 0000000..e69de29

From 803c3594413fabc39f6afa3bb4a584b4f32c8ca6 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:43:29 +0530
Subject: [PATCH 157/353] Update readme.md

---
 .../readme.md                                 | 147 ++++++++++++++++++
 1 file changed, 147 insertions(+)

diff --git a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md
index e69de29..5ff9e4d 100644
--- a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md	
+++ b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md	
@@ -0,0 +1,147 @@
+Below is a structured README.md snippet for **LeetCode 570: Managers with at Least 5 Direct Reports**, including the problem statement, examples, solution approaches (with SQL and Pandas), file structure, and useful links.
+
+---
+
+# **570. Managers with at Least 5 Direct Reports**
+
+## **Problem Statement**
+You are given a table `Employee` that holds all employee records, including their managers. Every employee has an `id`, a `name`, a `department`, and a `managerId`.
+
+### **Employee Table**
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| name        | varchar |
+| department  | varchar |
+| managerId   | int     |
++-------------+---------+
+```
+- `id` is the **primary key**.
+- `managerId` is a **foreign key** that references the `id` of a manager in the same table.
+- If `managerId` is `null`, then the employee does not have a manager.
+- No employee will be the manager of themselves.
+
+### **Task:**
+Write a solution to find all managers (i.e., employees who appear as a managerId in the table) with **at least five direct reports**.
+
+The result table should display the manager’s name in any order.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+#### **Employee Table**
+```
++-----+-------+------------+-----------+
+| id  | name  | department | managerId |
++-----+-------+------------+-----------+
+| 101 | John  | A          | null      |
+| 102 | Dan   | A          | 101       |
+| 103 | James | A          | 101       |
+| 104 | Amy   | A          | 101       |
+| 105 | Anne  | A          | 101       |
+| 106 | Ron   | B          | 101       |
++-----+-------+------------+-----------+
+```
+
+### **Output:**
+```
++------+
+| name |
++------+
+| John |
++------+
+```
+
+### **Explanation:**
+- **John** (id = 101) is the only manager who has **5 direct reports** (ids 102, 103, 104, 105, and 106).
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using Subquery)**
+```sql
+SELECT name
+FROM Employee
+WHERE id IN (
+    SELECT managerId
+    FROM Employee
+    GROUP BY managerId
+    HAVING COUNT(*) >= 5
+);
+```
+**Explanation:**
+- The subquery groups the `Employee` table by `managerId` and counts the number of direct reports.
+- Only managers with a count of **5 or more** are selected.
+- The outer query then retrieves the names of those managers.
+
+---
+
+### **SQL Solution (Using JOIN and Window Functions)**
+```sql
+SELECT name
+FROM Employee
+JOIN (
+    SELECT managerId
+    FROM Employee
+    GROUP BY managerId
+    HAVING COUNT(*) >= 5
+) AS managers
+ON Employee.id = managers.managerId;
+```
+**Explanation:**
+- The inner query identifies all `managerId`s with **at least five** direct reports.
+- The outer query then joins on the `Employee` table to fetch the corresponding manager names.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def managers_with_five_reports(employee: pd.DataFrame) -> None:
+    # Count direct reports for each manager
+    report_counts = employee.groupby('managerId').size().reset_index(name='report_count')
+    
+    # Identify managerIds with at least five direct reports
+    valid_managers = report_counts[report_counts['report_count'] >= 5]['managerId']
+    
+    # Filter the Employee table to get manager names
+    # Note: Since managerId can be null, we ignore them during merge.
+    result = employee[employee['id'].isin(valid_managers)][['name']]
+    
+    # Modify the original DataFrame in place if required.
+    employee = result
+    print(result)
+```
+**Explanation:**
+- Group the table by `managerId` and count the number of direct reports.
+- Filter out the managers having at least 5 direct reports.
+- Finally, retrieve the names of these managers.
+
+---
+
+## **File Structure**
+```
+LeetCode570/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_subquery_solution.sql   # SQL solution using subquery.
+├── sql_join_solution.sql       # SQL solution using JOIN.
+├── pandas_solution.py          # Pandas solution for Python users.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 570](https://leetcode.com/problems/managers-with-at-least-5-direct-reports/)
+- [SQL DELETE and JOIN Syntax](https://www.w3schools.com/sql/sql_join.asp)
+- [Pandas Documentation](https://pandas.pydata.org/docs/)
+
+---
+
+This structured format provides a clear problem overview, multiple solution approaches, and necessary file organization to help contributors quickly understand and navigate the project. Happy coding! 🚀
\ No newline at end of file

From 49adef990a19a5181711fc5396470aab874fd696 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:44:30 +0530
Subject: [PATCH 158/353] Update readme.md

---
 .../570. Managers with at Least 5 Direct Reports/readme.md | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md
index 5ff9e4d..407b040 100644
--- a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md	
+++ b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/readme.md	
@@ -1,7 +1,3 @@
-Below is a structured README.md snippet for **LeetCode 570: Managers with at Least 5 Direct Reports**, including the problem statement, examples, solution approaches (with SQL and Pandas), file structure, and useful links.
-
----
-
 # **570. Managers with at Least 5 Direct Reports**
 
 ## **Problem Statement**
@@ -142,6 +138,3 @@ LeetCode570/
 - [SQL DELETE and JOIN Syntax](https://www.w3schools.com/sql/sql_join.asp)
 - [Pandas Documentation](https://pandas.pydata.org/docs/)
 
----
-
-This structured format provides a clear problem overview, multiple solution approaches, and necessary file organization to help contributors quickly understand and navigate the project. Happy coding! 🚀
\ No newline at end of file

From a2a2b2b744534ff2e8659ec722d59c824f34c00d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:44:44 +0530
Subject: [PATCH 159/353] Create 570. Managers with at Least 5 Direct
 Reports.py

---
 .../570. Managers with at Least 5 Direct Reports.py               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.py

diff --git a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.py b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.py
new file mode 100644
index 0000000..e69de29

From f8cf2775bded05747e9290279ce50f295baabbcc Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:45:39 +0530
Subject: [PATCH 160/353] Update 570. Managers with at Least 5 Direct
 Reports.py

---
 ...0. Managers with at Least 5 Direct Reports.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.py b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.py
index e69de29..73f3df8 100644
--- a/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.py	
+++ b/LeetCode SQL 50 Solution/570. Managers with at Least 5 Direct Reports/570. Managers with at Least 5 Direct Reports.py	
@@ -0,0 +1,16 @@
+import pandas as pd
+
+def managers_with_five_reports(employee: pd.DataFrame) -> None:
+    # Count direct reports for each manager
+    report_counts = employee.groupby('managerId').size().reset_index(name='report_count')
+    
+    # Identify managerIds with at least five direct reports
+    valid_managers = report_counts[report_counts['report_count'] >= 5]['managerId']
+    
+    # Filter the Employee table to get manager names
+    # Note: Since managerId can be null, we ignore them during merge.
+    result = employee[employee['id'].isin(valid_managers)][['name']]
+    
+    # Modify the original DataFrame in place if required.
+    employee = result
+    print(result)
\ No newline at end of file

From c21bd402d5040e67bf440ec215ff2ccb96ae8b3a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:47:17 +0530
Subject: [PATCH 161/353] Create readme.md

---
 LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md b/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md
new file mode 100644
index 0000000..e69de29

From ebec9dc0150083e141de3d5b947b5c637602469f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:47:32 +0530
Subject: [PATCH 162/353] Create 584. Find Customer Referee.py

---
 .../584. Find Customer Referee/584. Find Customer Referee.py      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py
new file mode 100644
index 0000000..e69de29

From fdd46337f0c9683bcb630d2fb757828dfa86db26 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:48:17 +0530
Subject: [PATCH 163/353] Update readme.md

---
 .../584. Find Customer Referee/readme.md      | 111 ++++++++++++++++++
 1 file changed, 111 insertions(+)

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md b/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md
index e69de29..75a745c 100644
--- a/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md	
+++ b/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md	
@@ -0,0 +1,111 @@
+Below is a structured README.md snippet for **LeetCode 584: Find Customer Referee**:
+
+---
+
+# **584. Find Customer Referee**
+
+## **Problem Statement**
+You are given a table `Customer` that stores customer details along with the ID of the customer who referred them.
+
+### **Customer Table**
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| name        | varchar |
+| referee_id  | int     |
++-------------+---------+
+```
+- `id` is the **primary key**.
+- Each row represents a customer with their `id`, `name`, and `referee_id`.
+- `referee_id` indicates the customer who referred them. It can be **NULL** if no one referred the customer.
+
+### **Task:**
+Find the names of the customers who are **not referred** by the customer with `id = 2`.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+#### **Customer Table**
+```
++----+------+------------+
+| id | name | referee_id |
++----+------+------------+
+| 1  | Will | null       |
+| 2  | Jane | null       |
+| 3  | Alex | 2          |
+| 4  | Bill | null       |
+| 5  | Zack | 1          |
+| 6  | Mark | 2          |
++----+------+------------+
+```
+
+### **Output:**
+```
++------+
+| name |
++------+
+| Will |
+| Jane |
+| Bill |
+| Zack |
++------+
+```
+
+### **Explanation:**
+- **Alex** (id = 3) and **Mark** (id = 6) are referred by the customer with `id = 2` and are excluded.
+- The remaining customers (**Will**, **Jane**, **Bill**, **Zack**) are not referred by the customer with `id = 2`.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using WHERE Clause)**
+```sql
+SELECT name
+FROM Customer
+WHERE referee_id != 2 OR referee_id IS NULL;
+```
+**Explanation:**
+- The query selects customer names where `referee_id` is either not equal to `2` or is `NULL`.
+- This effectively filters out customers referred by the customer with `id = 2`.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def find_customer_referee(customer: pd.DataFrame) -> pd.DataFrame:
+    # Filter rows where referee_id is not equal to 2 or is null
+    result = customer[(customer['referee_id'] != 2) | (customer['referee_id'].isnull())][['name']]
+    return result
+```
+**Explanation:**
+- The Pandas solution filters the DataFrame for rows where `referee_id` is not 2 or is `NaN`.
+- It then returns the `name` column containing the desired customer names.
+
+---
+
+## **File Structure**
+```
+LeetCode584/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solution.
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 584](https://leetcode.com/problems/find-customer-referee/)
+- [SQL WHERE Clause Documentation](https://www.w3schools.com/sql/sql_where.asp)
+- [Pandas Filtering DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
+
+---
+
+This structured format provides a clear problem overview, multiple solution approaches, file organization, and useful references to help contributors understand and work on the problem. Happy coding! 🚀
\ No newline at end of file

From 39c5827749ad6f822259a8f5c388457b331cf141 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:48:31 +0530
Subject: [PATCH 164/353] Update readme.md

---
 .../584. Find Customer Referee/readme.md                     | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md b/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md
index 75a745c..cb973db 100644
--- a/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md	
+++ b/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md	
@@ -1,6 +1,4 @@
-Below is a structured README.md snippet for **LeetCode 584: Find Customer Referee**:
 
----
 
 # **584. Find Customer Referee**
 
@@ -106,6 +104,3 @@ LeetCode584/
 - [SQL WHERE Clause Documentation](https://www.w3schools.com/sql/sql_where.asp)
 - [Pandas Filtering DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
 
----
-
-This structured format provides a clear problem overview, multiple solution approaches, file organization, and useful references to help contributors understand and work on the problem. Happy coding! 🚀
\ No newline at end of file

From d21d28ba2741e3a2e65785594884635a124b93c7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:49:06 +0530
Subject: [PATCH 165/353] fiel

---
 .../584. Find Customer Referee.py                           | 6 ++++++
 .../584. Find Customer Referee/readme.md                    | 2 --
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py
index e69de29..6685391 100644
--- a/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py	
+++ b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py	
@@ -0,0 +1,6 @@
+import pandas as pd
+
+def find_customer_referee(customer: pd.DataFrame) -> pd.DataFrame:
+    # Filter rows where referee_id is not equal to 2 or is null
+    result = customer[(customer['referee_id'] != 2) | (customer['referee_id'].isnull())][['name']]
+    return result
\ No newline at end of file
diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md b/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md
index cb973db..9689cf0 100644
--- a/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md	
+++ b/LeetCode SQL 50 Solution/584. Find Customer Referee/readme.md	
@@ -1,5 +1,3 @@
-
-
 # **584. Find Customer Referee**
 
 ## **Problem Statement**

From 91e40bd9e315bd6f43eab1e34ba8251d3478f543 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:49:14 +0530
Subject: [PATCH 166/353] Update 584. Find Customer Referee.py

---
 .../584. Find Customer Referee.py                           | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py
index 6685391..e69de29 100644
--- a/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py	
+++ b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py	
@@ -1,6 +0,0 @@
-import pandas as pd
-
-def find_customer_referee(customer: pd.DataFrame) -> pd.DataFrame:
-    # Filter rows where referee_id is not equal to 2 or is null
-    result = customer[(customer['referee_id'] != 2) | (customer['referee_id'].isnull())][['name']]
-    return result
\ No newline at end of file

From 90489b9dc2f8d59909c0153f128b6a7aecf83036 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:49:19 +0530
Subject: [PATCH 167/353] Update 584. Find Customer Referee.py

---
 .../584. Find Customer Referee.py                           | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py
index e69de29..6685391 100644
--- a/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py	
+++ b/LeetCode SQL 50 Solution/584. Find Customer Referee/584. Find Customer Referee.py	
@@ -0,0 +1,6 @@
+import pandas as pd
+
+def find_customer_referee(customer: pd.DataFrame) -> pd.DataFrame:
+    # Filter rows where referee_id is not equal to 2 or is null
+    result = customer[(customer['referee_id'] != 2) | (customer['referee_id'].isnull())][['name']]
+    return result
\ No newline at end of file

From daf5cf655085514d64096d4cafae14abbe7d0ea9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:51:16 +0530
Subject: [PATCH 168/353] j

---
 .../585. Investments in 2016/585. Investments in 2016.py          | 0
 LeetCode SQL 50 Solution/585. Investments in 2016/readme.md       | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.py
 create mode 100644 LeetCode SQL 50 Solution/585. Investments in 2016/readme.md

diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.py b/LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.py
new file mode 100644
index 0000000..e69de29
diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md b/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md
new file mode 100644
index 0000000..e69de29

From 9e7cb82e52e5c000bff00039b854a4a739434bdc Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:51:30 +0530
Subject: [PATCH 169/353] Update readme.md

---
 .../585. Investments in 2016/readme.md        | 147 ++++++++++++++++++
 1 file changed, 147 insertions(+)

diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md b/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md
index e69de29..cdac45f 100644
--- a/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md	
+++ b/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md	
@@ -0,0 +1,147 @@
+Below is the structured README.md file for **LeetCode 585: Investments in 2016**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
+
+---
+
+# **585. Investments in 2016**
+
+## **Problem Statement**
+You are given a table `Insurance` that contains details about policy investments.
+
+### **Insurance Table**
+```
++-------------+-------+
+| Column Name | Type  |
++-------------+-------+
+| pid         | int   |
+| tiv_2015    | float |
+| tiv_2016    | float |
+| lat         | float |
+| lon         | float |
++-------------+-------+
+```
+- `pid` is the **primary key**.
+- `tiv_2015` is the total investment value in **2015**.
+- `tiv_2016` is the total investment value in **2016**.
+- `lat` and `lon` represent the **latitude** and **longitude** of the policyholder's city. Both values are guaranteed not to be `NULL`.
+
+### **Task:**
+Report the **sum** of all `tiv_2016` values (rounded to two decimal places) for all policyholders who:
+1. Have the **same `tiv_2015`** value as one or more other policyholders.
+2. Are **not located** in the same city as any other policyholder (i.e., the `(lat, lon)` attribute pair is **unique**).
+
+---
+
+## **Example 1:**
+
+### **Input:**
+#### **Insurance Table**
+```
++-----+----------+----------+-----+-----+
+| pid | tiv_2015 | tiv_2016 | lat | lon |
++-----+----------+----------+-----+-----+
+| 1   | 10       | 5        | 10  | 10  |
+| 2   | 20       | 20       | 20  | 20  |
+| 3   | 10       | 30       | 20  | 20  |
+| 4   | 10       | 40       | 40  | 40  |
++-----+----------+----------+-----+-----+
+```
+
+### **Output:**
+```
++----------+
+| tiv_2016 |
++----------+
+| 45.00    |
++----------+
+```
+
+### **Explanation:**
+- The policyholders with `tiv_2015 = 10` appear in multiple rows.
+- Among these, only the records with **unique locations** count.
+- Policy `pid = 1` and `pid = 4` meet both criteria, so the result is the sum of their `tiv_2016`: **5 + 40 = 45.00**.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using Window Functions)**
+```sql
+WITH InsuranceWithCounts AS (
+    SELECT
+        tiv_2016,
+        COUNT(*) OVER(PARTITION BY tiv_2015) AS tiv_2015_count,
+        COUNT(*) OVER(PARTITION BY lat, lon) AS city_count
+    FROM Insurance
+)
+SELECT ROUND(SUM(tiv_2016), 2) AS tiv_2016
+FROM InsuranceWithCounts
+WHERE tiv_2015_count > 1
+  AND city_count = 1;
+```
+**Explanation:**
+- The CTE `InsuranceWithCounts` computes:
+  - `tiv_2015_count`: Number of records with the same `tiv_2015`.
+  - `city_count`: Number of records with the same `(lat, lon)` pair.
+- The outer query filters rows where:
+  - `tiv_2015_count > 1` (i.e., policyholders share their 2015 investment value with others).
+  - `city_count = 1` (i.e., their location is unique).
+- Finally, it sums `tiv_2016` and rounds the result to 2 decimal places.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def investments_in_2016(insurance: pd.DataFrame) -> pd.DataFrame:
+    # Count the number of occurrences for each tiv_2015 value
+    insurance['tiv_2015_count'] = insurance.groupby('tiv_2015')['tiv_2015'].transform('count')
+    
+    # Count the number of occurrences for each (lat, lon) pair
+    insurance['city_count'] = insurance.groupby(['lat', 'lon'])['lat'].transform('count')
+    
+    # Filter rows that meet both criteria:
+    # 1. tiv_2015 appears more than once.
+    # 2. The location (lat, lon) is unique (appears only once).
+    valid_rows = insurance[(insurance['tiv_2015_count'] > 1) & (insurance['city_count'] == 1)]
+    
+    # Calculate the sum of tiv_2016 and round to 2 decimal places
+    total_tiv_2016 = round(valid_rows['tiv_2016'].sum(), 2)
+    
+    # Return result as a DataFrame
+    return pd.DataFrame({'tiv_2016': [total_tiv_2016]})
+
+# Example usage:
+# df = pd.read_csv('insurance.csv')
+# print(investments_in_2016(df))
+```
+**Explanation:**
+- The code computes two new columns:
+  - `tiv_2015_count` for the number of policyholders with the same 2015 investment.
+  - `city_count` for the count of policyholders in each unique city (using `(lat, lon)`).
+- Rows that meet the conditions are filtered.
+- The `tiv_2016` values of the valid rows are summed and rounded to 2 decimal places.
+- The result is returned as a DataFrame.
+
+---
+
+## **File Structure**
+```
+LeetCode585/
+├── problem_statement.md        # Contains the problem description and constraints.
+├── sql_solution.sql            # SQL solution using window functions.
+├── pandas_solution.py          # Pandas solution for Python users.
+├── README.md                   # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 585](https://leetcode.com/problems/investments-in-2016/)
+- [SQL Window Functions](https://www.w3schools.com/sql/sql_window.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+- [Pandas Transform Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.transform.html)
+
+---
+
+This structured format provides a clear overview of the problem and multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From ed601624f0f04558c10909548e74a767e3a69cbb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:51:45 +0530
Subject: [PATCH 170/353] Update readme.md

---
 LeetCode SQL 50 Solution/585. Investments in 2016/readme.md | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md b/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md
index cdac45f..4314981 100644
--- a/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md	
+++ b/LeetCode SQL 50 Solution/585. Investments in 2016/readme.md	
@@ -1,6 +1,4 @@
-Below is the structured README.md file for **LeetCode 585: Investments in 2016**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
 
----
 
 # **585. Investments in 2016**
 
@@ -141,7 +139,3 @@ LeetCode585/
 - [SQL Window Functions](https://www.w3schools.com/sql/sql_window.asp)
 - [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
 - [Pandas Transform Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.transform.html)
-
----
-
-This structured format provides a clear overview of the problem and multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From c329b6f251865ee367e6e9d9591e8fdbae16f634 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:52:19 +0530
Subject: [PATCH 171/353] Update 585. Investments in 2016.py

---
 .../585. Investments in 2016.py               | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.py b/LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.py
index e69de29..54c7c50 100644
--- a/LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.py	
+++ b/LeetCode SQL 50 Solution/585. Investments in 2016/585. Investments in 2016.py	
@@ -0,0 +1,19 @@
+import pandas as pd
+
+def investments_in_2016(insurance: pd.DataFrame) -> pd.DataFrame:
+    # Count the number of occurrences for each tiv_2015 value
+    insurance['tiv_2015_count'] = insurance.groupby('tiv_2015')['tiv_2015'].transform('count')
+    
+    # Count the number of occurrences for each (lat, lon) pair
+    insurance['city_count'] = insurance.groupby(['lat', 'lon'])['lat'].transform('count')
+    
+    # Filter rows that meet both criteria:
+    # 1. tiv_2015 appears more than once.
+    # 2. The location (lat, lon) is unique (appears only once).
+    valid_rows = insurance[(insurance['tiv_2015_count'] > 1) & (insurance['city_count'] == 1)]
+    
+    # Calculate the sum of tiv_2016 and round to 2 decimal places
+    total_tiv_2016 = round(valid_rows['tiv_2016'].sum(), 2)
+    
+    # Return result as a DataFrame
+    return pd.DataFrame({'tiv_2016': [total_tiv_2016]})
\ No newline at end of file

From e2a869d87aff427e7d61c1883c363f0d7f8dad51 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:55:08 +0530
Subject: [PATCH 172/353] Create 595. Big Countries.py

---
 LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py

diff --git a/LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py b/LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py
new file mode 100644
index 0000000..e69de29

From 38a8de491101e2fe7686f6834bfddb3c1ef6b3f0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:56:07 +0530
Subject: [PATCH 173/353] Create readme.md

---
 .../595. Big Countries/readme.md              | 142 ++++++++++++++++++
 1 file changed, 142 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/595. Big Countries/readme.md

diff --git a/LeetCode SQL 50 Solution/595. Big Countries/readme.md b/LeetCode SQL 50 Solution/595. Big Countries/readme.md
new file mode 100644
index 0000000..5f62064
--- /dev/null
+++ b/LeetCode SQL 50 Solution/595. Big Countries/readme.md	
@@ -0,0 +1,142 @@
+Below is the structured README.md snippet for **LeetCode 595: Big Countries**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
+
+---
+
+# **595. Big Countries**
+
+## **Problem Statement**
+You are given a table `World` that contains information about countries.
+
+### **World Table**
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| name        | varchar |
+| continent   | varchar |
+| area        | int     |
+| population  | int     |
+| gdp         | bigint  |
++-------------+---------+
+```
+- `name` is the **primary key**.
+- Each row contains:
+  - `name`: Name of the country.
+  - `continent`: Continent the country belongs to.
+  - `area`: Area of the country (in km²).
+  - `population`: Population of the country.
+  - `gdp`: GDP of the country.
+
+### **Task:**
+A country is considered **big** if:
+- It has an **area** of at least **3,000,000 km²**, **or**
+- It has a **population** of at least **25,000,000**.
+
+Write a solution to find the **name**, **population**, and **area** of the big countries.
+
+Return the result table in **any order**.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+#### **World Table**
+```
++-------------+-----------+---------+------------+--------------+
+| name        | continent | area    | population | gdp          |
++-------------+-----------+---------+------------+--------------+
+| Afghanistan | Asia      | 652230  | 25500100   | 20343000000  |
+| Albania     | Europe    | 28748   | 2831741    | 12960000000  |
+| Algeria     | Africa    | 2381741 | 37100000   | 188681000000 |
+| Andorra     | Europe    | 468     | 78115      | 3712000000   |
+| Angola      | Africa    | 1246700 | 20609294   | 100990000000 |
++-------------+-----------+---------+------------+--------------+
+```
+
+### **Output:**
+```
++-------------+------------+---------+
+| name        | population | area    |
++-------------+------------+---------+
+| Afghanistan | 25500100   | 652230  |
+| Algeria     | 37100000   | 2381741 |
++-------------+------------+---------+
+```
+
+### **Explanation:**
+- **Afghanistan** is not big by area (652,230 < 3,000,000) but is big by population (25,500,100 ≥ 25,000,000).
+- **Algeria** is big by population (37,100,000 ≥ 25,000,000), even though its area (2,381,741) is less than 3,000,000.
+- The other countries do not meet either condition.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using UNION)**
+```sql
+SELECT name, population, area 
+FROM World 
+WHERE area >= 3000000
+UNION
+SELECT name, population, area 
+FROM World 
+WHERE population >= 25000000;
+```
+**Explanation:**
+- The first `SELECT` returns countries with an area of at least 3,000,000 km².
+- The second `SELECT` returns countries with a population of at least 25,000,000.
+- `UNION` combines these two result sets, ensuring unique rows.
+
+---
+
+### **SQL Alternative (Using OR)**
+```sql
+SELECT name, population, area
+FROM World
+WHERE area >= 3000000 OR population >= 25000000;
+```
+**Explanation:**
+- This query uses a single `SELECT` statement with an `OR` condition to capture countries that meet either criterion.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def big_countries(world: pd.DataFrame) -> pd.DataFrame:
+    # Filter countries that are considered big by either area or population
+    result = world[(world['area'] >= 3000000) | (world['population'] >= 25000000)][['name', 'population', 'area']]
+    return result
+
+# Example usage:
+# world_df = pd.read_csv('world.csv')
+# print(big_countries(world_df))
+```
+**Explanation:**
+- The Pandas solution filters the DataFrame based on the condition that `area` is at least 3,000,000 or `population` is at least 25,000,000.
+- It then returns the columns `name`, `population`, and `area`.
+
+---
+
+## **File Structure**
+```
+LeetCode595/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_union_solution.sql     # SQL solution using UNION.
+├── sql_or_solution.sql        # SQL alternative solution using OR.
+├── pandas_solution.py         # Pandas solution for Python users.
+├── README.md                  # Overview of the problem and solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 595](https://leetcode.com/problems/big-countries/)
+- [SQL WHERE Clause](https://www.w3schools.com/sql/sql_where.asp)
+- [Pandas Documentation](https://pandas.pydata.org/docs/)
+
+---
+
+This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From b292abedf02fb6e269ea9246d99551d07580b1a9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:56:24 +0530
Subject: [PATCH 174/353] Update readme.md

---
 LeetCode SQL 50 Solution/595. Big Countries/readme.md | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/LeetCode SQL 50 Solution/595. Big Countries/readme.md b/LeetCode SQL 50 Solution/595. Big Countries/readme.md
index 5f62064..932d777 100644
--- a/LeetCode SQL 50 Solution/595. Big Countries/readme.md	
+++ b/LeetCode SQL 50 Solution/595. Big Countries/readme.md	
@@ -1,7 +1,3 @@
-Below is the structured README.md snippet for **LeetCode 595: Big Countries**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
-
----
-
 # **595. Big Countries**
 
 ## **Problem Statement**
@@ -137,6 +133,3 @@ LeetCode595/
 - [SQL WHERE Clause](https://www.w3schools.com/sql/sql_where.asp)
 - [Pandas Documentation](https://pandas.pydata.org/docs/)
 
----
-
-This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From 1a4b42a7b5b7b66a64dbc323079934d513e259e0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 20:56:48 +0530
Subject: [PATCH 175/353] Update 595. Big Countries.py

---
 .../595. Big Countries/595. Big Countries.py                | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py b/LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py
index e69de29..83c6cd5 100644
--- a/LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py	
+++ b/LeetCode SQL 50 Solution/595. Big Countries/595. Big Countries.py	
@@ -0,0 +1,6 @@
+import pandas as pd
+
+def big_countries(world: pd.DataFrame) -> pd.DataFrame:
+    # Filter countries that are considered big by either area or population
+    result = world[(world['area'] >= 3000000) | (world['population'] >= 25000000)][['name', 'population', 'area']]
+    return result
\ No newline at end of file

From 2472c0231e6712a10f4647c01b851ffe539d6356 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:01:23 +0530
Subject: [PATCH 176/353] Create 596. Classes More Than 5 Students.py

---
 .../596. Classes More Than 5 Students.py                          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.py

diff --git a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.py b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.py
new file mode 100644
index 0000000..e69de29

From b743e2610c3c1a920fd55faef57a3b1990db48a2 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:01:46 +0530
Subject: [PATCH 177/353] Create readme.md

---
 .../596. Classes More Than 5 Students/readme.md                   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md

diff --git a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md
new file mode 100644
index 0000000..e69de29

From ff5eba252637db5dcc4880e94139fc9e5f872ec3 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:01:54 +0530
Subject: [PATCH 178/353] Update readme.md

---
 .../readme.md                                 | 117 ++++++++++++++++++
 1 file changed, 117 insertions(+)

diff --git a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md
index e69de29..50055a2 100644
--- a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md	
+++ b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md	
@@ -0,0 +1,117 @@
+Below is the structured README.md snippet for **LeetCode 596: Classes More Than 5 Students** including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
+
+---
+
+# **596. Classes More Than 5 Students**
+
+## **Problem Statement**
+You are given a table `Courses` that contains the names of students and the class in which they are enrolled.
+
+### **Courses Table**
+```
++---------+---------+
+| Column  | Type    |
++---------+---------+
+| student | varchar |
+| class   | varchar |
++---------+---------+
+```
+- The combination of `(student, class)` is the **primary key**.
+- Each row indicates the name of a student and the class they are enrolled in.
+
+### **Task:**
+Write a solution to find all the classes that have **at least five students**.
+
+Return the result table in **any order**.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+```
+Courses table:
++---------+----------+
+| student | class    |
++---------+----------+
+| A       | Math     |
+| B       | English  |
+| C       | Math     |
+| D       | Biology  |
+| E       | Math     |
+| F       | Computer |
+| G       | Math     |
+| H       | Math     |
+| I       | Math     |
++---------+----------+
+```
+
+### **Output:**
+```
++---------+
+| class   |
++---------+
+| Math    |
++---------+
+```
+
+### **Explanation:**
+- **Math** has 6 students, so it is included.
+- **English**, **Biology**, and **Computer** have fewer than 5 students, so they are excluded.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution**
+```sql
+SELECT class
+FROM Courses
+GROUP BY class
+HAVING COUNT(student) >= 5;
+```
+**Explanation:**
+- The query groups records by `class`.
+- The `HAVING` clause filters out groups with fewer than 5 students.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def classes_with_five_or_more_students(courses: pd.DataFrame) -> pd.DataFrame:
+    # Group by 'class' and count the number of students
+    result = courses.groupby('class').filter(lambda x: len(x) >= 5)
+    # Return only the distinct class names
+    return result[['class']].drop_duplicates().reset_index(drop=True)
+
+# Example usage:
+# courses_df = pd.read_csv('courses.csv')
+# print(classes_with_five_or_more_students(courses_df))
+```
+**Explanation:**
+- The Pandas solution groups the DataFrame by `class` and filters groups with 5 or more students.
+- It then extracts and returns the distinct class names.
+
+---
+
+## **File Structure**
+```
+LeetCode596/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solution.
+├── pandas_solution.py         # Contains the Pandas solution for Python users.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 596](https://leetcode.com/problems/classes-more-than-5-students/)
+- [SQL GROUP BY and HAVING Clause](https://www.w3schools.com/sql/sql_groupby.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+
+---
+
+This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From b780b9ef9e354601365fde7cb52bb71ac93c2fcf Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:02:10 +0530
Subject: [PATCH 179/353] Update readme.md

---
 .../596. Classes More Than 5 Students/readme.md            | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md
index 50055a2..3f667e2 100644
--- a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md	
+++ b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/readme.md	
@@ -1,7 +1,3 @@
-Below is the structured README.md snippet for **LeetCode 596: Classes More Than 5 Students** including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
-
----
-
 # **596. Classes More Than 5 Students**
 
 ## **Problem Statement**
@@ -112,6 +108,3 @@ LeetCode596/
 - [SQL GROUP BY and HAVING Clause](https://www.w3schools.com/sql/sql_groupby.asp)
 - [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
 
----
-
-This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From c976da464871f0812c4914b886d3b276a8993caa Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:02:40 +0530
Subject: [PATCH 180/353] Update 596. Classes More Than 5 Students.py

---
 .../596. Classes More Than 5 Students.py                   | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.py b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.py
index e69de29..e93ee24 100644
--- a/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.py	
+++ b/LeetCode SQL 50 Solution/596. Classes More Than 5 Students/596. Classes More Than 5 Students.py	
@@ -0,0 +1,7 @@
+import pandas as pd
+
+def classes_with_five_or_more_students(courses: pd.DataFrame) -> pd.DataFrame:
+    # Group by 'class' and count the number of students
+    result = courses.groupby('class').filter(lambda x: len(x) >= 5)
+    # Return only the distinct class names
+    return result[['class']].drop_duplicates().reset_index(drop=True)
\ No newline at end of file

From 2ec35ddcf8ddb00877c13780aefa7d6c2da40733 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:04:01 +0530
Subject: [PATCH 181/353] Create 602. Friend Requests II.py

---
 .../602. Friend Requests II/602. Friend Requests II.py            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.py

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.py b/LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.py
new file mode 100644
index 0000000..e69de29

From 1f3f53161df6560613f614140f404c8b3cd82a74 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:04:12 +0530
Subject: [PATCH 182/353] Create readme.md

---
 LeetCode SQL 50 Solution/602. Friend Requests II/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/602. Friend Requests II/readme.md

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md b/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md
new file mode 100644
index 0000000..e69de29

From 70f09efa21a796eff79bcb11020a8fd2b495f4e7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:07:38 +0530
Subject: [PATCH 183/353] Update readme.md

---
 .../602. Friend Requests II/readme.md         | 129 ++++++++++++++++++
 1 file changed, 129 insertions(+)

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md b/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md
index e69de29..edec57c 100644
--- a/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md	
+++ b/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md	
@@ -0,0 +1,129 @@
+Below is a structured README.md snippet for **LeetCode 602: Friend Requests II: Who Has the Most Friends**. It includes the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
+
+---
+
+# **602. Friend Requests II: Who Has the Most Friends**
+
+## **Problem Statement**
+You are given a table `RequestAccepted` that records friend request acceptances between users.
+
+### **RequestAccepted Table**
+```
++--------------+-------------+-------------+
+| Column Name  | Type        |
++--------------+-------------+
+| requester_id | int         |
+| accepter_id  | int         |
+| accept_date  | date        |
++--------------+-------------+
+```
+- **(requester_id, accepter_id)** is the **primary key**.
+- Each row indicates the user who sent a friend request (`requester_id`), the user who accepted it (`accepter_id`), and the date when the request was accepted.
+
+### **Task:**
+Find the person who has the **most friends** along with the number of friends they have.  
+*Note:* The test cases are generated so that only one person has the most friends.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+#### **RequestAccepted Table**
+```
++--------------+-------------+-------------+
+| requester_id | accepter_id | accept_date |
++--------------+-------------+-------------+
+| 1            | 2           | 2016/06/03  |
+| 1            | 3           | 2016/06/08  |
+| 2            | 3           | 2016/06/08  |
+| 3            | 4           | 2016/06/09  |
++--------------+-------------+-------------+
+```
+
+### **Output:**
+```
++----+-----+
+| id | num |
++----+-----+
+| 3  | 3   |
++----+-----+
+```
+
+### **Explanation:**
+- User with `id = 3` is friends with users 1, 2, and 4, making a total of **3 friends**—the highest among all users.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using UNION ALL)**
+```sql
+WITH T AS (
+    SELECT requester_id, accepter_id FROM RequestAccepted
+    UNION ALL
+    SELECT accepter_id, requester_id FROM RequestAccepted
+)
+SELECT requester_id AS id, COUNT(*) AS num
+FROM T
+GROUP BY requester_id
+ORDER BY num DESC
+LIMIT 1;
+```
+**Explanation:**
+- The CTE `T` creates a complete friendship list by combining both directions of the friend relationship.
+- The outer query groups by `requester_id` (which now represents a user) and counts the number of occurrences (i.e., friends).
+- The result is ordered by the friend count in descending order and limited to one row, returning the user with the most friends.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def most_friends(requests: pd.DataFrame) -> pd.DataFrame:
+    # Create a DataFrame that contains all friend relationships in both directions
+    friend_df = pd.concat([
+        requests[['requester_id', 'accepter_id']].rename(columns={'requester_id': 'id', 'accepter_id': 'friend'}),
+        requests[['accepter_id', 'requester_id']].rename(columns={'accepter_id': 'id', 'requester_id': 'friend'})
+    ])
+    
+    # Count number of friends for each user
+    friend_counts = friend_df.groupby('id').size().reset_index(name='num')
+    
+    # Get the user with the most friends
+    max_friends = friend_counts.loc[friend_counts['num'].idxmax()]
+    
+    return pd.DataFrame({'id': [max_friends['id']], 'num': [max_friends['num']]})
+
+# Example usage:
+# requests_df = pd.read_csv('request_accepted.csv')
+# print(most_friends(requests_df))
+```
+**Explanation:**
+- The solution concatenates two DataFrames to consider friend relationships in both directions.
+- It then groups by user `id` and counts the number of friends.
+- The user with the maximum friend count is selected and returned.
+
+---
+
+## **File Structure**
+```
+LeetCode602/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solution using UNION ALL.
+├── pandas_solution.py         # Contains the Pandas solution for Python users.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 602](https://leetcode.com/problems/friend-requests-ii-who-has-the-most-friends/)
+- [SQL UNION ALL Documentation](https://www.w3schools.com/sql/sql_union.asp)
+- [Pandas concat Documentation](https://pandas.pydata.org/docs/reference/api/pandas.concat.html)
+- [Pandas groupby Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+
+---
+
+This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From e3f6c92cfb60760f9b6a3ba843ec9e17fb93c330 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:08:10 +0530
Subject: [PATCH 184/353] Update readme.md

---
 .../602. Friend Requests II/readme.md                     | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md b/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md
index edec57c..8be2c91 100644
--- a/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md	
+++ b/LeetCode SQL 50 Solution/602. Friend Requests II/readme.md	
@@ -1,7 +1,3 @@
-Below is a structured README.md snippet for **LeetCode 602: Friend Requests II: Who Has the Most Friends**. It includes the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
-
----
-
 # **602. Friend Requests II: Who Has the Most Friends**
 
 ## **Problem Statement**
@@ -123,7 +119,3 @@ LeetCode602/
 - [SQL UNION ALL Documentation](https://www.w3schools.com/sql/sql_union.asp)
 - [Pandas concat Documentation](https://pandas.pydata.org/docs/reference/api/pandas.concat.html)
 - [Pandas groupby Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-
----
-
-This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From 100af99edc17f51ae6299d2ea47961ffe736d775 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:08:48 +0530
Subject: [PATCH 185/353] Update 602. Friend Requests II.py

---
 .../602. Friend Requests II.py                   | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.py b/LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.py
index e69de29..1981f41 100644
--- a/LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.py	
+++ b/LeetCode SQL 50 Solution/602. Friend Requests II/602. Friend Requests II.py	
@@ -0,0 +1,16 @@
+import pandas as pd
+
+def most_friends(requests: pd.DataFrame) -> pd.DataFrame:
+    # Create a DataFrame that contains all friend relationships in both directions
+    friend_df = pd.concat([
+        requests[['requester_id', 'accepter_id']].rename(columns={'requester_id': 'id', 'accepter_id': 'friend'}),
+        requests[['accepter_id', 'requester_id']].rename(columns={'accepter_id': 'id', 'requester_id': 'friend'})
+    ])
+    
+    # Count number of friends for each user
+    friend_counts = friend_df.groupby('id').size().reset_index(name='num')
+    
+    # Get the user with the most friends
+    max_friends = friend_counts.loc[friend_counts['num'].idxmax()]
+    
+    return pd.DataFrame({'id': [max_friends['id']], 'num': [max_friends['num']]})

From bc63fecf53d925b5876da8b43f003a08bc245d28 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:11:03 +0530
Subject: [PATCH 186/353] Create 610. Triangle Judgement.py

---
 .../610. Triangle Judgement/610. Triangle Judgement.py            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.py

diff --git a/LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.py b/LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.py
new file mode 100644
index 0000000..e69de29

From e6cce8e586aeaebda4b631093fa07b480ebba491 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:11:22 +0530
Subject: [PATCH 187/353] Create readme.md

---
 LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md

diff --git a/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md b/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md
new file mode 100644
index 0000000..e69de29

From 52a53e5b9588e9e01e21136d4b211c2f9c803b58 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:13:13 +0530
Subject: [PATCH 188/353] Update readme.md

---
 .../610. Triangle Judgement/readme.md         | 116 ++++++++++++++++++
 1 file changed, 116 insertions(+)

diff --git a/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md b/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md
index e69de29..71e3a4f 100644
--- a/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md	
+++ b/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md	
@@ -0,0 +1,116 @@
+Below is the structured README.md snippet for **LeetCode 610: Triangle Judgement**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
+
+---
+
+# **610. Triangle Judgement**
+
+## **Problem Statement**
+You are given a table `Triangle` that contains three integer values representing the lengths of three line segments.
+
+### **Triangle Table**
+```
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| x           | int  |
+| y           | int  |
+| z           | int  |
++-------------+------+
+```
+- **(x, y, z)** is the **primary key**.
+- Each row represents the lengths of three line segments.
+
+### **Task:**
+Report for each row whether the three line segments can form a triangle. A triangle can be formed if and only if the sum of any two sides is greater than the third side.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+#### **Triangle Table**
+```
++----+----+----+
+| x  | y  | z  |
++----+----+----+
+| 13 | 15 | 30 |
+| 10 | 20 | 15 |
++----+----+----+
+```
+
+### **Output:**
+```
++----+----+----+----------+
+| x  | y  | z  | triangle |
++----+----+----+----------+
+| 13 | 15 | 30 | No       |
+| 10 | 20 | 15 | Yes      |
++----+----+----+----------+
+```
+
+### **Explanation:**
+- For the first row: `13 + 15` is not greater than `30`, so the segments cannot form a triangle.  
+- For the second row: All conditions are met (`10+20 > 15`, `10+15 > 20`, `20+15 > 10`), so they form a triangle.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution**
+```sql
+SELECT
+  x,
+  y,
+  z,
+  IF(x + y > z AND x + z > y AND y + z > x, 'Yes', 'No') AS triangle
+FROM Triangle;
+```
+**Explanation:**
+- The query checks if the sum of any two sides is greater than the third side.
+- If all conditions are true, it returns `'Yes'`; otherwise, it returns `'No'`.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def triangle_judgement(triangle: pd.DataFrame) -> pd.DataFrame:
+    # Create a new column 'triangle' based on the triangle inequality conditions
+    triangle['triangle'] = triangle.apply(
+        lambda row: 'Yes' if (row['x'] + row['y'] > row['z'] and 
+                              row['x'] + row['z'] > row['y'] and 
+                              row['y'] + row['z'] > row['x']) else 'No',
+        axis=1
+    )
+    return triangle
+
+# Example usage:
+# df = pd.DataFrame({'x': [13, 10], 'y': [15, 20], 'z': [30, 15]})
+# print(triangle_judgement(df))
+```
+**Explanation:**
+- The Pandas solution uses `apply()` with a lambda function to evaluate the triangle inequality for each row.
+- It then creates a new column `triangle` with the result `'Yes'` or `'No'`.
+
+---
+
+## **File Structure**
+```
+LeetCode610/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solution.
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 610](https://leetcode.com/problems/triangle-judgement/)
+- [SQL IF Function](https://www.w3schools.com/sql/func_mysql_if.asp)
+- [Pandas apply() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html)
+
+---
+
+This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From c59c8cfc90df607cdda1fde7ac880ba0a5ceb2ae Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:13:28 +0530
Subject: [PATCH 189/353] Update readme.md

---
 LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md b/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md
index 71e3a4f..ed31c6e 100644
--- a/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md	
+++ b/LeetCode SQL 50 Solution/610. Triangle Judgement/readme.md	
@@ -1,6 +1,3 @@
-Below is the structured README.md snippet for **LeetCode 610: Triangle Judgement**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
-
----
 
 # **610. Triangle Judgement**
 
@@ -110,7 +107,3 @@ LeetCode610/
 - [LeetCode Problem 610](https://leetcode.com/problems/triangle-judgement/)
 - [SQL IF Function](https://www.w3schools.com/sql/func_mysql_if.asp)
 - [Pandas apply() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html)
-
----
-
-This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From 4bd64252aedcc247c7e432e304ffa4fb7b396500 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:14:57 +0530
Subject: [PATCH 190/353] Update 610. Triangle Judgement.py

---
 .../610. Triangle Judgement.py                        | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.py b/LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.py
index e69de29..ea00949 100644
--- a/LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.py	
+++ b/LeetCode SQL 50 Solution/610. Triangle Judgement/610. Triangle Judgement.py	
@@ -0,0 +1,11 @@
+import pandas as pd
+
+def triangle_judgement(triangle: pd.DataFrame) -> pd.DataFrame:
+    # Create a new column 'triangle' based on the triangle inequality conditions
+    triangle['triangle'] = triangle.apply(
+        lambda row: 'Yes' if (row['x'] + row['y'] > row['z'] and 
+                              row['x'] + row['z'] > row['y'] and 
+                              row['y'] + row['z'] > row['x']) else 'No',
+        axis=1
+    )
+    return triangle
\ No newline at end of file

From 9bb554eeb0ac7ecdb1a1d5e1d4c935d49d199595 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:18:55 +0530
Subject: [PATCH 191/353] akwefh

---
 .../619. Biggest Single Number.py             |   0
 .../619. Biggest Single Number/readme.md      | 155 ++++++++++++++++++
 2 files changed, 155 insertions(+)
 create mode 100644 LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.py
 create mode 100644 LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md

diff --git a/LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.py b/LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.py
new file mode 100644
index 0000000..e69de29
diff --git a/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md b/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md
new file mode 100644
index 0000000..094853a
--- /dev/null
+++ b/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md	
@@ -0,0 +1,155 @@
+Below is the structured README.md snippet for **LeetCode 619: Biggest Single Number**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
+
+---
+
+# **619. Biggest Single Number**
+
+## **Problem Statement**
+You are given a table `MyNumbers` that contains integers, which may include duplicates.
+
+### **MyNumbers Table**
+```
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| num         | int  |
++-------------+------+
+```
+- There is **no primary key** for this table.
+- Each row contains an integer.
+
+### **Task:**
+A **single number** is a number that appears **only once** in the `MyNumbers` table.  
+Find the **largest single number**. If there is no single number, report `null`.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+```
+MyNumbers table:
++-----+
+| num |
++-----+
+| 8   |
+| 8   |
+| 3   |
+| 3   |
+| 1   |
+| 4   |
+| 5   |
+| 6   |
++-----+
+```
+
+### **Output:**
+```
++-----+
+| num |
++-----+
+| 6   |
++-----+
+```
+
+### **Explanation:**
+- The single numbers (appear exactly once) are: **1, 4, 5, 6**.
+- The largest among these is **6**.
+
+---
+
+## **Example 2:**
+
+### **Input:**
+```
+MyNumbers table:
++-----+
+| num |
++-----+
+| 8   |
+| 8   |
+| 7   |
+| 7   |
+| 3   |
+| 3   |
+| 3   |
++-----+
+```
+
+### **Output:**
+```
++------+
+| num  |
++------+
+| null |
++------+
+```
+
+### **Explanation:**
+- There are no single numbers (all numbers appear more than once), so the result is `null`.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution**
+```sql
+SELECT MAX(num) AS num
+FROM (
+    SELECT num
+    FROM MyNumbers
+    GROUP BY num
+    HAVING COUNT(num) = 1
+) AS unique_numbers;
+```
+**Explanation:**
+- The subquery groups by `num` and filters to include only those numbers that appear exactly once (`HAVING COUNT(num) = 1`).
+- The outer query returns the maximum value from these unique numbers.
+- If no unique number exists, `MAX(num)` returns `null`.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def biggest_single_number(my_numbers: pd.DataFrame) -> pd.DataFrame:
+    # Group by 'num' and filter those numbers that appear exactly once
+    unique_numbers = my_numbers.groupby('num').filter(lambda group: len(group) == 1)
+    
+    # Determine the largest single number, if any
+    if unique_numbers.empty:
+        result = None
+    else:
+        result = unique_numbers['num'].max()
+    
+    return pd.DataFrame({'num': [result]})
+
+# Example usage:
+# df = pd.DataFrame({'num': [8, 8, 3, 3, 1, 4, 5, 6]})
+# print(biggest_single_number(df))
+```
+**Explanation:**
+- The solution groups the DataFrame by `num` and filters groups where the number appears exactly once.
+- It then calculates the maximum from the filtered DataFrame.
+- If there are no unique numbers, it returns `None`.
+
+---
+
+## **File Structure**
+```
+LeetCode619/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solution.
+├── pandas_solution.py         # Contains the Pandas solution for Python users.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 619](https://leetcode.com/problems/biggest-single-number/)
+- [SQL GROUP BY and HAVING Clause](https://www.w3schools.com/sql/sql_groupby.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+- [Pandas filter() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.filter.html)
+

From 27eb621bcdb1909ef99ef521fb6d0cfaaffa42c2 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:19:09 +0530
Subject: [PATCH 192/353] Update readme.md

---
 LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md b/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md
index 094853a..2d85b6a 100644
--- a/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md	
+++ b/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md	
@@ -1,7 +1,3 @@
-Below is the structured README.md snippet for **LeetCode 619: Biggest Single Number**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
-
----
-
 # **619. Biggest Single Number**
 
 ## **Problem Statement**

From 0497a5dcd980eaf0375fe74bdad958339f147070 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:19:58 +0530
Subject: [PATCH 193/353] Update readme.md

---
 .../619. Biggest Single Number/readme.md                  | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md b/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md
index 2d85b6a..048f76d 100644
--- a/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md	
+++ b/LeetCode SQL 50 Solution/619. Biggest Single Number/readme.md	
@@ -4,7 +4,7 @@
 You are given a table `MyNumbers` that contains integers, which may include duplicates.
 
 ### **MyNumbers Table**
-```
+```rb
 +-------------+------+
 | Column Name | Type |
 +-------------+------+
@@ -23,7 +23,7 @@ Find the **largest single number**. If there is no single number, report `null`.
 ## **Example 1:**
 
 ### **Input:**
-```
+```rb
 MyNumbers table:
 +-----+
 | num |
@@ -40,7 +40,7 @@ MyNumbers table:
 ```
 
 ### **Output:**
-```
+```rb
 +-----+
 | num |
 +-----+
@@ -57,7 +57,7 @@ MyNumbers table:
 ## **Example 2:**
 
 ### **Input:**
-```
+```rb
 MyNumbers table:
 +-----+
 | num |

From 99a4601e61b6d67d18c655bfa8b333a3543248cb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:20:07 +0530
Subject: [PATCH 194/353] Update 619. Biggest Single Number.py

---
 .../619. Biggest Single Number.py                   | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.py b/LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.py
index e69de29..1c92778 100644
--- a/LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.py	
+++ b/LeetCode SQL 50 Solution/619. Biggest Single Number/619. Biggest Single Number.py	
@@ -0,0 +1,13 @@
+import pandas as pd
+
+def biggest_single_number(my_numbers: pd.DataFrame) -> pd.DataFrame:
+    # Group by 'num' and filter those numbers that appear exactly once
+    unique_numbers = my_numbers.groupby('num').filter(lambda group: len(group) == 1)
+    
+    # Determine the largest single number, if any
+    if unique_numbers.empty:
+        result = None
+    else:
+        result = unique_numbers['num'].max()
+    
+    return pd.DataFrame({'num': [result]})

From 5ceedacffe804d4e4ffd53c0c6c9cb1aef6faecd Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:26:10 +0530
Subject: [PATCH 195/353] file

---
 .../620. Not Boring Movies/620. Not Boring Movies.py              | 0
 LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md         | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.py
 create mode 100644 LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md

diff --git a/LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.py b/LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.py
new file mode 100644
index 0000000..e69de29
diff --git a/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md b/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md
new file mode 100644
index 0000000..e69de29

From 985d8af373dadf5fe1d87bc56616d22e8d3290c1 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:26:18 +0530
Subject: [PATCH 196/353] Update readme.md

---
 .../620. Not Boring Movies/readme.md          | 124 ++++++++++++++++++
 1 file changed, 124 insertions(+)

diff --git a/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md b/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md
index e69de29..505c13a 100644
--- a/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md	
+++ b/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md	
@@ -0,0 +1,124 @@
+Below is a structured README.md snippet for **LeetCode 620: Not Boring Movies**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
+
+---
+
+# **620. Not Boring Movies**
+
+## **Problem Statement**
+You are given a table `Cinema` that contains information about movies, their descriptions, and ratings.
+
+### **Cinema Table**
+```
++----------------+----------+
+| Column Name    | Type     |
++----------------+----------+
+| id             | int      |
+| movie          | varchar  |
+| description    | varchar  |
+| rating         | float    |
++----------------+----------+
+```
+- `id` is the **primary key**.
+- Each row provides details about a movie:
+  - `id`: The movie's unique identifier.
+  - `movie`: The name of the movie.
+  - `description`: The description or genre of the movie.
+  - `rating`: A float representing the movie's rating (in the range [0, 10] with 2 decimal places).
+
+### **Task:**
+Write a solution to report the movies that have:
+- An **odd-numbered `id`**.
+- A `description` that is **not "boring"**.
+
+Return the result table **ordered by rating in descending order**.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+#### **Cinema Table**
+```
++----+------------+-------------+--------+
+| id | movie      | description | rating |
++----+------------+-------------+--------+
+| 1  | War        | great 3D    | 8.9    |
+| 2  | Science    | fiction     | 8.5    |
+| 3  | irish      | boring      | 6.2    |
+| 4  | Ice song   | Fantacy     | 8.6    |
+| 5  | House card | Interesting | 9.1    |
++----+------------+-------------+--------+
+```
+
+### **Output:**
+```
++----+------------+-------------+--------+
+| id | movie      | description | rating |
++----+------------+-------------+--------+
+| 5  | House card | Interesting | 9.1    |
+| 1  | War        | great 3D    | 8.9    |
++----+------------+-------------+--------+
+```
+
+### **Explanation:**
+- Movies with **odd-numbered IDs**: `1`, `3`, and `5`.
+- Excluding movie with `id = 3` because its description is `"boring"`.
+- Sorting the remaining movies by `rating` in descending order gives the result.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution**
+```sql
+SELECT * 
+FROM Cinema 
+WHERE id % 2 = 1 
+  AND description != 'boring'
+ORDER BY rating DESC;
+```
+**Explanation:**
+- The query filters movies where the `id` is odd (`id % 2 = 1`) and the `description` is not `"boring"`.
+- The results are ordered by `rating` in descending order.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def not_boring_movies(cinema: pd.DataFrame) -> pd.DataFrame:
+    # Filter movies with odd-numbered id and description not equal to 'boring'
+    result = cinema[(cinema['id'] % 2 == 1) & (cinema['description'] != 'boring')]
+    # Sort the result by rating in descending order
+    return result.sort_values(by='rating', ascending=False)
+
+# Example usage:
+# cinema_df = pd.read_csv('cinema.csv')
+# print(not_boring_movies(cinema_df))
+```
+**Explanation:**
+- The Pandas solution filters the DataFrame to include only rows where the `id` is odd and the `description` is not `"boring"`.
+- It then sorts the filtered results by `rating` in descending order.
+
+---
+
+## **File Structure**
+```
+LeetCode620/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solution.
+├── pandas_solution.py         # Contains the Pandas solution for Python users.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 620](https://leetcode.com/problems/not-boring-movies/)
+- [SQL WHERE Clause Documentation](https://www.w3schools.com/sql/sql_where.asp)
+- [Pandas Documentation](https://pandas.pydata.org/docs/)
+
+---
+
+This structured format provides a clear understanding of the problem and offers both SQL and Pandas solutions. Happy coding! 🚀
\ No newline at end of file

From 4b10026db487c1cf7684511b27b36fce98319fcd Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:26:31 +0530
Subject: [PATCH 197/353] Update readme.md

---
 LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md b/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md
index 505c13a..5b519fd 100644
--- a/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md	
+++ b/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md	
@@ -1,6 +1,4 @@
-Below is a structured README.md snippet for **LeetCode 620: Not Boring Movies**, including the problem statement, example, solution approaches (SQL and Pandas), file structure, and useful links.
 
----
 
 # **620. Not Boring Movies**
 
@@ -119,6 +117,3 @@ LeetCode620/
 - [SQL WHERE Clause Documentation](https://www.w3schools.com/sql/sql_where.asp)
 - [Pandas Documentation](https://pandas.pydata.org/docs/)
 
----
-
-This structured format provides a clear understanding of the problem and offers both SQL and Pandas solutions. Happy coding! 🚀
\ No newline at end of file

From ce0db6f30e7c742f587b90a22a557255c81e0d4a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:26:55 +0530
Subject: [PATCH 198/353] add

---
 .../620. Not Boring Movies/620. Not Boring Movies.py       | 7 +++++++
 LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md  | 2 --
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.py b/LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.py
index e69de29..b1df1fa 100644
--- a/LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.py	
+++ b/LeetCode SQL 50 Solution/620. Not Boring Movies/620. Not Boring Movies.py	
@@ -0,0 +1,7 @@
+import pandas as pd
+
+def not_boring_movies(cinema: pd.DataFrame) -> pd.DataFrame:
+    # Filter movies with odd-numbered id and description not equal to 'boring'
+    result = cinema[(cinema['id'] % 2 == 1) & (cinema['description'] != 'boring')]
+    # Sort the result by rating in descending order
+    return result.sort_values(by='rating', ascending=False)
diff --git a/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md b/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md
index 5b519fd..9a6d9ef 100644
--- a/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md	
+++ b/LeetCode SQL 50 Solution/620. Not Boring Movies/readme.md	
@@ -1,5 +1,3 @@
-
-
 # **620. Not Boring Movies**
 
 ## **Problem Statement**

From 1c5f51a4203daca8ea7ea6d92934b21672632767 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:27:53 +0530
Subject: [PATCH 199/353] Create 626. Exchange Seats.py

---
 .../626. Exchange Seats/626. Exchange Seats.py                    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.py

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.py b/LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.py
new file mode 100644
index 0000000..e69de29

From 6c7ca7200a39fc5c020f7a5c1eaf5b379588dbb8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:28:10 +0530
Subject: [PATCH 200/353] Create readme.md

---
 LeetCode SQL 50 Solution/626. Exchange Seats/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/626. Exchange Seats/readme.md

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md b/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md
new file mode 100644
index 0000000..e69de29

From 19adbad3ead165121ee85365c0f64e5dcc72b1d8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:30:19 +0530
Subject: [PATCH 201/353] Update readme.md

---
 .../626. Exchange Seats/readme.md             | 154 ++++++++++++++++++
 1 file changed, 154 insertions(+)

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md b/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md
index e69de29..dfe8455 100644
--- a/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md	
+++ b/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md	
@@ -0,0 +1,154 @@
+Below is the structured README.md snippet for **LeetCode 626: Exchange Seats**, including the problem statement, example, SQL and Pandas solutions, file structure, and useful links.
+
+---
+
+# **626. Exchange Seats**
+
+## **Problem Statement**
+You are given a table `Seat` that contains the seat IDs and names of students. The seat IDs are assigned consecutively starting from 1.
+
+### **Seat Table**
+```
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| student     | varchar |
++-------------+---------+
+```
+- `id` is the **primary key** (unique value).
+- Each row represents a student and their assigned seat.
+- The `id` sequence always starts from 1 and increments continuously.
+
+### **Task:**
+Swap the seat `id` of every two consecutive students.  
+- If the number of students is odd, the `id` of the last student remains unchanged.
+- Return the result table ordered by `id` in ascending order.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+```
+Seat table:
++----+---------+
+| id | student |
++----+---------+
+| 1  | Abbot   |
+| 2  | Doris   |
+| 3  | Emerson |
+| 4  | Green   |
+| 5  | Jeames  |
++----+---------+
+```
+
+### **Output:**
+```
++----+---------+
+| id | student |
++----+---------+
+| 1  | Doris   |
+| 2  | Abbot   |
+| 3  | Green   |
+| 4  | Emerson |
+| 5  | Jeames  |
++----+---------+
+```
+
+### **Explanation:**
+- Swap the seat assignments of every two consecutive students:
+  - Seats 1 and 2: **Abbot** and **Doris** swap positions.
+  - Seats 3 and 4: **Emerson** and **Green** swap positions.
+  - Since the number of students is odd, **Jeames** (seat 5) remains in the same seat.
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution**
+```sql
+SELECT 
+    CASE 
+        WHEN id % 2 != 0 AND id != counts THEN id + 1
+        WHEN id % 2 != 0 AND id = counts THEN id
+        ELSE id - 1
+    END AS id, 
+    student
+FROM Seat, (SELECT COUNT(*) AS counts FROM Seat) AS seat_counts
+ORDER BY id ASC;
+```
+**Explanation:**
+- The subquery `(SELECT COUNT(*) AS counts FROM Seat)` computes the total number of students.
+- The `CASE` statement swaps IDs:
+  - For odd `id` (except the last one if the count is odd), we add 1.
+  - For even `id`, we subtract 1.
+  - For the last student in an odd-length list, we leave the `id` unchanged.
+- The results are then ordered by the new `id` in ascending order.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def exchange_seats(seat: pd.DataFrame) -> pd.DataFrame:
+    # Total number of students
+    total = seat.shape[0]
+    
+    # Function to compute the new seat id
+    def new_id(row):
+        # For odd id values:
+        if row['id'] % 2 != 0:
+            # If it's the last row in an odd-length list, do not change the id.
+            if row['id'] == total:
+                return row['id']
+            else:
+                return row['id'] + 1
+        # For even id values, swap with previous odd id
+        else:
+            return row['id'] - 1
+    
+    # Apply the new_id function to each row
+    seat['new_id'] = seat.apply(new_id, axis=1)
+    
+    # Sort by the new seat id and select the desired columns
+    result = seat.sort_values('new_id')[['new_id', 'student']].rename(columns={'new_id': 'id'})
+    
+    return result.reset_index(drop=True)
+
+# Example usage:
+# data = {'id': [1, 2, 3, 4, 5], 'student': ['Abbot', 'Doris', 'Emerson', 'Green', 'Jeames']}
+# df = pd.DataFrame(data)
+# print(exchange_seats(df))
+```
+**Explanation:**
+- The solution calculates the total number of rows.
+- A helper function `new_id` computes the new seat id:
+  - For odd `id`s (except the last one), add 1.
+  - For even `id`s, subtract 1.
+  - Leave the last seat unchanged if the count is odd.
+- The DataFrame is sorted by the new `id`, and the result is returned.
+
+---
+
+## **File Structure**
+```
+LeetCode626/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solution.
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 626](https://leetcode.com/problems/exchange-seats/)
+- [SQL CASE Statement Documentation](https://www.w3schools.com/sql/sql_case.asp)
+- [Pandas apply() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html)
+- [Pandas DataFrame Sorting](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html)
+
+---
+
+This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From 12d6e1fa6c58de2e277b8b6f2601ffded196b769 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:30:47 +0530
Subject: [PATCH 202/353] Update readme.md

---
 LeetCode SQL 50 Solution/626. Exchange Seats/readme.md | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md b/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md
index dfe8455..7bc6b79 100644
--- a/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md	
+++ b/LeetCode SQL 50 Solution/626. Exchange Seats/readme.md	
@@ -1,7 +1,3 @@
-Below is the structured README.md snippet for **LeetCode 626: Exchange Seats**, including the problem statement, example, SQL and Pandas solutions, file structure, and useful links.
-
----
-
 # **626. Exchange Seats**
 
 ## **Problem Statement**
@@ -149,6 +145,3 @@ LeetCode626/
 - [Pandas apply() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html)
 - [Pandas DataFrame Sorting](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html)
 
----
-
-This structured format provides a comprehensive overview of the problem along with multiple solution approaches. Happy coding! 🚀
\ No newline at end of file

From c098a4ee3ef92379e7ece8197c794a2c07ba5385 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sat, 29 Mar 2025 21:31:06 +0530
Subject: [PATCH 203/353] Update 626. Exchange Seats.py

---
 .../626. Exchange Seats/626. Exchange Seats.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.py b/LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.py
index e69de29..512b00b 100644
--- a/LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.py	
+++ b/LeetCode SQL 50 Solution/626. Exchange Seats/626. Exchange Seats.py	
@@ -0,0 +1,18 @@
+import pandas as pd
+
+def exchange_seats(seat: pd.DataFrame) -> pd.DataFrame:
+    # Total number of students
+    total = seat.shape[0]
+    
+    # Function to compute the new seat id
+    def new_id(row):
+        # For odd id values:
+        if row['id'] % 2 != 0:
+            # If it's the last row in an odd-length list, do not change the id.
+            if row['id'] == total:
+                return row['id']
+            else:
+                return row['id'] + 1
+        # For even id values, swap with previous odd id
+        else:
+            return row['id'] - 1
\ No newline at end of file

From c111f6b627aa44a46dfa93adc5f2251e03b0c9e9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
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From 1c294a7ca3d901c034b80dc7f7e7bd177428544b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
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From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
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Date: Sun, 30 Mar 2025 02:44:10 +0530
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From 81a5841bd0c25413f010110f778adcf8419464e0 Mon Sep 17 00:00:00 2001
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From d8a72a467e96de2238f103d7954b02c98f181141 Mon Sep 17 00:00:00 2001
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From 469f1005a9f423949d6c858962dd9154891b182c Mon Sep 17 00:00:00 2001
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From c78261fead012c840e7434a518e1c5d04e8543b9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:53:13 +0530
Subject: [PATCH 231/353] Create 1341. Movie Rating.py

---
 LeetCode SQL 50 Solution/1341. Movie Rating/1341. Movie Rating.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1341. Movie Rating/1341. Movie Rating.py

diff --git a/LeetCode SQL 50 Solution/1341. Movie Rating/1341. Movie Rating.py b/LeetCode SQL 50 Solution/1341. Movie Rating/1341. Movie Rating.py
new file mode 100644
index 0000000..e69de29

From f70c0f96631dcee51b8cd7b1c2c11c7d702062da Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:53:23 +0530
Subject: [PATCH 232/353] Create readme.md

---
 LeetCode SQL 50 Solution/1341. Movie Rating/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1341. Movie Rating/readme.md

diff --git a/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md b/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md
new file mode 100644
index 0000000..e69de29

From 67ab330e56c38f51686c6e82565ae023a3b2a76a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:53:41 +0530
Subject: [PATCH 233/353] Create 1378. Replace Employee ID With The Unique
 Identifier.md

---
 .../1378. Replace Employee ID With The Unique Identifier.md       | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/1378. Replace Employee ID With The Unique Identifier.md

diff --git a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/1378. Replace Employee ID With The Unique Identifier.md b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/1378. Replace Employee ID With The Unique Identifier.md
new file mode 100644
index 0000000..e69de29

From 76147b4aa44229950f90791cd55b2bad4918494e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:54:10 +0530
Subject: [PATCH 234/353] Create readme.md

---
 .../readme.md                                                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md

diff --git a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md
new file mode 100644
index 0000000..e69de29

From 57c9872ffa11a73e4d30c670ad63c9c8f9e8f4cf Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:55:47 +0530
Subject: [PATCH 235/353] Create 1484. Group Sold Products By The Date.py

---
 .../1484. Group Sold Products By The Date.py                      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/1484. Group Sold Products By The Date.py

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/1484. Group Sold Products By The Date.py b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/1484. Group Sold Products By The Date.py
new file mode 100644
index 0000000..e69de29

From 52e78d2cd8a59a3983d101240a65e1c645d66b10 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:55:57 +0530
Subject: [PATCH 236/353] Create readme.md

---
 .../1484. Group Sold Products By The Date/readme.md               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md
new file mode 100644
index 0000000..e69de29

From a3e3c8648d008ddaeb7c66176beb3c73d8ab6626 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:56:13 +0530
Subject: [PATCH 237/353] Create 1517. Find Users With Valid E-Mails.py

---
 .../1517. Find Users With Valid E-Mails.py                        | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/1517. Find Users With Valid E-Mails.py

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/1517. Find Users With Valid E-Mails.py b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/1517. Find Users With Valid E-Mails.py
new file mode 100644
index 0000000..e69de29

From e7757010dd53c893c1e4ac4d83d9bcda69e89def Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:56:20 +0530
Subject: [PATCH 238/353] Create readme.md

---
 .../1517. Find Users With Valid E-Mails/readme.md                 | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md
new file mode 100644
index 0000000..e69de29

From df64a95f585615e446f33c3582ec93a764c60748 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:56:46 +0530
Subject: [PATCH 239/353] Create 1527. Patients With a Condition.py

---
 .../1527. Patients With a Condition.py                            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1527. Patients With a Condition/1527. Patients With a Condition.py

diff --git a/LeetCode SQL 50 Solution/1527. Patients With a Condition/1527. Patients With a Condition.py b/LeetCode SQL 50 Solution/1527. Patients With a Condition/1527. Patients With a Condition.py
new file mode 100644
index 0000000..e69de29

From cf000951167df11a38b43f1d4c951cf28b3011c8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:57:02 +0530
Subject: [PATCH 240/353] Create readme.md

---
 .../1527. Patients With a Condition/readme.md                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md

diff --git a/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md b/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md
new file mode 100644
index 0000000..e69de29

From ead3046915a684c5b5753eb750ea10eb287fe35a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:58:21 +0530
Subject: [PATCH 241/353] Create 1581. Customer Who Visited but Did Not Make
 Any Transactions.py

---
 ...581. Customer Who Visited but Did Not Make Any Transactions.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.py

diff --git a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.py b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/1581. Customer Who Visited but Did Not Make Any Transactions.py
new file mode 100644
index 0000000..e69de29

From b33a861eb62ae7fa3cb8c801fea7f7f8cb84380a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:58:29 +0530
Subject: [PATCH 242/353] Create readme.md

---
 .../readme.md                                                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md

diff --git a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md
new file mode 100644
index 0000000..e69de29

From da5ead6d12943ef435457446683a62973d6e760b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:58:52 +0530
Subject: [PATCH 243/353] Create 1633. Percentage of Users Attended a
 Contest.py

---
 .../1633. Percentage of Users Attended a Contest.py               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/1633. Percentage of Users Attended a Contest.py

diff --git a/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/1633. Percentage of Users Attended a Contest.py b/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/1633. Percentage of Users Attended a Contest.py
new file mode 100644
index 0000000..e69de29

From fafc7c98e9b33d1ae4c98c6275f2245e9d962eed Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:59:00 +0530
Subject: [PATCH 244/353] Create readme.md

---
 .../1633. Percentage of Users Attended a Contest/readme.md        | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/readme.md

diff --git a/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/readme.md b/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/readme.md
new file mode 100644
index 0000000..e69de29

From 90115e82de9a164106566518feb0cd3da5c7eebb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:59:39 +0530
Subject: [PATCH 245/353] Create 1667. Fix Names in a Table,py

---
 .../1667. Fix Names in a Table/1667. Fix Names in a Table,py      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table,py

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table,py b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table,py
new file mode 100644
index 0000000..e69de29

From 36be8890bb051e307ab33df9b81b4c0b6823e346 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 02:59:58 +0530
Subject: [PATCH 246/353] ds

---
 ...667. Fix Names in a Table,py => 1667. Fix Names in a Table.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LeetCode SQL 50 Solution/1667. Fix Names in a Table/{1667. Fix Names in a Table,py => 1667. Fix Names in a Table.py} (100%)

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table,py b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table.py
similarity index 100%
rename from LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table,py
rename to LeetCode SQL 50 Solution/1667. Fix Names in a Table/1667. Fix Names in a Table.py

From bdb5ba29395381a0323264e094f8fc5a22697ce7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:00:07 +0530
Subject: [PATCH 247/353] Create readme.md

---
 LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md
new file mode 100644
index 0000000..e69de29

From dd53f3fcb6d040f3d10ba48f1ff0678f36c1278f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:00:25 +0530
Subject: [PATCH 248/353] Create 1683. Invalid Tweets.py

---
 .../1683. Invalid Tweets/1683. Invalid Tweets.py                  | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1683. Invalid Tweets/1683. Invalid Tweets.py

diff --git a/LeetCode SQL 50 Solution/1683. Invalid Tweets/1683. Invalid Tweets.py b/LeetCode SQL 50 Solution/1683. Invalid Tweets/1683. Invalid Tweets.py
new file mode 100644
index 0000000..e69de29

From 986b3fbc9c7c18c2672cc9116f72c3dc3bcd3200 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:00:36 +0530
Subject: [PATCH 249/353] Create readme.md

---
 LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md

diff --git a/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md b/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md
new file mode 100644
index 0000000..e69de29

From 3864ee610b83620f1f4954f3ea0ae6d2d1fac500 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:00:50 +0530
Subject: [PATCH 250/353] Create 1729. Find Followers Count.py

---
 .../1729. Find Followers Count/1729. Find Followers Count.py      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1729. Find Followers Count/1729. Find Followers Count.py

diff --git a/LeetCode SQL 50 Solution/1729. Find Followers Count/1729. Find Followers Count.py b/LeetCode SQL 50 Solution/1729. Find Followers Count/1729. Find Followers Count.py
new file mode 100644
index 0000000..e69de29

From e1335cae99fcdd68057fb9e9e2b670069af63c4f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:01:03 +0530
Subject: [PATCH 251/353] Create readme.md

---
 LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md

diff --git a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md
new file mode 100644
index 0000000..e69de29

From 5c1966f9a2e27bc689c81d931040b442ad62492e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:01:25 +0530
Subject: [PATCH 252/353] Create 1731. The Number of Employees Which Report to
 Each Employee.py

---
 ...1731. The Number of Employees Which Report to Each Employee.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/1731. The Number of Employees Which Report to Each Employee.py

diff --git a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/1731. The Number of Employees Which Report to Each Employee.py b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/1731. The Number of Employees Which Report to Each Employee.py
new file mode 100644
index 0000000..e69de29

From 5fffef826cafa5179ba108d7fa5ce9de3868fa2e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:01:32 +0530
Subject: [PATCH 253/353] Create readme.md

---
 .../readme.md                                                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md

diff --git a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md
new file mode 100644
index 0000000..e69de29

From d7e72a5f27eaba3be2a511007b7aa7195d5f791c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:01:54 +0530
Subject: [PATCH 254/353] Create 1757. Recyclable and Low Fat Products.py

---
 .../1757. Recyclable and Low Fat Products.py                      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/1757. Recyclable and Low Fat Products.py

diff --git a/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/1757. Recyclable and Low Fat Products.py b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/1757. Recyclable and Low Fat Products.py
new file mode 100644
index 0000000..e69de29

From fd2a5aabb9c2b21e202a448ccd65f83eb5d93b6f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:02:09 +0530
Subject: [PATCH 255/353] Create readme.md

---
 .../1757. Recyclable and Low Fat Products/readme.md               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md

diff --git a/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md
new file mode 100644
index 0000000..e69de29

From d39ee34a5958a6af68b8c73aeaf70c9d04197afe Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:04:45 +0530
Subject: [PATCH 256/353] Create 1789. Primary Department for Each Employee.py

---
 .../1789. Primary Department for Each Employee.py                 | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/1789. Primary Department for Each Employee.py

diff --git a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/1789. Primary Department for Each Employee.py b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/1789. Primary Department for Each Employee.py
new file mode 100644
index 0000000..e69de29

From 9a7f2303a84aac6ad8d6b6ffe0e4017a3b851f0e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:04:53 +0530
Subject: [PATCH 257/353] Create readme.md

---
 .../1789. Primary Department for Each Employee/readme.md          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md

diff --git a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md
new file mode 100644
index 0000000..e69de29

From fa7861d8deb143897ec43fde20180ba7bc1dc29d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:05:12 +0530
Subject: [PATCH 258/353] Create 1907. Count Salary Categories.py

---
 .../1907. Count Salary Categories.py                              | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1907. Count Salary Categories/1907. Count Salary Categories.py

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories/1907. Count Salary Categories.py b/LeetCode SQL 50 Solution/1907. Count Salary Categories/1907. Count Salary Categories.py
new file mode 100644
index 0000000..e69de29

From f0531699f542e61bec3e7d8e3b67086332d7c982 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:05:19 +0530
Subject: [PATCH 259/353] Create readme.md

---
 LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md b/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md
new file mode 100644
index 0000000..e69de29

From 704f62f2085468c2eb6dc09e740dea45a2cd2350 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:05:47 +0530
Subject: [PATCH 260/353] Create 1934. Confirmation Rate.py

---
 .../1934. Confirmation Rate/1934. Confirmation Rate.py            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1934. Confirmation Rate/1934. Confirmation Rate.py

diff --git a/LeetCode SQL 50 Solution/1934. Confirmation Rate/1934. Confirmation Rate.py b/LeetCode SQL 50 Solution/1934. Confirmation Rate/1934. Confirmation Rate.py
new file mode 100644
index 0000000..e69de29

From 6067143db3e83e043ec0501ba2a70ffa82f9acf0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:05:55 +0530
Subject: [PATCH 261/353] Create readme.md

---
 LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md

diff --git a/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md b/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md
new file mode 100644
index 0000000..e69de29

From 632393bcb70a04f733fac6e5fbcabc352c8820c0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:06:10 +0530
Subject: [PATCH 262/353] Create 1978. Employees Whose Manager Left the
 Company.py

---
 .../1978. Employees Whose Manager Left the Company.py             | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/1978. Employees Whose Manager Left the Company.py

diff --git a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/1978. Employees Whose Manager Left the Company.py b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/1978. Employees Whose Manager Left the Company.py
new file mode 100644
index 0000000..e69de29

From fe33d160b96201b9e009fa86947df81c3639e764 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:06:21 +0530
Subject: [PATCH 263/353] Create readme.md

---
 .../1978. Employees Whose Manager Left the Company/readme.md      | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md

diff --git a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md
new file mode 100644
index 0000000..e69de29

From 65240ce0c2cf0de0b63d7b531f68b7e07763942e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:06:38 +0530
Subject: [PATCH 264/353] Create readme.md

---
 .../readme.md                                                     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
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From 4d9788b1c20ad6fcedbe6c57c9f3fd86aa7c4813 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:06:57 +0530
Subject: [PATCH 265/353] Create prosnal_database.py

---
 LeetCode SQL 50 Solution/prosnal_database/prosnal_database.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LeetCode SQL 50 Solution/prosnal_database/prosnal_database.py

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new file mode 100644
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From 724e843d82acdc4271829a13c400de86e7c7b616 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:07:05 +0530
Subject: [PATCH 266/353] Create readme.md

---
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 1 file changed, 0 insertions(+), 0 deletions(-)
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diff --git a/LeetCode SQL 50 Solution/prosnal_database/readme.md b/LeetCode SQL 50 Solution/prosnal_database/readme.md
new file mode 100644
index 0000000..e69de29

From bcee40a6ad2e2fe156a32526529f969b3f46fb1f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:11:50 +0530
Subject: [PATCH 267/353] Update readme.md

---
 .../readme.md                                 | 181 ++++++++++++++++++
 1 file changed, 181 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md
index e69de29..2d3bb82 100644
--- a/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md	
+++ b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md	
@@ -0,0 +1,181 @@
+Here’s the updated README.md, including **Pandas and Window SQL solutions** along with the original **MySQL solution**.
+
+---
+
+# **1045. Customers Who Bought All Products**
+
+## **Problem Statement**
+You are given two tables:  
+- `Customer` (contains `customer_id` and `product_key`)  
+- `Product` (contains all available `product_key`s)
+
+Each `product_key` in `Customer` is a **foreign key** referring to the `Product` table.
+
+### **Customer Table**
+```
++-------------+-------------+
+| Column Name | Type        |
++-------------+-------------+
+| customer_id | int         |
+| product_key | int         |
++-------------+-------------+
+```
+- The table may contain **duplicate rows**.
+- `customer_id` is **not NULL**.
+- `product_key` refers to the `Product` table.
+
+### **Product Table**
+```
++-------------+
+| product_key |
++-------------+
+| int         |
++-------------+
+```
+- `product_key` is the **primary key** (unique values) of this table.
+
+### **Task:**
+Find **all customer IDs** who bought **every product** listed in the `Product` table.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+**Customer Table**
+```
++-------------+-------------+
+| customer_id | product_key |
++-------------+-------------+
+| 1           | 5           |
+| 2           | 6           |
+| 3           | 5           |
+| 3           | 6           |
+| 1           | 6           |
++-------------+-------------+
+```
+
+**Product Table**
+```
++-------------+
+| product_key |
++-------------+
+| 5           |
+| 6           |
++-------------+
+```
+
+### **Output:**
+```
++-------------+
+| customer_id |
++-------------+
+| 1           |
+| 3           |
++-------------+
+```
+
+### **Explanation:**
+- There are **two products** (5 and 6).
+- Customers who bought **both** products:
+  - **Customer 1**: Bought `5, 6` ✅
+  - **Customer 2**: Bought `6` ❌ (missing `5`)
+  - **Customer 3**: Bought `5, 6` ✅
+- So, **customers 1 and 3** are returned.
+
+---
+
+## **SQL Solutions**
+
+### **1️⃣ Standard MySQL Solution**
+```sql
+SELECT customer_id 
+FROM Customer 
+GROUP BY customer_id
+HAVING COUNT(DISTINCT product_key) = (SELECT COUNT(product_key) FROM Product);
+```
+#### **Explanation:**
+1. **GROUP BY `customer_id`** → Group purchases per customer.
+2. **COUNT(DISTINCT product_key)** → Count unique products each customer bought.
+3. **Compare with total products:**  
+   - `(SELECT COUNT(product_key) FROM Product)` counts all available products.
+   - Only customers with `COUNT(DISTINCT product_key) = total products` are included.
+4. **HAVING** ensures we return only those who bought **all products**.
+
+---
+
+### **2️⃣ Window Function (SQL) Solution**
+```sql
+WITH product_count AS (
+    SELECT COUNT(*) AS total_products FROM Product
+),
+customer_purchase AS (
+    SELECT customer_id, COUNT(DISTINCT product_key) AS purchased_count
+    FROM Customer
+    GROUP BY customer_id
+)
+SELECT customer_id 
+FROM customer_purchase, product_count
+WHERE customer_purchase.purchased_count = product_count.total_products;
+```
+#### **Explanation:**
+1. **CTE `product_count`** → Stores total number of products in `Product` table.
+2. **CTE `customer_purchase`** → Groups purchases per customer and counts distinct products.
+3. **Final SELECT query** → Compares each customer's purchase count with `total_products` and returns only those who match.
+
+---
+
+## **Pandas Solution (Python)**
+```python
+import pandas as pd
+
+# Sample data
+customer_data = {'customer_id': [1, 2, 3, 3, 1],
+                 'product_key': [5, 6, 5, 6, 6]}
+product_data = {'product_key': [5, 6]}
+
+# Create DataFrames
+customer_df = pd.DataFrame(customer_data)
+product_df = pd.DataFrame(product_data)
+
+# Get the total number of products
+total_products = product_df['product_key'].nunique()
+
+# Count distinct products per customer
+customer_purchase = customer_df.groupby('customer_id')['product_key'].nunique()
+
+# Filter customers who bought all products
+result = customer_purchase[customer_purchase == total_products].reset_index()
+
+print(result)
+```
+
+### **Explanation:**
+1. **Create DataFrames** → Convert customer and product tables into Pandas DataFrames.
+2. **Get total unique products** → `product_df['product_key'].nunique()`
+3. **Count distinct products per customer** → `.groupby('customer_id')['product_key'].nunique()`
+4. **Filter customers who match total products** → Customers with `purchased_count == total_products`
+5. **Return final result**.
+
+---
+
+## **File Structure**
+```
+LeetCode1045/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solutions (Standard + Window Functions).
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 1045](https://leetcode.com/problems/customers-who-bought-all-products/)
+- [SQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
+- [SQL HAVING Clause](https://www.w3schools.com/sql/sql_having.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+
+---ture & Useful Links**  
+
+🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file

From 7009703b5226522e5f3fd108cdc68d1146e1ee1e Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:11:57 +0530
Subject: [PATCH 268/353] Update 1045. Customers Who Bought All Products.py

---
 ...1045. Customers Who Bought All Products.py | 21 +++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/1045. Customers Who Bought All Products.py b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/1045. Customers Who Bought All Products.py
index e69de29..5db1189 100644
--- a/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/1045. Customers Who Bought All Products.py	
+++ b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/1045. Customers Who Bought All Products.py	
@@ -0,0 +1,21 @@
+import pandas as pd
+
+# Sample data
+customer_data = {'customer_id': [1, 2, 3, 3, 1],
+                 'product_key': [5, 6, 5, 6, 6]}
+product_data = {'product_key': [5, 6]}
+
+# Create DataFrames
+customer_df = pd.DataFrame(customer_data)
+product_df = pd.DataFrame(product_data)
+
+# Get the total number of products
+total_products = product_df['product_key'].nunique()
+
+# Count distinct products per customer
+customer_purchase = customer_df.groupby('customer_id')['product_key'].nunique()
+
+# Filter customers who bought all products
+result = customer_purchase[customer_purchase == total_products].reset_index()
+
+print(result)
\ No newline at end of file

From 93d3f17fe8a255621acb67b33af226ec1937384d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:13:21 +0530
Subject: [PATCH 269/353] Update readme.md

---
 .../1068. Product Sales Analysis I/readme.md  | 168 ++++++++++++++++++
 1 file changed, 168 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md
index e69de29..0ae642e 100644
--- a/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md	
+++ b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md	
@@ -0,0 +1,168 @@
+
+
+# **1068. Product Sales Analysis I**
+
+## **Problem Statement**
+You are given two tables:  
+
+- `Sales` (contains sales data including `product_id`, `year`, `quantity`, and `price`).  
+- `Product` (contains `product_id` and `product_name`).
+
+Each `product_id` in `Sales` is a **foreign key** referring to the `Product` table.
+
+### **Sales Table**
+```
++---------+------------+------+----------+-------+
+| sale_id | product_id | year | quantity | price |
++---------+------------+------+----------+-------+
+| int     | int        | int  | int      | int   |
++---------+------------+------+----------+-------+
+```
+- `(sale_id, year)` is the **primary key** (unique values).
+- `product_id` refers to the `Product` table.
+- `price` represents the **per unit price** of the product in that year.
+
+### **Product Table**
+```
++------------+--------------+
+| product_id | product_name |
++------------+--------------+
+| int        | varchar      |
++------------+--------------+
+```
+- `product_id` is the **primary key** of this table.
+
+### **Task:**
+Find the `product_name`, `year`, and `price` for each sale in the `Sales` table.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+**Sales Table**
+```
++---------+------------+------+----------+-------+
+| sale_id | product_id | year | quantity | price |
++---------+------------+------+----------+-------+
+| 1       | 100        | 2008 | 10       | 5000  |
+| 2       | 100        | 2009 | 12       | 5000  |
+| 7       | 200        | 2011 | 15       | 9000  |
++---------+------------+------+----------+-------+
+```
+
+**Product Table**
+```
++------------+--------------+
+| product_id | product_name |
++------------+--------------+
+| 100        | Nokia        |
+| 200        | Apple        |
+| 300        | Samsung      |
++------------+--------------+
+```
+
+### **Output:**
+```
++--------------+-------+-------+
+| product_name | year  | price |
++--------------+-------+-------+
+| Nokia        | 2008  | 5000  |
+| Nokia        | 2009  | 5000  |
+| Apple        | 2011  | 9000  |
++--------------+-------+-------+
+```
+
+### **Explanation:**
+- **Sale ID 1:** `Nokia` was sold in **2008** for **5000**.
+- **Sale ID 2:** `Nokia` was sold in **2009** for **5000**.
+- **Sale ID 7:** `Apple` was sold in **2011** for **9000**.
+
+---
+
+## **SQL Solutions**
+
+### **1️⃣ Standard MySQL Solution**
+```sql
+SELECT p.product_name, s.year, s.price
+FROM Sales s
+JOIN Product p ON s.product_id = p.product_id;
+```
+#### **Explanation:**
+1. **JOIN** the `Sales` table with the `Product` table using `product_id`.
+2. **Select `product_name`, `year`, and `price`** from the joined result.
+
+---
+
+### **2️⃣ Window Function (SQL) Solution**
+```sql
+WITH SalesData AS (
+    SELECT s.product_id, s.year, s.price, p.product_name
+    FROM Sales s
+    JOIN Product p ON s.product_id = p.product_id
+)
+SELECT product_name, year, price 
+FROM SalesData;
+```
+#### **Explanation:**
+1. **CTE `SalesData`** → Stores the joined data from `Sales` and `Product`.
+2. **Final SELECT** → Retrieves `product_name`, `year`, and `price`.
+
+---
+
+## **Pandas Solution (Python)**
+```python
+import pandas as pd
+
+# Sample Data
+sales_data = {'sale_id': [1, 2, 7], 
+              'product_id': [100, 100, 200], 
+              'year': [2008, 2009, 2011], 
+              'quantity': [10, 12, 15], 
+              'price': [5000, 5000, 9000]}
+
+product_data = {'product_id': [100, 200, 300], 
+                'product_name': ['Nokia', 'Apple', 'Samsung']}
+
+# Create DataFrames
+sales_df = pd.DataFrame(sales_data)
+product_df = pd.DataFrame(product_data)
+
+# Perform Join
+result = sales_df.merge(product_df, on='product_id')[['product_name', 'year', 'price']]
+
+print(result)
+```
+
+### **Explanation:**
+1. **Create DataFrames** → Convert `Sales` and `Product` tables into Pandas DataFrames.
+2. **Perform `merge()` on `product_id`** → Equivalent to SQL `JOIN`.
+3. **Select required columns (`product_name`, `year`, `price`)**.
+
+---
+
+## **File Structure**
+```
+LeetCode1068/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solutions (Standard + Window Functions).
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 1068](https://leetcode.com/problems/product-sales-analysis-i/)
+- [SQL JOIN Documentation](https://www.w3schools.com/sql/sql_join.asp)
+- [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
+
+---
+
+This README now includes:
+✅ **MySQL Query**  
+✅ **Window SQL Query**  
+✅ **Pandas Python Solution**  
+✅ **File Structure & Useful Links**  
+
+🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file

From e4f2ac9d367b495aa4b694ef7569d01d706d19c3 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:13:59 +0530
Subject: [PATCH 270/353] Update readme.md

---
 .../1068. Product Sales Analysis I/readme.md              | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md
index 0ae642e..94b47f5 100644
--- a/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md	
+++ b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/readme.md	
@@ -157,12 +157,6 @@ LeetCode1068/
 - [SQL JOIN Documentation](https://www.w3schools.com/sql/sql_join.asp)
 - [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
 
----
 
-This README now includes:
-✅ **MySQL Query**  
-✅ **Window SQL Query**  
-✅ **Pandas Python Solution**  
-✅ **File Structure & Useful Links**  
 
-🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file
+## 🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file

From 8b781315836ab591152b17f974e1f8805c844a82 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:14:09 +0530
Subject: [PATCH 271/353] Update 1068. Product Sales Analysis I.py

---
 .../1068. Product Sales Analysis I.py         | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/1068. Product Sales Analysis I.py b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/1068. Product Sales Analysis I.py
index e69de29..a23b6c2 100644
--- a/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/1068. Product Sales Analysis I.py	
+++ b/LeetCode SQL 50 Solution/1068. Product Sales Analysis I/1068. Product Sales Analysis I.py	
@@ -0,0 +1,20 @@
+import pandas as pd
+
+# Sample Data
+sales_data = {'sale_id': [1, 2, 7], 
+              'product_id': [100, 100, 200], 
+              'year': [2008, 2009, 2011], 
+              'quantity': [10, 12, 15], 
+              'price': [5000, 5000, 9000]}
+
+product_data = {'product_id': [100, 200, 300], 
+                'product_name': ['Nokia', 'Apple', 'Samsung']}
+
+# Create DataFrames
+sales_df = pd.DataFrame(sales_data)
+product_df = pd.DataFrame(product_data)
+
+# Perform Join
+result = sales_df.merge(product_df, on='product_id')[['product_name', 'year', 'price']]
+
+print(result)
\ No newline at end of file

From d390a8c03c7b6f0865b62cc5eba810fbfe65168b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:15:01 +0530
Subject: [PATCH 272/353] Update readme.md

---
 .../readme.md                                 | 189 ++++++++++++++++++
 1 file changed, 189 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md
index e69de29..e873703 100644
--- a/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md	
+++ b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md	
@@ -0,0 +1,189 @@
+Here's the **README.md** file including **MySQL, Window SQL, and Pandas solutions** for **LeetCode 1070 - Product Sales Analysis III**.
+
+---
+
+# **1070. Product Sales Analysis III**
+
+## **Problem Statement**
+You are given two tables:
+
+- `Sales` (contains sales data including `product_id`, `year`, `quantity`, and `price`).  
+- `Product` (contains `product_id` and `product_name`).
+
+Each `product_id` in `Sales` is a **foreign key** referring to the `Product` table.
+
+### **Sales Table**
+```
++---------+------------+------+----------+-------+
+| sale_id | product_id | year | quantity | price |
++---------+------------+------+----------+-------+
+| int     | int        | int  | int      | int   |
++---------+------------+------+----------+-------+
+```
+- `(sale_id, year)` is the **primary key** (unique values).
+- `product_id` refers to the `Product` table.
+- `price` represents the **per unit price** of the product in that year.
+
+### **Product Table**
+```
++------------+--------------+
+| product_id | product_name |
++------------+--------------+
+| int        | varchar      |
++------------+--------------+
+```
+- `product_id` is the **primary key** of this table.
+
+### **Task:**
+Find the `product_id`, `first_year`, `quantity`, and `price` for **the first year a product was sold**.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+**Sales Table**
+```
++---------+------------+------+----------+-------+
+| sale_id | product_id | year | quantity | price |
++---------+------------+------+----------+-------+
+| 1       | 100        | 2008 | 10       | 5000  |
+| 2       | 100        | 2009 | 12       | 5000  |
+| 7       | 200        | 2011 | 15       | 9000  |
++---------+------------+------+----------+-------+
+```
+
+**Product Table**
+```
++------------+--------------+
+| product_id | product_name |
++------------+--------------+
+| 100        | Nokia        |
+| 200        | Apple        |
+| 300        | Samsung      |
++------------+--------------+
+```
+
+### **Output:**
+```
++------------+------------+----------+-------+
+| product_id | first_year | quantity | price |
++------------+------------+----------+-------+
+| 100        | 2008       | 10       | 5000  |
+| 200        | 2011       | 15       | 9000  |
++------------+------------+----------+-------+
+```
+
+### **Explanation:**
+- **Product 100 (Nokia):** First sold in **2008** with **10 units** at **5000** price.
+- **Product 200 (Apple):** First sold in **2011** with **15 units** at **9000** price.
+
+---
+
+## **SQL Solutions**
+
+### **1️⃣ Standard MySQL Solution**
+```sql
+SELECT 
+  product_id, 
+  year AS first_year, 
+  quantity, 
+  price 
+FROM 
+  Sales 
+WHERE 
+  (product_id, year) IN (
+    SELECT 
+      product_id, 
+      MIN(year) AS year 
+    FROM 
+      Sales 
+    GROUP BY 
+      product_id
+  );
+```
+#### **Explanation:**
+1. **Subquery (`MIN(year)`)** → Finds the **first year** (`MIN(year)`) each `product_id` was sold.
+2. **Filter the main table** → Selects rows matching the **earliest year** for each product.
+
+---
+
+### **2️⃣ Window Function (SQL) Solution**
+```sql
+WITH RankedSales AS (
+    SELECT 
+        product_id, 
+        year AS first_year, 
+        quantity, 
+        price,
+        RANK() OVER (PARTITION BY product_id ORDER BY year ASC) AS rnk
+    FROM Sales
+)
+SELECT product_id, first_year, quantity, price
+FROM RankedSales
+WHERE rnk = 1;
+```
+#### **Explanation:**
+1. **`RANK() OVER (PARTITION BY product_id ORDER BY year ASC)`**  
+   - Assigns **rank 1** to the first sale per `product_id`.
+2. **Filter (`WHERE rnk = 1`)**  
+   - Retrieves **only the first sale per product**.
+
+---
+
+## **Pandas Solution (Python)**
+```python
+import pandas as pd
+
+# Sample Data
+sales_data = {'sale_id': [1, 2, 7], 
+              'product_id': [100, 100, 200], 
+              'year': [2008, 2009, 2011], 
+              'quantity': [10, 12, 15], 
+              'price': [5000, 5000, 9000]}
+
+# Create DataFrame
+sales_df = pd.DataFrame(sales_data)
+
+# Find the first sale per product
+first_sales = sales_df.loc[sales_df.groupby('product_id')['year'].idxmin(), ['product_id', 'year', 'quantity', 'price']]
+
+# Rename columns
+first_sales.rename(columns={'year': 'first_year'}, inplace=True)
+
+print(first_sales)
+```
+
+### **Explanation:**
+1. **Create DataFrame** → Convert `Sales` table into Pandas DataFrame.
+2. **Group by `product_id` and get the `idxmin()` of `year`** → Finds the first sale per product.
+3. **Retrieve `product_id`, `year`, `quantity`, and `price`**.
+4. **Rename `year` to `first_year`**.
+
+---
+
+## **File Structure**
+```
+LeetCode1070/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solutions (Standard + Window Functions).
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 1070](https://leetcode.com/problems/product-sales-analysis-iii/)
+- [SQL JOIN Documentation](https://www.w3schools.com/sql/sql_join.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+
+---
+
+This README now includes:
+✅ **MySQL Query**  
+✅ **Window SQL Query**  
+✅ **Pandas Python Solution**  
+✅ **File Structure & Useful Links**  
+
+🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file

From 4e913498047e8fabfff48c6ea4fa31cecac485ae Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:15:34 +0530
Subject: [PATCH 273/353] Update readme.md

---
 .../1070. Product Sales Analysis III/readme.md       | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md
index e873703..b8bff49 100644
--- a/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md	
+++ b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md	
@@ -1,6 +1,3 @@
-Here's the **README.md** file including **MySQL, Window SQL, and Pandas solutions** for **LeetCode 1070 - Product Sales Analysis III**.
-
----
 
 # **1070. Product Sales Analysis III**
 
@@ -178,12 +175,5 @@ LeetCode1070/
 - [SQL JOIN Documentation](https://www.w3schools.com/sql/sql_join.asp)
 - [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
 
----
-
-This README now includes:
-✅ **MySQL Query**  
-✅ **Window SQL Query**  
-✅ **Pandas Python Solution**  
-✅ **File Structure & Useful Links**  
 
-🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file
+## 🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file

From 7ee1d6cba086e62408f052176bd9caf507948159 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Sun, 30 Mar 2025 03:15:51 +0530
Subject: [PATCH 274/353] Update 1070. Product Sales Analysis III.py

---
 .../1070. Product Sales Analysis III.py       | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/1070. Product Sales Analysis III.py b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/1070. Product Sales Analysis III.py
index e69de29..310d14c 100644
--- a/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/1070. Product Sales Analysis III.py	
+++ b/LeetCode SQL 50 Solution/1070. Product Sales Analysis III/1070. Product Sales Analysis III.py	
@@ -0,0 +1,19 @@
+import pandas as pd
+
+# Sample Data
+sales_data = {'sale_id': [1, 2, 7], 
+              'product_id': [100, 100, 200], 
+              'year': [2008, 2009, 2011], 
+              'quantity': [10, 12, 15], 
+              'price': [5000, 5000, 9000]}
+
+# Create DataFrame
+sales_df = pd.DataFrame(sales_data)
+
+# Find the first sale per product
+first_sales = sales_df.loc[sales_df.groupby('product_id')['year'].idxmin(), ['product_id', 'year', 'quantity', 'price']]
+
+# Rename columns
+first_sales.rename(columns={'year': 'first_year'}, inplace=True)
+
+print(first_sales)
\ No newline at end of file

From dc83e56fbf5edc0b7f295095d3b3689d9c879999 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 10:52:15 +0530
Subject: [PATCH 275/353] Update readme.md

---
 .../readme.md                                 | 174 ++++++++++++++++++
 1 file changed, 174 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md
index e69de29..c3f4599 100644
--- a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md	
+++ b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md	
@@ -0,0 +1,174 @@
+Here's the **README.md** file including **MySQL, Window SQL, and Pandas solutions** for **LeetCode 1164 - Product Price at a Given Date**.
+
+---
+
+# **1164. Product Price at a Given Date**
+
+## **Problem Statement**
+You are given the **Products** table, which keeps track of price changes.
+
+### **Products Table**
+```
++------------+-----------+-------------+
+| product_id | new_price | change_date |
++------------+-----------+-------------+
+| int        | int       | date        |
++------------+-----------+-------------+
+```
+- `(product_id, change_date)` is the **primary key**.
+- Each row represents a price update for a product on a specific date.
+
+### **Task:**
+Find the price of all products on **2019-08-16**.  
+Assume the **initial price of all products is 10** before any change occurs.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+**Products Table**
+```
++------------+-----------+-------------+
+| product_id | new_price | change_date |
++------------+-----------+-------------+
+| 1          | 20        | 2019-08-14  |
+| 2          | 50        | 2019-08-14  |
+| 1          | 30        | 2019-08-15  |
+| 1          | 35        | 2019-08-16  |
+| 2          | 65        | 2019-08-17  |
+| 3          | 20        | 2019-08-18  |
++------------+-----------+-------------+
+```
+
+### **Output:**
+```
++------------+-------+
+| product_id | price |
++------------+-------+
+| 2          | 50    |
+| 1          | 35    |
+| 3          | 10    |
++------------+-------+
+```
+
+### **Explanation:**
+- **Product 1:** Last change before `2019-08-16` → **35**  
+- **Product 2:** Last change before `2019-08-16` → **50**  
+- **Product 3:** **No price change before 2019-08-16**, so default price is **10**  
+
+---
+
+## **SQL Solutions**
+
+### **1️⃣ Standard MySQL Solution**
+```sql
+SELECT 
+    p.product_id, 
+    COALESCE((
+        SELECT new_price 
+        FROM Products 
+        WHERE product_id = p.product_id 
+        AND change_date <= '2019-08-16' 
+        ORDER BY change_date DESC 
+        LIMIT 1
+    ), 10) AS price
+FROM 
+    (SELECT DISTINCT product_id FROM Products) p;
+```
+#### **Explanation:**
+1. **Find the last price before or on `2019-08-16`**  
+   - `ORDER BY change_date DESC LIMIT 1` → Gets the most recent price before `2019-08-16`.
+2. **Use `COALESCE()`**  
+   - If no price exists, set default price **10**.
+3. **Use `DISTINCT product_id`**  
+   - Ensures all unique products are checked.
+
+---
+
+### **2️⃣ Window Function (SQL) Solution**
+```sql
+WITH RankedPrices AS (
+    SELECT 
+        product_id, 
+        new_price AS price, 
+        change_date,
+        RANK() OVER (PARTITION BY product_id ORDER BY change_date DESC) AS rnk
+    FROM Products
+    WHERE change_date <= '2019-08-16'
+)
+SELECT p.product_id, COALESCE(rp.price, 10) AS price
+FROM (SELECT DISTINCT product_id FROM Products) p
+LEFT JOIN RankedPrices rp ON p.product_id = rp.product_id AND rp.rnk = 1;
+```
+#### **Explanation:**
+1. **`RANK() OVER (PARTITION BY product_id ORDER BY change_date DESC)`**  
+   - Assigns **rank 1** to the last price before `2019-08-16`.
+2. **`LEFT JOIN` with `DISTINCT product_id`**  
+   - Ensures all products are included.
+3. **Use `COALESCE(price, 10)`**  
+   - If no price exists, set default **10**.
+
+---
+
+## **Pandas Solution (Python)**
+```python
+import pandas as pd
+
+# Sample Data
+products_data = {
+    'product_id': [1, 2, 1, 1, 2, 3],
+    'new_price': [20, 50, 30, 35, 65, 20],
+    'change_date': ['2019-08-14', '2019-08-14', '2019-08-15', '2019-08-16', '2019-08-17', '2019-08-18']
+}
+
+# Create DataFrame
+products_df = pd.DataFrame(products_data)
+products_df['change_date'] = pd.to_datetime(products_df['change_date'])  # Convert to datetime
+
+# Filter for changes before or on '2019-08-16'
+valid_prices = products_df[products_df['change_date'] <= '2019-08-16']
+
+# Get the latest price for each product before '2019-08-16'
+latest_prices = valid_prices.sort_values(by=['product_id', 'change_date']).groupby('product_id').last().reset_index()
+
+# Rename column
+latest_prices = latest_prices[['product_id', 'new_price']].rename(columns={'new_price': 'price'})
+
+# Get all unique products
+all_products = products_df[['product_id']].drop_duplicates()
+
+# Merge with latest prices and fill missing values with 10
+final_prices = all_products.merge(latest_prices, on='product_id', how='left').fillna({'price': 10})
+
+print(final_prices)
+```
+
+### **Explanation:**
+1. **Convert `change_date` to datetime**  
+   - Ensures proper date comparison.
+2. **Filter for prices before `2019-08-16`**  
+   - Excludes future price changes.
+3. **Get the latest price per product (`groupby().last()`)**  
+   - Retrieves the most recent price change.
+4. **Merge with all products and set missing prices to `10`**  
+   - Ensures all products are included.
+
+---
+
+## **File Structure**
+```
+LeetCode1164/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_solution.sql           # Contains the SQL solutions (Standard + Window Functions).
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 1164](https://leetcode.com/problems/product-price-at-a-given-date/)
+- [SQL COALESCE Documentation](https://www.w3schools.com/sql/sql_coalesce.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+

From 7863db6ba350f46ee1b52bbb5e509362efe4445b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 10:52:21 +0530
Subject: [PATCH 276/353] Update readme.md

---
 .../1164. Product Price at a Given Date/readme.md               | 2 --
 1 file changed, 2 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md
index c3f4599..c10bb5b 100644
--- a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md	
+++ b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md	
@@ -1,6 +1,4 @@
-Here's the **README.md** file including **MySQL, Window SQL, and Pandas solutions** for **LeetCode 1164 - Product Price at a Given Date**.
 
----
 
 # **1164. Product Price at a Given Date**
 

From cf7a2461b4ac98db68898115db3ef84d7e10f677 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 10:56:06 +0530
Subject: [PATCH 277/353] Update readme.md

---
 .../readme.md                                 | 31 ++++++++++++-------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md
index c10bb5b..e4aaf10 100644
--- a/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md	
+++ b/LeetCode SQL 50 Solution/1164. Product Price at a Given Date/readme.md	
@@ -86,18 +86,25 @@ FROM
 
 ### **2️⃣ Window Function (SQL) Solution**
 ```sql
-WITH RankedPrices AS (
-    SELECT 
-        product_id, 
-        new_price AS price, 
-        change_date,
-        RANK() OVER (PARTITION BY product_id ORDER BY change_date DESC) AS rnk
-    FROM Products
-    WHERE change_date <= '2019-08-16'
-)
-SELECT p.product_id, COALESCE(rp.price, 10) AS price
-FROM (SELECT DISTINCT product_id FROM Products) p
-LEFT JOIN RankedPrices rp ON p.product_id = rp.product_id AND rp.rnk = 1;
+# Write your MySQL query statement below
+# Write your MySQL query statement below
+WITH
+    T AS (SELECT DISTINCT product_id FROM Products),
+    P AS (
+        SELECT product_id, new_price AS price
+        FROM Products
+        WHERE
+            (product_id, change_date) IN (
+                SELECT product_id, MAX(change_date) AS change_date
+                FROM Products
+                WHERE change_date <= '2019-08-16'
+                GROUP BY 1
+            )
+    )
+SELECT product_id, IFNULL(price, 10) AS price
+FROM
+    T
+    LEFT JOIN P USING (product_id);
 ```
 #### **Explanation:**
 1. **`RANK() OVER (PARTITION BY product_id ORDER BY change_date DESC)`**  

From 465cd510dd239f2252c77712e3567b9cbc2df164 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 10:59:21 +0530
Subject: [PATCH 278/353] Update readme.md

---
 .../1075. Project Employees I/readme.md       | 192 ++++++++++++++++++
 1 file changed, 192 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md
index e69de29..62bd0b2 100644
--- a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
+++ b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
@@ -0,0 +1,192 @@
+Below is a structured README.md for **LeetCode 1174: Immediate Food Delivery II** following your requested format.
+
+---
+
+# **1174. Immediate Food Delivery II**
+
+## **Problem Statement**
+You are given a table `Delivery` that records food deliveries made to customers. Each row represents an order with the date it was placed and the customer’s preferred delivery date.
+
+---
+
+## **Delivery Table**
+```
++-------------+-------------+------------+-----------------------------+
+| Column Name | Type        | Description                                  |
++-------------+-------------+----------------------------------------------+
+| delivery_id | int         | Unique identifier for the delivery           |
+| customer_id | int         | Identifier for the customer                  |
+| order_date  | date        | Date when the order was placed               |
+| customer_pref_delivery_date | date | Customer’s preferred delivery date  |
++-------------+-------------+----------------------------------------------+
+```
+- `delivery_id` is the **primary key**.
+- Each customer specifies a preferred delivery date, which can be the same as or after the order date.
+
+---
+
+## **Task:**
+Calculate the **percentage** of customers whose **first order** is **immediate** (i.e., the order date is the same as the customer’s preferred delivery date).  
+- A customer’s **first order** is defined as the order with the **earliest order_date** for that customer.
+- The result should be **rounded to 2 decimal places**.
+- Return the percentage as `immediate_percentage`.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+**Delivery Table**
+```
++-------------+-------------+------------+-----------------------------+
+| delivery_id | customer_id | order_date | customer_pref_delivery_date |
++-------------+-------------+------------+-----------------------------+
+| 1           | 1           | 2019-08-01 | 2019-08-02                  |
+| 2           | 2           | 2019-08-02 | 2019-08-02                  |
+| 3           | 1           | 2019-08-11 | 2019-08-12                  |
+| 4           | 3           | 2019-08-24 | 2019-08-24                  |
+| 5           | 3           | 2019-08-21 | 2019-08-22                  |
+| 6           | 2           | 2019-08-11 | 2019-08-13                  |
+| 7           | 4           | 2019-08-09 | 2019-08-09                  |
++-------------+-------------+------------+-----------------------------+
+```
+
+### **Output:**
+```
++----------------------+
+| immediate_percentage |
++----------------------+
+| 50.00                |
++----------------------+
+```
+
+### **Explanation:**
+- **Customer 1:** First order is on **2019-08-01** (preferred: 2019-08-02) → **Scheduled**
+- **Customer 2:** First order is on **2019-08-02** (preferred: 2019-08-02) → **Immediate**
+- **Customer 3:** First order is on **2019-08-21** (preferred: 2019-08-22) → **Scheduled**
+- **Customer 4:** First order is on **2019-08-09** (preferred: 2019-08-09) → **Immediate**
+  
+Out of 4 customers, 2 have immediate first orders.  
+Percentage = (2 / 4) * 100 = **50.00**
+
+---
+
+## **SQL Solutions**
+
+### **1️⃣ Standard MySQL Solution**
+```sql
+SELECT 
+    ROUND(100 * SUM(CASE 
+        WHEN first_orders.order_date = first_orders.customer_pref_delivery_date THEN 1 
+        ELSE 0 
+    END) / COUNT(*), 2) AS immediate_percentage
+FROM (
+    -- Get the first order (earliest order_date) for each customer
+    SELECT customer_id, order_date, customer_pref_delivery_date 
+    FROM Delivery 
+    WHERE (customer_id, order_date) IN (
+        SELECT customer_id, MIN(order_date) 
+        FROM Delivery 
+        GROUP BY customer_id
+    )
+) AS first_orders;
+```
+
+#### **Explanation:**
+- **Subquery:** Retrieves the first order for each customer by selecting the minimum `order_date`.
+- **Outer Query:**  
+  - Uses a `CASE` statement to check if the `order_date` equals `customer_pref_delivery_date` (i.e., immediate order).
+  - Calculates the percentage of immediate first orders.
+  - Rounds the result to 2 decimal places.
+
+---
+
+### **2️⃣ Window Function (SQL) Solution**
+```sql
+WITH RankedOrders AS (
+    SELECT 
+        customer_id, 
+        order_date, 
+        customer_pref_delivery_date,
+        ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS rn
+    FROM Delivery
+)
+SELECT 
+    ROUND(100 * SUM(CASE WHEN order_date = customer_pref_delivery_date THEN 1 ELSE 0 END) / COUNT(*), 2) AS immediate_percentage
+FROM RankedOrders
+WHERE rn = 1;
+```
+
+#### **Explanation:**
+- **CTE `RankedOrders`:**  
+  - Uses `ROW_NUMBER()` to rank orders for each customer by `order_date`.
+  - Filters for the first order of each customer (`rn = 1`).
+- **Final SELECT:**  
+  - Computes the percentage of first orders that are immediate.
+  - Rounds the result to 2 decimal places.
+
+---
+
+## **Pandas Solution (Python)**
+```python
+import pandas as pd
+
+def immediate_food_delivery_percentage(delivery: pd.DataFrame) -> pd.DataFrame:
+    # Ensure order_date and customer_pref_delivery_date are in datetime format
+    delivery['order_date'] = pd.to_datetime(delivery['order_date'])
+    delivery['customer_pref_delivery_date'] = pd.to_datetime(delivery['customer_pref_delivery_date'])
+    
+    # Get the first order date for each customer
+    first_order = delivery.groupby('customer_id')['order_date'].min().reset_index()
+    first_order = first_order.rename(columns={'order_date': 'first_order_date'})
+    
+    # Merge to get the corresponding preferred delivery date for the first order
+    merged = pd.merge(delivery, first_order, on='customer_id', how='inner')
+    first_orders = merged[merged['order_date'] == merged['first_order_date']]
+    
+    # Calculate immediate orders
+    immediate_count = (first_orders['order_date'] == first_orders['customer_pref_delivery_date']).sum()
+    total_customers = first_orders['customer_id'].nunique()
+    immediate_percentage = round(100 * immediate_count / total_customers, 2)
+    
+    return pd.DataFrame({'immediate_percentage': [immediate_percentage]})
+
+# Example usage:
+# df = pd.read_csv('delivery.csv')
+# print(immediate_food_delivery_percentage(df))
+```
+
+#### **Explanation:**
+- **Convert Dates:**  
+  - Convert `order_date` and `customer_pref_delivery_date` to datetime for accurate comparison.
+- **Determine First Order:**  
+  - Group by `customer_id` to find the minimum `order_date` as the first order.
+  - Merge with the original DataFrame to obtain details of the first order.
+- **Calculate Percentage:**  
+  - Count how many first orders are immediate (where `order_date` equals `customer_pref_delivery_date`).
+  - Compute the percentage and round to 2 decimal places.
+  
+---
+
+## **File Structure**
+```
+LeetCode1174/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_standard_solution.sql  # Contains the Standard MySQL solution.
+├── sql_window_solution.sql    # Contains the Window Function solution.
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 1174](https://leetcode.com/problems/immediate-food-delivery-ii/)
+- [SQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
+- [SQL Window Functions](https://www.w3schools.com/sql/sql_window.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+- [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
+
+---
+
+This complete guide provides multiple solution approaches with detailed explanations, file organization, and useful resources. Happy coding! 🚀
\ No newline at end of file

From 5cd49412d1f9711b01643c9a4eeb39cc2057562d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 10:59:40 +0530
Subject: [PATCH 279/353] Update readme.md

---
 .../1075. Project Employees I/readme.md                     | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md
index 62bd0b2..ad1e800 100644
--- a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
+++ b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
@@ -1,6 +1,4 @@
-Below is a structured README.md for **LeetCode 1174: Immediate Food Delivery II** following your requested format.
 
----
 
 # **1174. Immediate Food Delivery II**
 
@@ -186,7 +184,3 @@ LeetCode1174/
 - [SQL Window Functions](https://www.w3schools.com/sql/sql_window.asp)
 - [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
 - [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
-
----
-
-This complete guide provides multiple solution approaches with detailed explanations, file organization, and useful resources. Happy coding! 🚀
\ No newline at end of file

From 8ff3e3a116745bfbcc1d616e054596e5f894489b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:00:30 +0530
Subject: [PATCH 280/353] as

---
 .../1075. Project Employees I/readme.md       | 186 ------------------
 1 file changed, 186 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md
index ad1e800..e69de29 100644
--- a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
+++ b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
@@ -1,186 +0,0 @@
-
-
-# **1174. Immediate Food Delivery II**
-
-## **Problem Statement**
-You are given a table `Delivery` that records food deliveries made to customers. Each row represents an order with the date it was placed and the customer’s preferred delivery date.
-
----
-
-## **Delivery Table**
-```
-+-------------+-------------+------------+-----------------------------+
-| Column Name | Type        | Description                                  |
-+-------------+-------------+----------------------------------------------+
-| delivery_id | int         | Unique identifier for the delivery           |
-| customer_id | int         | Identifier for the customer                  |
-| order_date  | date        | Date when the order was placed               |
-| customer_pref_delivery_date | date | Customer’s preferred delivery date  |
-+-------------+-------------+----------------------------------------------+
-```
-- `delivery_id` is the **primary key**.
-- Each customer specifies a preferred delivery date, which can be the same as or after the order date.
-
----
-
-## **Task:**
-Calculate the **percentage** of customers whose **first order** is **immediate** (i.e., the order date is the same as the customer’s preferred delivery date).  
-- A customer’s **first order** is defined as the order with the **earliest order_date** for that customer.
-- The result should be **rounded to 2 decimal places**.
-- Return the percentage as `immediate_percentage`.
-
----
-
-## **Example 1:**
-
-### **Input:**
-**Delivery Table**
-```
-+-------------+-------------+------------+-----------------------------+
-| delivery_id | customer_id | order_date | customer_pref_delivery_date |
-+-------------+-------------+------------+-----------------------------+
-| 1           | 1           | 2019-08-01 | 2019-08-02                  |
-| 2           | 2           | 2019-08-02 | 2019-08-02                  |
-| 3           | 1           | 2019-08-11 | 2019-08-12                  |
-| 4           | 3           | 2019-08-24 | 2019-08-24                  |
-| 5           | 3           | 2019-08-21 | 2019-08-22                  |
-| 6           | 2           | 2019-08-11 | 2019-08-13                  |
-| 7           | 4           | 2019-08-09 | 2019-08-09                  |
-+-------------+-------------+------------+-----------------------------+
-```
-
-### **Output:**
-```
-+----------------------+
-| immediate_percentage |
-+----------------------+
-| 50.00                |
-+----------------------+
-```
-
-### **Explanation:**
-- **Customer 1:** First order is on **2019-08-01** (preferred: 2019-08-02) → **Scheduled**
-- **Customer 2:** First order is on **2019-08-02** (preferred: 2019-08-02) → **Immediate**
-- **Customer 3:** First order is on **2019-08-21** (preferred: 2019-08-22) → **Scheduled**
-- **Customer 4:** First order is on **2019-08-09** (preferred: 2019-08-09) → **Immediate**
-  
-Out of 4 customers, 2 have immediate first orders.  
-Percentage = (2 / 4) * 100 = **50.00**
-
----
-
-## **SQL Solutions**
-
-### **1️⃣ Standard MySQL Solution**
-```sql
-SELECT 
-    ROUND(100 * SUM(CASE 
-        WHEN first_orders.order_date = first_orders.customer_pref_delivery_date THEN 1 
-        ELSE 0 
-    END) / COUNT(*), 2) AS immediate_percentage
-FROM (
-    -- Get the first order (earliest order_date) for each customer
-    SELECT customer_id, order_date, customer_pref_delivery_date 
-    FROM Delivery 
-    WHERE (customer_id, order_date) IN (
-        SELECT customer_id, MIN(order_date) 
-        FROM Delivery 
-        GROUP BY customer_id
-    )
-) AS first_orders;
-```
-
-#### **Explanation:**
-- **Subquery:** Retrieves the first order for each customer by selecting the minimum `order_date`.
-- **Outer Query:**  
-  - Uses a `CASE` statement to check if the `order_date` equals `customer_pref_delivery_date` (i.e., immediate order).
-  - Calculates the percentage of immediate first orders.
-  - Rounds the result to 2 decimal places.
-
----
-
-### **2️⃣ Window Function (SQL) Solution**
-```sql
-WITH RankedOrders AS (
-    SELECT 
-        customer_id, 
-        order_date, 
-        customer_pref_delivery_date,
-        ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS rn
-    FROM Delivery
-)
-SELECT 
-    ROUND(100 * SUM(CASE WHEN order_date = customer_pref_delivery_date THEN 1 ELSE 0 END) / COUNT(*), 2) AS immediate_percentage
-FROM RankedOrders
-WHERE rn = 1;
-```
-
-#### **Explanation:**
-- **CTE `RankedOrders`:**  
-  - Uses `ROW_NUMBER()` to rank orders for each customer by `order_date`.
-  - Filters for the first order of each customer (`rn = 1`).
-- **Final SELECT:**  
-  - Computes the percentage of first orders that are immediate.
-  - Rounds the result to 2 decimal places.
-
----
-
-## **Pandas Solution (Python)**
-```python
-import pandas as pd
-
-def immediate_food_delivery_percentage(delivery: pd.DataFrame) -> pd.DataFrame:
-    # Ensure order_date and customer_pref_delivery_date are in datetime format
-    delivery['order_date'] = pd.to_datetime(delivery['order_date'])
-    delivery['customer_pref_delivery_date'] = pd.to_datetime(delivery['customer_pref_delivery_date'])
-    
-    # Get the first order date for each customer
-    first_order = delivery.groupby('customer_id')['order_date'].min().reset_index()
-    first_order = first_order.rename(columns={'order_date': 'first_order_date'})
-    
-    # Merge to get the corresponding preferred delivery date for the first order
-    merged = pd.merge(delivery, first_order, on='customer_id', how='inner')
-    first_orders = merged[merged['order_date'] == merged['first_order_date']]
-    
-    # Calculate immediate orders
-    immediate_count = (first_orders['order_date'] == first_orders['customer_pref_delivery_date']).sum()
-    total_customers = first_orders['customer_id'].nunique()
-    immediate_percentage = round(100 * immediate_count / total_customers, 2)
-    
-    return pd.DataFrame({'immediate_percentage': [immediate_percentage]})
-
-# Example usage:
-# df = pd.read_csv('delivery.csv')
-# print(immediate_food_delivery_percentage(df))
-```
-
-#### **Explanation:**
-- **Convert Dates:**  
-  - Convert `order_date` and `customer_pref_delivery_date` to datetime for accurate comparison.
-- **Determine First Order:**  
-  - Group by `customer_id` to find the minimum `order_date` as the first order.
-  - Merge with the original DataFrame to obtain details of the first order.
-- **Calculate Percentage:**  
-  - Count how many first orders are immediate (where `order_date` equals `customer_pref_delivery_date`).
-  - Compute the percentage and round to 2 decimal places.
-  
----
-
-## **File Structure**
-```
-LeetCode1174/
-├── problem_statement.md       # Contains the problem description and constraints.
-├── sql_standard_solution.sql  # Contains the Standard MySQL solution.
-├── sql_window_solution.sql    # Contains the Window Function solution.
-├── pandas_solution.py         # Contains the Pandas solution.
-├── README.md                  # Overview of the problem and available solutions.
-```
-
----
-
-## **Useful Links**
-- [LeetCode Problem 1174](https://leetcode.com/problems/immediate-food-delivery-ii/)
-- [SQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
-- [SQL Window Functions](https://www.w3schools.com/sql/sql_window.asp)
-- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-- [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)

From f863d4f8394cacdd05c8fba3d78465fe688c543c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:00:37 +0530
Subject: [PATCH 281/353] Update readme.md

---
 .../readme.md                                 | 186 ++++++++++++++++++
 1 file changed, 186 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1174. Immediate Food Delivery II/readme.md b/LeetCode SQL 50 Solution/1174. Immediate Food Delivery II/readme.md
index e69de29..ad1e800 100644
--- a/LeetCode SQL 50 Solution/1174. Immediate Food Delivery II/readme.md	
+++ b/LeetCode SQL 50 Solution/1174. Immediate Food Delivery II/readme.md	
@@ -0,0 +1,186 @@
+
+
+# **1174. Immediate Food Delivery II**
+
+## **Problem Statement**
+You are given a table `Delivery` that records food deliveries made to customers. Each row represents an order with the date it was placed and the customer’s preferred delivery date.
+
+---
+
+## **Delivery Table**
+```
++-------------+-------------+------------+-----------------------------+
+| Column Name | Type        | Description                                  |
++-------------+-------------+----------------------------------------------+
+| delivery_id | int         | Unique identifier for the delivery           |
+| customer_id | int         | Identifier for the customer                  |
+| order_date  | date        | Date when the order was placed               |
+| customer_pref_delivery_date | date | Customer’s preferred delivery date  |
++-------------+-------------+----------------------------------------------+
+```
+- `delivery_id` is the **primary key**.
+- Each customer specifies a preferred delivery date, which can be the same as or after the order date.
+
+---
+
+## **Task:**
+Calculate the **percentage** of customers whose **first order** is **immediate** (i.e., the order date is the same as the customer’s preferred delivery date).  
+- A customer’s **first order** is defined as the order with the **earliest order_date** for that customer.
+- The result should be **rounded to 2 decimal places**.
+- Return the percentage as `immediate_percentage`.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+**Delivery Table**
+```
++-------------+-------------+------------+-----------------------------+
+| delivery_id | customer_id | order_date | customer_pref_delivery_date |
++-------------+-------------+------------+-----------------------------+
+| 1           | 1           | 2019-08-01 | 2019-08-02                  |
+| 2           | 2           | 2019-08-02 | 2019-08-02                  |
+| 3           | 1           | 2019-08-11 | 2019-08-12                  |
+| 4           | 3           | 2019-08-24 | 2019-08-24                  |
+| 5           | 3           | 2019-08-21 | 2019-08-22                  |
+| 6           | 2           | 2019-08-11 | 2019-08-13                  |
+| 7           | 4           | 2019-08-09 | 2019-08-09                  |
++-------------+-------------+------------+-----------------------------+
+```
+
+### **Output:**
+```
++----------------------+
+| immediate_percentage |
++----------------------+
+| 50.00                |
++----------------------+
+```
+
+### **Explanation:**
+- **Customer 1:** First order is on **2019-08-01** (preferred: 2019-08-02) → **Scheduled**
+- **Customer 2:** First order is on **2019-08-02** (preferred: 2019-08-02) → **Immediate**
+- **Customer 3:** First order is on **2019-08-21** (preferred: 2019-08-22) → **Scheduled**
+- **Customer 4:** First order is on **2019-08-09** (preferred: 2019-08-09) → **Immediate**
+  
+Out of 4 customers, 2 have immediate first orders.  
+Percentage = (2 / 4) * 100 = **50.00**
+
+---
+
+## **SQL Solutions**
+
+### **1️⃣ Standard MySQL Solution**
+```sql
+SELECT 
+    ROUND(100 * SUM(CASE 
+        WHEN first_orders.order_date = first_orders.customer_pref_delivery_date THEN 1 
+        ELSE 0 
+    END) / COUNT(*), 2) AS immediate_percentage
+FROM (
+    -- Get the first order (earliest order_date) for each customer
+    SELECT customer_id, order_date, customer_pref_delivery_date 
+    FROM Delivery 
+    WHERE (customer_id, order_date) IN (
+        SELECT customer_id, MIN(order_date) 
+        FROM Delivery 
+        GROUP BY customer_id
+    )
+) AS first_orders;
+```
+
+#### **Explanation:**
+- **Subquery:** Retrieves the first order for each customer by selecting the minimum `order_date`.
+- **Outer Query:**  
+  - Uses a `CASE` statement to check if the `order_date` equals `customer_pref_delivery_date` (i.e., immediate order).
+  - Calculates the percentage of immediate first orders.
+  - Rounds the result to 2 decimal places.
+
+---
+
+### **2️⃣ Window Function (SQL) Solution**
+```sql
+WITH RankedOrders AS (
+    SELECT 
+        customer_id, 
+        order_date, 
+        customer_pref_delivery_date,
+        ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS rn
+    FROM Delivery
+)
+SELECT 
+    ROUND(100 * SUM(CASE WHEN order_date = customer_pref_delivery_date THEN 1 ELSE 0 END) / COUNT(*), 2) AS immediate_percentage
+FROM RankedOrders
+WHERE rn = 1;
+```
+
+#### **Explanation:**
+- **CTE `RankedOrders`:**  
+  - Uses `ROW_NUMBER()` to rank orders for each customer by `order_date`.
+  - Filters for the first order of each customer (`rn = 1`).
+- **Final SELECT:**  
+  - Computes the percentage of first orders that are immediate.
+  - Rounds the result to 2 decimal places.
+
+---
+
+## **Pandas Solution (Python)**
+```python
+import pandas as pd
+
+def immediate_food_delivery_percentage(delivery: pd.DataFrame) -> pd.DataFrame:
+    # Ensure order_date and customer_pref_delivery_date are in datetime format
+    delivery['order_date'] = pd.to_datetime(delivery['order_date'])
+    delivery['customer_pref_delivery_date'] = pd.to_datetime(delivery['customer_pref_delivery_date'])
+    
+    # Get the first order date for each customer
+    first_order = delivery.groupby('customer_id')['order_date'].min().reset_index()
+    first_order = first_order.rename(columns={'order_date': 'first_order_date'})
+    
+    # Merge to get the corresponding preferred delivery date for the first order
+    merged = pd.merge(delivery, first_order, on='customer_id', how='inner')
+    first_orders = merged[merged['order_date'] == merged['first_order_date']]
+    
+    # Calculate immediate orders
+    immediate_count = (first_orders['order_date'] == first_orders['customer_pref_delivery_date']).sum()
+    total_customers = first_orders['customer_id'].nunique()
+    immediate_percentage = round(100 * immediate_count / total_customers, 2)
+    
+    return pd.DataFrame({'immediate_percentage': [immediate_percentage]})
+
+# Example usage:
+# df = pd.read_csv('delivery.csv')
+# print(immediate_food_delivery_percentage(df))
+```
+
+#### **Explanation:**
+- **Convert Dates:**  
+  - Convert `order_date` and `customer_pref_delivery_date` to datetime for accurate comparison.
+- **Determine First Order:**  
+  - Group by `customer_id` to find the minimum `order_date` as the first order.
+  - Merge with the original DataFrame to obtain details of the first order.
+- **Calculate Percentage:**  
+  - Count how many first orders are immediate (where `order_date` equals `customer_pref_delivery_date`).
+  - Compute the percentage and round to 2 decimal places.
+  
+---
+
+## **File Structure**
+```
+LeetCode1174/
+├── problem_statement.md       # Contains the problem description and constraints.
+├── sql_standard_solution.sql  # Contains the Standard MySQL solution.
+├── sql_window_solution.sql    # Contains the Window Function solution.
+├── pandas_solution.py         # Contains the Pandas solution.
+├── README.md                  # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 1174](https://leetcode.com/problems/immediate-food-delivery-ii/)
+- [SQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
+- [SQL Window Functions](https://www.w3schools.com/sql/sql_window.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+- [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)

From cd802c7b75d16dceff3fc9d315bf3454a697e5ab Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:07:56 +0530
Subject: [PATCH 282/353] Update readme.md

---
 .../1075. Project Employees I/readme.md       | 140 ++++++++++++++++++
 1 file changed, 140 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md
index e69de29..4b365c1 100644
--- a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
+++ b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
@@ -0,0 +1,140 @@
+Here's a well-formatted `README.md` file for your GitHub repository with the SQL solutions:
+
+```md
+# 🏆 Project Employees I - LeetCode 1075
+
+## 📌 Problem Statement
+You are given two tables: **Project** and **Employee**.
+
+### Project Table
+| Column Name | Type |
+| ----------- | ---- |
+| project_id  | int  |
+| employee_id | int  |
+
+- `(project_id, employee_id)` is the primary key of this table.
+- `employee_id` is a foreign key referencing the `Employee` table.
+
+### Employee Table
+| Column Name      | Type    |
+| ---------------- | ------- |
+| employee_id      | int     |
+| name             | varchar |
+| experience_years | int     |
+
+- `employee_id` is the primary key.
+- `experience_years` is guaranteed to be **NOT NULL**.
+
+The task is to **return the average experience years of all employees for each project, rounded to 2 decimal places**.
+
+---
+
+## 📊 Example 1:
+### Input:
+**Project Table**
+| project_id | employee_id |
+| ---------- | ----------- |
+| 1          | 1           |
+| 1          | 2           |
+| 1          | 3           |
+| 2          | 1           |
+| 2          | 4           |
+
+**Employee Table**
+| employee_id | name   | experience_years |
+| ----------- | ------ | ---------------- |
+| 1           | Khaled | 3                |
+| 2           | Ali    | 2                |
+| 3           | John   | 1                |
+| 4           | Doe    | 2                |
+
+### Output:
+| project_id | average_years |
+| ---------- | ------------- |
+| 1          | 2.00          |
+| 2          | 2.50          |
+
+### Explanation:
+- **Project 1:** `(3 + 2 + 1) / 3 = 2.00`
+- **Project 2:** `(3 + 2) / 2 = 2.50`
+
+---
+
+## 🖥 SQL Solutions
+
+### 1️⃣ Standard MySQL Solution
+#### Explanation:
+- We **JOIN** the `Project` and `Employee` tables using `employee_id`.
+- We **calculate the average** of `experience_years` for each `project_id`.
+- We **round** the result to **two decimal places**.
+
+```sql
+SELECT project_id, ROUND(AVG(experience_years), 2) AS average_years
+FROM project AS p
+LEFT JOIN employee AS e
+ON p.employee_id = e.employee_id
+GROUP BY project_id;
+```
+
+---
+
+### 2️⃣ Window Function (SQL) Solution
+#### Explanation:
+- Using **window functions**, we calculate the `AVG(experience_years)` over a **partitioned** dataset.
+
+```sql
+SELECT DISTINCT project_id, 
+       ROUND(AVG(experience_years) OVER (PARTITION BY project_id), 2) AS average_years
+FROM project AS p
+JOIN employee AS e 
+ON p.employee_id = e.employee_id;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### Explanation:
+- We read both tables into Pandas **DataFrames**.
+- We merge the tables on `employee_id`.
+- We group by `project_id` and compute the mean.
+- We round the output to 2 decimal places.
+
+```python
+import pandas as pd
+
+def project_average_experience(project: pd.DataFrame, employee: pd.DataFrame) -> pd.DataFrame:
+    df = project.merge(employee, on="employee_id")
+    result = df.groupby("project_id")["experience_years"].mean().round(2).reset_index()
+    result.columns = ["project_id", "average_years"]
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Project-Employees-I
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_window.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/project-employees-i/)
+- 📚 [SQL Joins Explanation](https://www.w3schools.com/sql/sql_join.asp)
+- 🐍 [Pandas Documentation](https://pandas.pydata.org/docs/)
+```
+
+This README provides:
+- A **clear problem statement**
+- **Input and output tables**
+- **Multiple SQL solutions with explanations**
+- **A Python Pandas solution**
+- **File structure**
+- **Useful links**
+
+Let me know if you need any modifications! 🚀
\ No newline at end of file

From a94c39a848bc761cc78f7907279f104f588799eb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:08:34 +0530
Subject: [PATCH 283/353] Update readme.md

---
 .../1075. Project Employees I/readme.md             | 13 ++-----------
 1 file changed, 2 insertions(+), 11 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md
index 4b365c1..4c1d8da 100644
--- a/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
+++ b/LeetCode SQL 50 Solution/1075. Project Employees I/readme.md	
@@ -1,6 +1,4 @@
-Here's a well-formatted `README.md` file for your GitHub repository with the SQL solutions:
 
-```md
 # 🏆 Project Employees I - LeetCode 1075
 
 ## 📌 Problem Statement
@@ -127,14 +125,7 @@ def project_average_experience(project: pd.DataFrame, employee: pd.DataFrame) ->
 - 📖 [LeetCode Problem](https://leetcode.com/problems/project-employees-i/)
 - 📚 [SQL Joins Explanation](https://www.w3schools.com/sql/sql_join.asp)
 - 🐍 [Pandas Documentation](https://pandas.pydata.org/docs/)
-```
 
-This README provides:
-- A **clear problem statement**
-- **Input and output tables**
-- **Multiple SQL solutions with explanations**
-- **A Python Pandas solution**
-- **File structure**
-- **Useful links**
+---
 
-Let me know if you need any modifications! 🚀
\ No newline at end of file
+## Let me know if you need any modifications! 🚀
\ No newline at end of file

From 7d7dbe30e6e7843201b2aa097a2b1e6f2d6c7de0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:10:15 +0530
Subject: [PATCH 284/353] Update readme.md

---
 .../readme.md                                 | 146 ++++++++++++++++++
 1 file changed, 146 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md b/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md
index e69de29..c794a5f 100644
--- a/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md	
+++ b/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md	
@@ -0,0 +1,146 @@
+Here’s a well-structured `README.md` file for **LeetCode 1141 - User Activity for the Past 30 Days I** with SQL and Pandas solutions:
+
+```md
+# 📊 User Activity for the Past 30 Days I - LeetCode 1141
+
+## 📌 Problem Statement
+You are given the **Activity** table that records user activities on a social media website.
+
+### Activity Table
+| Column Name   | Type |
+| ------------- | ---- |
+| user_id       | int  |
+| session_id    | int  |
+| activity_date | date |
+| activity_type | enum |
+
+- The `activity_type` column is an ENUM of **('open_session', 'end_session', 'scroll_down', 'send_message')**.
+- Each session belongs to exactly **one user**.
+- The table **may have duplicate rows**.
+
+### Task:
+Find the **daily active user count** for a period of **30 days ending 2019-07-27 inclusively**.
+- A user is considered **active on a given day** if they made at least **one activity**.
+- Ignore days with **zero active users**.
+
+---
+
+## 📊 Example 1:
+### Input:
+**Activity Table**
+| user_id | session_id | activity_date | activity_type |
+| ------- | ---------- | ------------- | ------------- |
+| 1       | 1          | 2019-07-20    | open_session  |
+| 1       | 1          | 2019-07-20    | scroll_down   |
+| 1       | 1          | 2019-07-20    | end_session   |
+| 2       | 4          | 2019-07-20    | open_session  |
+| 2       | 4          | 2019-07-21    | send_message  |
+| 2       | 4          | 2019-07-21    | end_session   |
+| 3       | 2          | 2019-07-21    | open_session  |
+| 3       | 2          | 2019-07-21    | send_message  |
+| 3       | 2          | 2019-07-21    | end_session   |
+| 4       | 3          | 2019-06-25    | open_session  |
+| 4       | 3          | 2019-06-25    | end_session   |
+
+### Output:
+| day        | active_users |
+| ---------- | ------------ |
+| 2019-07-20 | 2            |
+| 2019-07-21 | 2            |
+
+### Explanation:
+- **2019-07-20**: Users **1 and 2** were active.
+- **2019-07-21**: Users **2 and 3** were active.
+- **Days with zero active users are ignored**.
+
+---
+
+## 🖥 SQL Solutions
+
+### 1️⃣ Standard MySQL Solution
+#### Explanation:
+- **Filter records** for the last **30 days** (ending on `2019-07-27`).
+- Use `COUNT(DISTINCT user_id)` to count **unique active users per day**.
+- Ignore **days with zero active users**.
+
+```sql
+SELECT 
+    activity_date AS day, 
+    COUNT(DISTINCT user_id) AS active_users
+FROM 
+    Activity
+WHERE 
+    DATEDIFF('2019-07-27', activity_date) < 30 
+    AND DATEDIFF('2019-07-27', activity_date) >= 0
+GROUP BY activity_date;
+```
+
+---
+
+### 2️⃣ Alternative Solution Using `BETWEEN`
+#### Explanation:
+- This solution filters the date range using `BETWEEN` instead of `DATEDIFF`.
+
+```sql
+SELECT 
+    activity_date AS day, 
+    COUNT(DISTINCT user_id) AS active_users
+FROM 
+    Activity
+WHERE 
+    activity_date BETWEEN DATE_SUB('2019-07-27', INTERVAL 29 DAY) AND '2019-07-27'
+GROUP BY activity_date;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### Explanation:
+- Filter activity records for the **last 30 days**.
+- **Group by `activity_date`** and count **unique `user_id`s**.
+- **Ignore days with zero active users**.
+
+```python
+import pandas as pd
+
+def daily_active_users(activity: pd.DataFrame) -> pd.DataFrame:
+    # Filter data within the last 30 days (ending on '2019-07-27')
+    filtered = activity[(activity["activity_date"] >= "2019-06-28") & (activity["activity_date"] <= "2019-07-27")]
+
+    # Group by day and count unique users
+    result = filtered.groupby("activity_date")["user_id"].nunique().reset_index()
+
+    # Rename columns
+    result.columns = ["day", "active_users"]
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 User-Activity-Past-30-Days
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_between.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/user-activity-for-the-past-30-days-i/)
+- 📚 [SQL Date Functions](https://www.w3schools.com/sql/sql_dates.asp)
+- 🐍 [Pandas Documentation](https://pandas.pydata.org/docs/)
+```
+
+This README includes:
+- **Clear problem statement**
+- **Input and output tables**
+- **Multiple SQL solutions with explanations**
+- **Pandas solution in Python**
+- **File structure for a clean GitHub repo**
+- **Useful links**
+
+Let me know if you need any changes! 🚀
\ No newline at end of file

From 8617bc402b52cf310b9a2933b1a33ac7d80910aa Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:11:11 +0530
Subject: [PATCH 285/353] Update readme.md

---
 .../readme.md                                       | 13 +------------
 1 file changed, 1 insertion(+), 12 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md b/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md
index c794a5f..fd44f51 100644
--- a/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md	
+++ b/LeetCode SQL 50 Solution/1141. User Activity for the Past 30 Days I/readme.md	
@@ -1,6 +1,4 @@
-Here’s a well-structured `README.md` file for **LeetCode 1141 - User Activity for the Past 30 Days I** with SQL and Pandas solutions:
 
-```md
 # 📊 User Activity for the Past 30 Days I - LeetCode 1141
 
 ## 📌 Problem Statement
@@ -133,14 +131,5 @@ def daily_active_users(activity: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/user-activity-for-the-past-30-days-i/)
 - 📚 [SQL Date Functions](https://www.w3schools.com/sql/sql_dates.asp)
 - 🐍 [Pandas Documentation](https://pandas.pydata.org/docs/)
-```
-
-This README includes:
-- **Clear problem statement**
-- **Input and output tables**
-- **Multiple SQL solutions with explanations**
-- **Pandas solution in Python**
-- **File structure for a clean GitHub repo**
-- **Useful links**
 
-Let me know if you need any changes! 🚀
\ No newline at end of file
+## Let me know if you need any changes! 🚀
\ No newline at end of file

From 209a657bed615cd55221f344a1be86fc9ee56e86 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:16:30 +0530
Subject: [PATCH 286/353] Update readme.md

---
 .../1148. Article Views I/readme.md           | 125 ++++++++++++++++++
 1 file changed, 125 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1148. Article Views I/readme.md b/LeetCode SQL 50 Solution/1148. Article Views I/readme.md
index e69de29..3210efb 100644
--- a/LeetCode SQL 50 Solution/1148. Article Views I/readme.md	
+++ b/LeetCode SQL 50 Solution/1148. Article Views I/readme.md	
@@ -0,0 +1,125 @@
+Here's a well-structured `README.md` file for **LeetCode 1148 - Article Views I** with SQL and Pandas solutions:
+
+```md
+# 📰 Article Views I - LeetCode 1148
+
+## 📌 Problem Statement
+You are given the **Views** table that records article views.
+
+### Views Table
+| Column Name | Type |
+| ----------- | ---- |
+| article_id  | int  |
+| author_id   | int  |
+| viewer_id   | int  |
+| view_date   | date |
+
+- The table **may contain duplicate rows**.
+- Each row indicates that **some viewer viewed an article** written by some author on a specific date.
+- If `author_id = viewer_id`, it means **the author viewed their own article**.
+
+### Task:
+Find **all authors** who have viewed **at least one of their own articles**.
+- **Return the result sorted by `id` in ascending order**.
+
+---
+
+## 📊 Example 1:
+### Input:
+**Views Table**
+| article_id | author_id | viewer_id | view_date  |
+| ---------- | --------- | --------- | ---------- |
+| 1          | 3         | 5         | 2019-08-01 |
+| 1          | 3         | 6         | 2019-08-02 |
+| 2          | 7         | 7         | 2019-08-01 |
+| 2          | 7         | 6         | 2019-08-02 |
+| 4          | 7         | 1         | 2019-07-22 |
+| 3          | 4         | 4         | 2019-07-21 |
+| 3          | 4         | 4         | 2019-07-21 |
+
+### Output:
+| id  |
+| --- |
+| 4   |
+| 7   |
+
+### Explanation:
+- **Author 4** viewed their own article (`viewer_id = author_id`).
+- **Author 7** viewed their own article (`viewer_id = author_id`).
+- The result is sorted in **ascending order**.
+
+---
+
+## 🖥 SQL Solutions
+
+### 1️⃣ Standard MySQL Solution
+#### Explanation:
+- **Filter rows** where `author_id = viewer_id`.
+- Use `DISTINCT` to **remove duplicates**.
+- **Sort the result** in ascending order.
+
+```sql
+SELECT DISTINCT author_id AS id
+FROM Views
+WHERE author_id = viewer_id
+ORDER BY id ASC;
+```
+
+---
+
+### 2️⃣ Alternative Solution Using `GROUP BY`
+#### Explanation:
+- **Group by** `author_id` and **filter authors** who have viewed at least one of their own articles.
+
+```sql
+SELECT author_id AS id
+FROM Views
+WHERE author_id = viewer_id
+GROUP BY author_id
+ORDER BY id ASC;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### Explanation:
+- **Filter rows** where `author_id == viewer_id`.
+- **Select distinct author IDs**.
+- **Sort the result** in ascending order.
+
+```python
+import pandas as pd
+
+def authors_who_viewed_own_articles(views: pd.DataFrame) -> pd.DataFrame:
+    # Filter rows where author_id == viewer_id
+    filtered = views[views["author_id"] == views["viewer_id"]]
+    
+    # Select unique author IDs and sort
+    result = pd.DataFrame({"id": sorted(filtered["author_id"].unique())})
+    
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Article-Views-I
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_group_by.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/article-views-i/)
+- 📚 [SQL DISTINCT vs GROUP BY](https://www.w3schools.com/sql/sql_distinct.asp)
+- 🐍 [Pandas Unique Function](https://pandas.pydata.org/docs/reference/api/pandas.Series.unique.html)
+```
+
+
+
+Let me know if you need any changes! 🚀
\ No newline at end of file

From ff52f318bcce1d847e54a88d99671c0798e00b60 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:17:21 +0530
Subject: [PATCH 287/353] Update readme.md

---
 LeetCode SQL 50 Solution/1148. Article Views I/readme.md | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1148. Article Views I/readme.md b/LeetCode SQL 50 Solution/1148. Article Views I/readme.md
index 3210efb..9e1f1db 100644
--- a/LeetCode SQL 50 Solution/1148. Article Views I/readme.md	
+++ b/LeetCode SQL 50 Solution/1148. Article Views I/readme.md	
@@ -1,6 +1,3 @@
-Here's a well-structured `README.md` file for **LeetCode 1148 - Article Views I** with SQL and Pandas solutions:
-
-```md
 # 📰 Article Views I - LeetCode 1148
 
 ## 📌 Problem Statement
@@ -118,8 +115,5 @@ def authors_who_viewed_own_articles(views: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/article-views-i/)
 - 📚 [SQL DISTINCT vs GROUP BY](https://www.w3schools.com/sql/sql_distinct.asp)
 - 🐍 [Pandas Unique Function](https://pandas.pydata.org/docs/reference/api/pandas.Series.unique.html)
-```
-
-
 
-Let me know if you need any changes! 🚀
\ No newline at end of file
+## Let me know if you need any changes! 🚀
\ No newline at end of file

From 16ba1dee01fa4742e6f0290752de088efd0bf018 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:18:23 +0530
Subject: [PATCH 288/353] Update readme.md

---
 .../1193. Monthly Transactions I/readme.md    | 138 ++++++++++++++++++
 1 file changed, 138 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md b/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md
index e69de29..f674abf 100644
--- a/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md	
+++ b/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md	
@@ -0,0 +1,138 @@
+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! 🚀
\ No newline at end of file

From 4d18303d9378543dcab50f88a8f01e107d950026 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:19:00 +0530
Subject: [PATCH 289/353] Update readme.md

---
 .../1193. Monthly Transactions I/readme.md         | 14 +-------------
 1 file changed, 1 insertion(+), 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md b/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md
index f674abf..c4de0bd 100644
--- a/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md	
+++ b/LeetCode SQL 50 Solution/1193. Monthly Transactions I/readme.md	
@@ -1,6 +1,3 @@
-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
@@ -125,14 +122,5 @@ def monthly_transactions(transactions: pd.DataFrame) -> pd.DataFrame:
 - 📖 [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! 🚀
\ No newline at end of file
+## Let me know if you need any modifications! 🚀
\ No newline at end of file

From 29c7efaf091d3537d23b6b45f9351e825844e203 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:19:57 +0530
Subject: [PATCH 290/353] Update readme.md

---
 .../readme.md                                 | 161 ++++++++++++++++++
 1 file changed, 161 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md
index e69de29..860bcc0 100644
--- a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md	
+++ b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md	
@@ -0,0 +1,161 @@
+Here's a well-structured `README.md` file for **LeetCode 1204 - Last Person to Fit in the Bus**, formatted for a GitHub repository:
+
+```md
+# 🚌 Last Person to Fit in the Bus - LeetCode 1204
+
+## 📌 Problem Statement
+You are given the **Queue** table, which contains information about people waiting for a bus.
+
+### Queue Table
+| Column Name | Type    |
+| ----------- | ------- |
+| person_id   | int     |
+| person_name | varchar |
+| weight      | int     |
+| turn        | int     |
+
+- **person_id** contains unique values.
+- The **turn** column determines the order in which people will board (`turn = 1` means the first person to board).
+- The **bus has a weight limit of 1000 kg**.
+- Only **one person can board at a time**.
+
+### Task:
+Find **the last person** who can board the bus **without exceeding the 1000 kg weight limit**.
+
+---
+
+## 📊 Example 1:
+### Input:
+**Queue Table**
+| person_id | person_name | weight | turn |
+| --------- | ----------- | ------ | ---- |
+| 5         | Alice       | 250    | 1    |
+| 4         | Bob         | 175    | 5    |
+| 3         | Alex        | 350    | 2    |
+| 6         | John Cena   | 400    | 3    |
+| 1         | Winston     | 500    | 6    |
+| 2         | Marie       | 200    | 4    |
+
+### Output:
+| person_name |
+| ----------- |
+| John Cena   |
+
+### Explanation:
+Ordering by `turn`:
+| Turn | ID  | Name      | Weight | Total Weight |
+| ---- | --- | --------- | ------ | ------------ |
+| 1    | 5   | Alice     | 250    | 250          |
+| 2    | 3   | Alex      | 350    | 600          |
+| 3    | 6   | John Cena | 400    | 1000         | ✅ (last person to board) |
+| 4    | 2   | Marie     | 200    | 1200         | ❌ (exceeds limit)        |
+| 5    | 4   | Bob       | 175    | ❌            |
+| 6    | 1   | Winston   | 500    | ❌            |
+
+---
+
+## 🖥 SQL Solution
+
+### 1️⃣ Standard MySQL Solution
+#### Explanation:
+- **Use a self-join** to accumulate the total weight up to each person's turn.
+- **Filter out** people whose cumulative weight exceeds **1000**.
+- **Find the last person** who can board.
+
+```sql
+SELECT a.person_name
+FROM
+    Queue AS a,
+    Queue AS b
+WHERE a.turn >= b.turn
+GROUP BY a.person_id
+HAVING SUM(b.weight) <= 1000
+ORDER BY a.turn DESC
+LIMIT 1;
+```
+
+---
+
+### 📝 Step-by-Step Breakdown:
+
+1️⃣ **Self-Join on the Table**
+```sql
+FROM Queue AS a, Queue AS b
+WHERE a.turn >= b.turn
+```
+- This pairs each row `a` with all rows `b` where `b.turn` is less than or equal to `a.turn`.
+- Allows us to calculate the **cumulative sum of weights** for each person.
+
+2️⃣ **Group by Each Person**
+```sql
+GROUP BY a.person_id
+```
+- Groups all rows by `person_id` so we can perform calculations per person.
+
+3️⃣ **Compute the Cumulative Weight**
+```sql
+HAVING SUM(b.weight) <= 1000
+```
+- Filters out people whose cumulative boarding weight exceeds **1000 kg**.
+
+4️⃣ **Find the Last Person Who Can Board**
+```sql
+ORDER BY a.turn DESC
+LIMIT 1;
+```
+- **Sorts by turn in descending order** so that we find the **last person** who can board.
+- **Limits to 1 row** to return only the last eligible person.
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### Explanation:
+- **Sort by `turn`** to simulate the boarding order.
+- **Compute the cumulative sum** of weights.
+- **Find the last person** whose weight sum **does not exceed 1000**.
+
+```python
+import pandas as pd
+
+def last_person_to_fit(queue: pd.DataFrame) -> pd.DataFrame:
+    # Sort by turn
+    queue = queue.sort_values(by="turn")
+
+    # Compute cumulative weight sum
+    queue["cumulative_weight"] = queue["weight"].cumsum()
+
+    # Filter those who fit on the bus
+    queue = queue[queue["cumulative_weight"] <= 1000]
+
+    # Return the last person to fit
+    return queue.tail(1)[["person_name"]]
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Last-Person-Fit
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/last-person-to-fit-in-the-bus/)
+- 📚 [SQL `GROUP BY` Clause](https://www.w3schools.com/sql/sql_groupby.asp)
+- 🐍 [Pandas cumsum() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cumsum.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! 🚀
\ No newline at end of file

From c3b0106a5e9df4b39e0d66658cbaba62fdc98b62 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:20:16 +0530
Subject: [PATCH 291/353] Update readme.md

---
 .../1204. Last Person to Fit in the Bus/readme.md  | 14 +-------------
 1 file changed, 1 insertion(+), 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md
index 860bcc0..a5a1373 100644
--- a/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md	
+++ b/LeetCode SQL 50 Solution/1204. Last Person to Fit in the Bus/readme.md	
@@ -1,6 +1,3 @@
-Here's a well-structured `README.md` file for **LeetCode 1204 - Last Person to Fit in the Bus**, formatted for a GitHub repository:
-
-```md
 # 🚌 Last Person to Fit in the Bus - LeetCode 1204
 
 ## 📌 Problem Statement
@@ -148,14 +145,5 @@ def last_person_to_fit(queue: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/last-person-to-fit-in-the-bus/)
 - 📚 [SQL `GROUP BY` Clause](https://www.w3schools.com/sql/sql_groupby.asp)
 - 🐍 [Pandas cumsum() Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.cumsum.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! 🚀
\ No newline at end of file
+## Let me know if you need any modifications! 🚀
\ No newline at end of file

From b77c62f18a3f5fbb01235111635467b5f0a3db2d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:22:02 +0530
Subject: [PATCH 292/353] Update readme.md

---
 .../readme.md                                 | 192 ++++++++++++++++++
 1 file changed, 192 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md b/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md
index e69de29..abcfff7 100644
--- a/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md	
+++ b/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md	
@@ -0,0 +1,192 @@
+Here's a structured `README.md` file for **LeetCode 1211 - Queries Quality and Percentage**, formatted for a GitHub repository:
+
+```md
+# 📊 Queries Quality and Percentage - LeetCode 1211
+
+## 📌 Problem Statement
+You are given the **Queries** table, which contains information collected from various queries on a database.
+
+### Queries Table
+| Column Name | Type    |
+| ----------- | ------- |
+| query_name  | varchar |
+| result      | varchar |
+| position    | int     |
+| rating      | int     |
+
+- The **position** column has values from **1 to 500**.
+- The **rating** column has values from **1 to 5**.
+- **Queries with rating < 3 are considered "poor queries".**
+
+### Definitions:
+1️⃣ **Query Quality:**  
+   The **average** of the **ratio** between query rating and its position:
+   \[
+   \text{quality} = \frac{\sum (\text{rating} / \text{position})}{\text{total queries for that name}}
+   \]
+
+2️⃣ **Poor Query Percentage:**  
+   The percentage of all queries where **rating < 3**:
+   \[
+   \text{poor\_query\_percentage} = \left(\frac{\text{count of poor queries}}{\text{total queries}}\right) \times 100
+   \]
+
+---
+
+## 📊 Example 1:
+### Input:
+**Queries Table**
+| query_name | result           | position | rating |
+| ---------- | ---------------- | -------- | ------ |
+| Dog        | Golden Retriever | 1        | 5      |
+| Dog        | German Shepherd  | 2        | 5      |
+| Dog        | Mule             | 200      | 1      |
+| Cat        | Shirazi          | 5        | 2      |
+| Cat        | Siamese          | 3        | 3      |
+| Cat        | Sphynx           | 7        | 4      |
+
+### Output:
+| query_name | quality | poor_query_percentage |
+| ---------- | ------- | --------------------- |
+| Dog        | 2.50    | 33.33                 |
+| Cat        | 0.66    | 33.33                 |
+
+### Explanation:
+#### **Dog**
+- **Quality Calculation:**
+  \[
+  \left( \frac{5}{1} + \frac{5}{2} + \frac{1}{200} \right) \div 3 = 2.50
+  \]
+- **Poor Query Percentage:**
+  - Poor Queries: **1** (Mule, rating = 1)
+  - Total Queries: **3**
+  \[
+  (1 / 3) \times 100 = 33.33\%
+  \]
+
+#### **Cat**
+- **Quality Calculation:**
+  \[
+  \left( \frac{2}{5} + \frac{3}{3} + \frac{4}{7} \right) \div 3 = 0.66
+  \]
+- **Poor Query Percentage:**
+  - Poor Queries: **1** (Shirazi, rating = 2)
+  - Total Queries: **3**
+  \[
+  (1 / 3) \times 100 = 33.33\%
+  \]
+
+---
+
+## 🖥 SQL Solution
+
+### 1️⃣ Standard MySQL Query
+#### Explanation:
+- **Calculate quality** using `AVG(rating / position)`.
+- **Count poor queries** using `SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END)`.
+- **Calculate percentage** using `(COUNT of poor queries / total queries) * 100`.
+
+```sql
+SELECT query_name, 
+    ROUND(AVG(rating * 1.0 / position), 2) AS quality,
+    ROUND(SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS poor_query_percentage
+FROM Queries
+GROUP BY query_name;
+```
+
+---
+
+### 📝 Step-by-Step Breakdown:
+
+1️⃣ **Grouping Queries by `query_name`**
+```sql
+GROUP BY query_name;
+```
+- Ensures calculations are **per query type**.
+
+2️⃣ **Calculating Query Quality**
+```sql
+ROUND(AVG(rating * 1.0 / position), 2) AS quality
+```
+- **`rating / position`** calculates the ratio.
+- **`AVG(...)`** finds the average across all entries for the query.
+- **Multiplying by `1.0` ensures floating-point division.**
+- **`ROUND(..., 2)` rounds to 2 decimal places**.
+
+3️⃣ **Calculating Poor Query Percentage**
+```sql
+ROUND(SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS poor_query_percentage
+```
+- **Counts queries with `rating < 3` using `SUM(CASE WHEN ... THEN 1 ELSE 0 END)`**.
+- **Divides by total queries (`COUNT(*)`) and multiplies by `100`**.
+- **Rounds to 2 decimal places**.
+
+---
+
+### 2️⃣ Alternative MySQL Query (Using `IF` Instead of `CASE`)
+
+```sql
+SELECT query_name,
+    ROUND(AVG(rating * 1.0 / position), 2) AS quality,
+    ROUND(SUM(IF(rating < 3, 1, 0)) * 100.0 / COUNT(*), 2) AS poor_query_percentage  
+FROM Queries
+GROUP BY query_name;
+```
+- **`IF(rating < 3, 1, 0)`** is equivalent to `CASE WHEN rating < 3 THEN 1 ELSE 0 END`.
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### Explanation:
+- **Group by `query_name`**.
+- **Calculate query quality** as `rating / position`, then average.
+- **Filter poor queries (`rating < 3`) and compute percentage**.
+
+```python
+import pandas as pd
+
+def queries_quality(queries: pd.DataFrame) -> pd.DataFrame:
+    # Group by query_name
+    grouped = queries.groupby("query_name")
+
+    # Compute Quality
+    quality = grouped.apply(lambda x: round((x["rating"] / x["position"]).mean(), 2))
+
+    # Compute Poor Query Percentage
+    poor_query_percentage = grouped.apply(lambda x: round((x["rating"] < 3).mean() * 100, 2))
+
+    # Return result
+    result = pd.DataFrame({"query_name": quality.index, 
+                           "quality": quality.values, 
+                           "poor_query_percentage": poor_query_percentage.values})
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Queries-Quality
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/queries-quality-and-percentage/)
+- 📚 [SQL `GROUP BY` Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/groupby.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem statement with table structure**  
+✅ **Examples with detailed calculations**  
+✅ **SQL and Pandas solutions with explanations**  
+✅ **Alternative SQL query for flexibility**  
+✅ **File structure for GitHub organization**  
+✅ **Useful reference links**  
+
+Let me know if you'd like any modifications! 🚀
\ No newline at end of file

From 2b7acdb5856acfddc90c02bef7bddab8aecf6f75 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:22:26 +0530
Subject: [PATCH 293/353] Update readme.md

---
 .../1211. Queries Quality and Percentage/readme.md | 14 +-------------
 1 file changed, 1 insertion(+), 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md b/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md
index abcfff7..0f00160 100644
--- a/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md	
+++ b/LeetCode SQL 50 Solution/1211. Queries Quality and Percentage/readme.md	
@@ -1,6 +1,3 @@
-Here's a structured `README.md` file for **LeetCode 1211 - Queries Quality and Percentage**, formatted for a GitHub repository:
-
-```md
 # 📊 Queries Quality and Percentage - LeetCode 1211
 
 ## 📌 Problem Statement
@@ -179,14 +176,5 @@ def queries_quality(queries: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/queries-quality-and-percentage/)
 - 📚 [SQL `GROUP BY` Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/groupby.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem statement with table structure**  
-✅ **Examples with detailed calculations**  
-✅ **SQL and Pandas solutions with explanations**  
-✅ **Alternative SQL query for flexibility**  
-✅ **File structure for GitHub organization**  
-✅ **Useful reference links**  
 
-Let me know if you'd like any modifications! 🚀
\ No newline at end of file
+## Let me know if you'd like any modifications! 🚀
\ No newline at end of file

From 64c794d23eb3f7613cfa397e6cc3dcf583599ae7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:24:50 +0530
Subject: [PATCH 294/353] Update readme.md

---
 .../1251. Average Selling Price/readme.md     | 229 ++++++++++++++++++
 1 file changed, 229 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md b/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md
index e69de29..5542cc5 100644
--- a/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md	
+++ b/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md	
@@ -0,0 +1,229 @@
+Here's a well-structured `README.md` file for **LeetCode 1251 - Average Selling Price**, formatted for a GitHub repository:
+
+```md
+# 🛒 Average Selling Price - LeetCode 1251
+
+## 📌 Problem Statement
+You are given two tables, **Prices** and **UnitsSold**, which contain information about product pricing and sales.
+
+### 📊 Prices Table
+| Column Name | Type |
+| ----------- | ---- |
+| product_id  | int  |
+| start_date  | date |
+| end_date    | date |
+| price       | int  |
+
+- `(product_id, start_date, end_date)` is the **primary key**.
+- Each row defines the price for `product_id` **within a specific date range**.
+- **No two price periods overlap** for the same product.
+
+### 📊 UnitsSold Table
+| Column Name   | Type |
+| ------------- | ---- |
+| product_id    | int  |
+| purchase_date | date |
+| units         | int  |
+
+- Each row records the number of units sold for `product_id` on `purchase_date`.
+- **Table may contain duplicate rows**.
+
+### 🔢 Goal:
+Find the **average selling price** for each `product_id`, rounded to **2 decimal places**.  
+If a product has **no sales**, its average price should be **0**.
+
+---
+
+## 📊 Example 1:
+### Input:
+### **Prices Table**
+| product_id | start_date | end_date   | price |
+| ---------- | ---------- | ---------- | ----- |
+| 1          | 2019-02-17 | 2019-02-28 | 5     |
+| 1          | 2019-03-01 | 2019-03-22 | 20    |
+| 2          | 2019-02-01 | 2019-02-20 | 15    |
+| 2          | 2019-02-21 | 2019-03-31 | 30    |
+
+### **UnitsSold Table**
+| product_id | purchase_date | units |
+| ---------- | ------------- | ----- |
+| 1          | 2019-02-25    | 100   |
+| 1          | 2019-03-01    | 15    |
+| 2          | 2019-02-10    | 200   |
+| 2          | 2019-03-22    | 30    |
+
+### Output:
+| product_id | average_price |
+| ---------- | ------------- |
+| 1          | 6.96          |
+| 2          | 16.96         |
+
+---
+
+## 🔍 Explanation:
+### **Formula**
+\[
+\text{Average Selling Price} = \frac{\sum (\text{price} \times \text{units sold})}{\sum (\text{units sold})}
+\]
+
+### **Product 1 Calculation**
+- **Feb 25, 2019:** 100 units sold at **$5**
+- **Mar 01, 2019:** 15 units sold at **$20**
+- **Total Price Contribution:**  
+  \[
+  (100 \times 5) + (15 \times 20) = 500 + 300 = 800
+  \]
+- **Total Units Sold:**  
+  \[
+  100 + 15 = 115
+  \]
+- **Average Price:**  
+  \[
+  800 / 115 = 6.96
+  \]
+
+### **Product 2 Calculation**
+- **Feb 10, 2019:** 200 units sold at **$15**
+- **Mar 22, 2019:** 30 units sold at **$30**
+- **Total Price Contribution:**  
+  \[
+  (200 \times 15) + (30 \times 30) = 3000 + 900 = 3900
+  \]
+- **Total Units Sold:**  
+  \[
+  200 + 30 = 230
+  \]
+- **Average Price:**  
+  \[
+  3900 / 230 = 16.96
+  \]
+
+---
+
+## 🖥 SQL Solution
+
+### 1️⃣ Standard MySQL Query
+#### **Explanation:**
+- **Join `Prices` and `UnitsSold`** on `product_id`, ensuring `purchase_date` falls **within the valid price period** (`start_date` ≤ `purchase_date` ≤ `end_date`).
+- **Multiply `price * units`** for total revenue.
+- **Sum total units** for each product.
+- **Use `ROUND(..., 2)`** to get 2 decimal places.
+- **Use `IFNULL(..., 0)`** to handle cases where no units were sold.
+
+```sql
+SELECT p.product_id, 
+       IFNULL(ROUND(SUM(p.price * u.units) / SUM(u.units), 2), 0) AS average_price
+FROM Prices p
+LEFT JOIN UnitsSold u
+ON p.product_id = u.product_id 
+AND u.purchase_date BETWEEN p.start_date AND p.end_date
+GROUP BY p.product_id;
+```
+
+---
+
+### 📝 Step-by-Step Breakdown:
+
+1️⃣ **Join Tables Based on Matching Date Ranges**
+```sql
+LEFT JOIN UnitsSold u
+ON p.product_id = u.product_id 
+AND u.purchase_date BETWEEN p.start_date AND p.end_date
+```
+- Ensures we only match **valid** sales based on pricing periods.
+
+2️⃣ **Calculate Revenue Per Product**
+```sql
+SUM(p.price * u.units)
+```
+- Computes total revenue for each product.
+
+3️⃣ **Compute Total Sold Units Per Product**
+```sql
+SUM(u.units)
+```
+- Sums up all sold units.
+
+4️⃣ **Calculate Average Price and Handle Edge Cases**
+```sql
+ROUND(SUM(p.price * u.units) / SUM(u.units), 2)
+```
+- Ensures precision with 2 decimal places.
+
+5️⃣ **Handle Products with No Sales**
+```sql
+IFNULL(..., 0)
+```
+- If `SUM(u.units)` is `NULL`, return `0`.
+
+---
+
+### 2️⃣ Alternative MySQL Query (Using `COALESCE`)
+```sql
+SELECT p.product_id, 
+       ROUND(SUM(COALESCE(p.price, 0) * COALESCE(u.units, 0)) / SUM(COALESCE(u.units, 0)), 2) AS average_price
+FROM Prices p
+LEFT JOIN UnitsSold u
+ON p.product_id = u.product_id 
+AND u.purchase_date BETWEEN p.start_date AND p.end_date
+GROUP BY p.product_id;
+```
+- Uses **`COALESCE(value, 0)`** instead of `IFNULL()` for robustness.
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+- **Merge DataFrames on `product_id`** where `purchase_date` falls in the price range.
+- **Compute total price & units**.
+- **Handle cases where no units were sold**.
+
+```python
+import pandas as pd
+
+def average_selling_price(prices: pd.DataFrame, units_sold: pd.DataFrame) -> pd.DataFrame:
+    # Merge on product_id where purchase_date is within the valid price period
+    merged = prices.merge(units_sold, on="product_id")
+    merged = merged[(merged["purchase_date"] >= merged["start_date"]) & 
+                    (merged["purchase_date"] <= merged["end_date"])]
+
+    # Calculate total revenue and total units per product
+    merged["total_price"] = merged["price"] * merged["units"]
+    result = merged.groupby("product_id").agg(
+        average_price=("total_price", lambda x: round(x.sum() / merged.loc[x.index, "units"].sum(), 2))
+    ).reset_index()
+
+    # Handle products with no sales
+    result["average_price"] = result["average_price"].fillna(0)
+
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Average-Selling-Price
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/average-selling-price/)
+- 📚 [SQL `LEFT JOIN` Documentation](https://www.w3schools.com/sql/sql_join_left.asp)
+- 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem statement with table structure**  
+✅ **Examples with detailed calculations**  
+✅ **SQL and Pandas solutions with explanations**  
+✅ **Alternative SQL query for flexibility**  
+✅ **File structure for GitHub organization**  
+✅ **Useful reference links**  
+
+Would you like any refinements? 🚀
\ No newline at end of file

From a37cf6b41a058c359429bf2f6568e37e93072164 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:25:54 +0530
Subject: [PATCH 295/353] Update readme.md

---
 .../1251. Average Selling Price/readme.md           | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md b/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md
index 5542cc5..d099985 100644
--- a/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md	
+++ b/LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md	
@@ -1,6 +1,4 @@
-Here's a well-structured `README.md` file for **LeetCode 1251 - Average Selling Price**, formatted for a GitHub repository:
 
-```md
 # 🛒 Average Selling Price - LeetCode 1251
 
 ## 📌 Problem Statement
@@ -216,14 +214,3 @@ def average_selling_price(prices: pd.DataFrame, units_sold: pd.DataFrame) -> pd.
 - 📖 [LeetCode Problem](https://leetcode.com/problems/average-selling-price/)
 - 📚 [SQL `LEFT JOIN` Documentation](https://www.w3schools.com/sql/sql_join_left.asp)
 - 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem statement with table structure**  
-✅ **Examples with detailed calculations**  
-✅ **SQL and Pandas solutions with explanations**  
-✅ **Alternative SQL query for flexibility**  
-✅ **File structure for GitHub organization**  
-✅ **Useful reference links**  
-
-Would you like any refinements? 🚀
\ No newline at end of file

From d98712141aa3c98018d4e4d0a66ead9d96e09f8a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:27:33 +0530
Subject: [PATCH 296/353] Update readme.md

---
 .../1045. Customers Who Bought All Products/readme.md        | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md
index 2d3bb82..cedd5e5 100644
--- a/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md	
+++ b/LeetCode SQL 50 Solution/1045. Customers Who Bought All Products/readme.md	
@@ -1,6 +1,3 @@
-Here’s the updated README.md, including **Pandas and Window SQL solutions** along with the original **MySQL solution**.
-
----
 
 # **1045. Customers Who Bought All Products**
 
@@ -178,4 +175,4 @@ LeetCode1045/
 
 ---ture & Useful Links**  
 
-🚀 **Now it's a complete guide!** 🚀
\ No newline at end of file
+🚀 **Now it's a complete guide!** 🚀

From b03b944c2b3b4eca807bda1fe1804f309d2a1491 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:30:42 +0530
Subject: [PATCH 297/353] Update readme.md

---
 .../1280. Students and Examinations/readme.md | 192 ++++++++++++++++++
 1 file changed, 192 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md b/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md
index e69de29..f1279a1 100644
--- a/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md	
+++ b/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md	
@@ -0,0 +1,192 @@
+Here is the well-structured `README.md` file for **LeetCode 1204 - Find the Number of Times Each Student Attended Each Exam**, formatted for a GitHub repository:
+
+```md
+# 🎓 Find the Number of Times Each Student Attended Each Exam - LeetCode 1204
+
+## 📌 Problem Statement
+You are given three tables: **Students**, **Subjects**, and **Examinations**, which contain information about students, subjects, and exam attendance.
+
+### 📊 Students Table
+| Column Name  | Type    |
+| ------------ | ------- |
+| student_id   | int     |
+| student_name | varchar |
+- `student_id` is the **primary key**.
+- Each row represents a **unique student**.
+
+### 📊 Subjects Table
+| Column Name  | Type    |
+| ------------ | ------- |
+| subject_name | varchar |
+- `subject_name` is the **primary key**.
+- Each row represents a **unique subject**.
+
+### 📊 Examinations Table
+| Column Name  | Type    |
+| ------------ | ------- |
+| student_id   | int     |
+| subject_name | varchar |
+- **No primary key** (may contain duplicates).
+- Each row indicates that a student attended an exam for a specific subject.
+
+### 🔢 Goal:
+Find the **number of times each student attended each exam**.  
+If a student did **not attend an exam**, return `0`.  
+Return the results **ordered by** `student_id` and `subject_name`.
+
+---
+
+## 📊 Example 1:
+### Input:
+
+### **Students Table**
+| student_id | student_name |
+| ---------- | ------------ |
+| 1          | Alice        |
+| 2          | Bob          |
+| 13         | John         |
+| 6          | Alex         |
+
+### **Subjects Table**
+| subject_name |
+| ------------ |
+| Math         |
+| Physics      |
+| Programming  |
+
+### **Examinations Table**
+| student_id | subject_name |
+| ---------- | ------------ |
+| 1          | Math         |
+| 1          | Physics      |
+| 1          | Programming  |
+| 2          | Programming  |
+| 1          | Physics      |
+| 1          | Math         |
+| 13         | Math         |
+| 13         | Programming  |
+| 13         | Physics      |
+| 2          | Math         |
+| 1          | Math         |
+
+### Output:
+| student_id | student_name | subject_name | attended_exams |
+| ---------- | ------------ | ------------ | -------------- |
+| 1          | Alice        | Math         | 3              |
+| 1          | Alice        | Physics      | 2              |
+| 1          | Alice        | Programming  | 1              |
+| 2          | Bob          | Math         | 1              |
+| 2          | Bob          | Physics      | 0              |
+| 2          | Bob          | Programming  | 1              |
+| 6          | Alex         | Math         | 0              |
+| 6          | Alex         | Physics      | 0              |
+| 6          | Alex         | Programming  | 0              |
+| 13         | John         | Math         | 1              |
+| 13         | John         | Physics      | 1              |
+| 13         | John         | Programming  | 1              |
+
+---
+
+## 🖥 SQL Solution
+
+### 1️⃣ Standard MySQL Query
+#### **Explanation:**
+- **Use `CROSS JOIN`** to generate **all possible student-subject combinations**.
+- **Use `LEFT JOIN`** to attach attendance records from `Examinations`.
+- **Use `COUNT(e.subject_name)`** to count how many times a student attended an exam.
+- **Sort results by** `student_id` and `subject_name`.
+
+```sql
+SELECT s.student_id, s.student_name, sb.subject_name, 
+       COUNT(e.subject_name) AS attended_exams
+FROM Students s
+CROSS JOIN Subjects sb
+LEFT JOIN Examinations e
+ON s.student_id = e.student_id AND sb.subject_name = e.subject_name
+GROUP BY s.student_id, sb.subject_name
+ORDER BY s.student_id, sb.subject_name;
+```
+
+---
+
+### 2️⃣ Alternative SQL Query (Using `COALESCE`)
+```sql
+SELECT s.student_id, s.student_name, sb.subject_name, 
+       COALESCE(COUNT(e.subject_name), 0) AS attended_exams
+FROM Students s
+CROSS JOIN Subjects sb
+LEFT JOIN Examinations e
+ON s.student_id = e.student_id AND sb.subject_name = e.subject_name
+GROUP BY s.student_id, sb.subject_name
+ORDER BY s.student_id, sb.subject_name;
+```
+- **Uses `COALESCE(COUNT(...), 0)`** to explicitly handle `NULL` values.
+
+---
+
+### 3️⃣ Alternative SQL Query (Using `WITH ROLLUP`)
+```sql
+SELECT s.student_id, s.student_name, sb.subject_name, 
+       COUNT(e.subject_name) AS attended_exams
+FROM Students s
+CROSS JOIN Subjects sb
+LEFT JOIN Examinations e
+ON s.student_id = e.student_id AND sb.subject_name = e.subject_name
+GROUP BY s.student_id, sb.subject_name WITH ROLLUP
+HAVING GROUPING(subject_name) = 0
+ORDER BY s.student_id, sb.subject_name;
+```
+- **Uses `WITH ROLLUP`** to generate **aggregated results**.
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+- **Generate all possible (student, subject) pairs** using `pd.merge()`.
+- **Group by student_id and subject_name**, then count occurrences.
+- **Fill missing values with `0`**.
+
+```python
+import pandas as pd
+
+def student_exam_attendance(students: pd.DataFrame, subjects: pd.DataFrame, exams: pd.DataFrame) -> pd.DataFrame:
+    # Create all possible student-subject combinations
+    all_combinations = students.merge(subjects, how="cross")
+
+    # Merge with exam data
+    merged = all_combinations.merge(exams, on=["student_id", "subject_name"], how="left")
+
+    # Count the number of times each student attended each exam
+    result = merged.groupby(["student_id", "student_name", "subject_name"]).size().reset_index(name="attended_exams")
+
+    return result.sort_values(["student_id", "subject_name"])
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Student-Exam-Attendance
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/find-the-number-of-times-each-student-attended-each-exam/)
+- 📚 [SQL `CROSS JOIN` Documentation](https://www.w3schools.com/sql/sql_join_cross.asp)
+- 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem statement with table structure**  
+✅ **Examples with detailed calculations**  
+✅ **SQL and Pandas solutions with explanations**  
+✅ **Alternative SQL query for flexibility**  
+✅ **File structure for GitHub organization**  
+✅ **Useful reference links**  
+
+Would you like any refinements? 🚀
\ No newline at end of file

From 97822743ba703905e0fc8c8ca711fa875c69e999 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:30:58 +0530
Subject: [PATCH 298/353] Update readme.md

---
 .../1280. Students and Examinations/readme.md      | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md b/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md
index f1279a1..56e7bf0 100644
--- a/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md	
+++ b/LeetCode SQL 50 Solution/1280. Students and Examinations/readme.md	
@@ -1,6 +1,3 @@
-Here is the well-structured `README.md` file for **LeetCode 1204 - Find the Number of Times Each Student Attended Each Exam**, formatted for a GitHub repository:
-
-```md
 # 🎓 Find the Number of Times Each Student Attended Each Exam - LeetCode 1204
 
 ## 📌 Problem Statement
@@ -179,14 +176,3 @@ def student_exam_attendance(students: pd.DataFrame, subjects: pd.DataFrame, exam
 - 📖 [LeetCode Problem](https://leetcode.com/problems/find-the-number-of-times-each-student-attended-each-exam/)
 - 📚 [SQL `CROSS JOIN` Documentation](https://www.w3schools.com/sql/sql_join_cross.asp)
 - 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem statement with table structure**  
-✅ **Examples with detailed calculations**  
-✅ **SQL and Pandas solutions with explanations**  
-✅ **Alternative SQL query for flexibility**  
-✅ **File structure for GitHub organization**  
-✅ **Useful reference links**  
-
-Would you like any refinements? 🚀
\ No newline at end of file

From f8a519a8520dcef895f9fcd0f7f702228cfdfe49 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:31:26 +0530
Subject: [PATCH 299/353] Update readme.md

---
 .../1321. Restaurant Growth/readme.md         | 185 ++++++++++++++++++
 1 file changed, 185 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md b/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md
index e69de29..96e7d5f 100644
--- a/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md	
+++ b/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md	
@@ -0,0 +1,185 @@
+Here's a well-structured `README.md` for **LeetCode 321 - Restaurant Growth**, formatted for a GitHub repository:  
+
+```md
+# 🍽️ Restaurant Growth - LeetCode 321
+
+## 📌 Problem Statement
+You are given a table **Customer**, which records daily customer transactions in a restaurant.  
+The restaurant owner wants to analyze a **7-day moving average** of customer spending.  
+
+### 📊 Customer Table
+| Column Name | Type    |
+| ----------- | ------- |
+| customer_id | int     |
+| name        | varchar |
+| visited_on  | date    |
+| amount      | int     |
+- **(customer_id, visited_on) is the primary key**.
+- `visited_on` represents the date a customer visited the restaurant.
+- `amount` represents the total amount paid by a customer on that day.
+
+---
+
+## 🔢 Goal:
+Compute the **7-day moving average** of customer spending.  
+- The window consists of **current day + 6 days before**.
+- `average_amount` should be **rounded to 2 decimal places**.
+- The result should be **ordered by `visited_on` in ascending order**.
+
+---
+
+## 📊 Example 1:
+### **Input:**
+#### **Customer Table**
+| customer_id | name    | visited_on | amount |
+| ----------- | ------- | ---------- | ------ |
+| 1           | Jhon    | 2019-01-01 | 100    |
+| 2           | Daniel  | 2019-01-02 | 110    |
+| 3           | Jade    | 2019-01-03 | 120    |
+| 4           | Khaled  | 2019-01-04 | 130    |
+| 5           | Winston | 2019-01-05 | 110    |
+| 6           | Elvis   | 2019-01-06 | 140    |
+| 7           | Anna    | 2019-01-07 | 150    |
+| 8           | Maria   | 2019-01-08 | 80     |
+| 9           | Jaze    | 2019-01-09 | 110    |
+| 1           | Jhon    | 2019-01-10 | 130    |
+| 3           | Jade    | 2019-01-10 | 150    |
+
+### **Output:**
+| visited_on | amount | average_amount |
+| ---------- | ------ | -------------- |
+| 2019-01-07 | 860    | 122.86         |
+| 2019-01-08 | 840    | 120            |
+| 2019-01-09 | 840    | 120            |
+| 2019-01-10 | 1000   | 142.86         |
+
+### **Explanation:**
+1. **First moving average (2019-01-01 to 2019-01-07)**  
+   \[
+   (100 + 110 + 120 + 130 + 110 + 140 + 150) / 7 = 122.86
+   \]
+2. **Second moving average (2019-01-02 to 2019-01-08)**  
+   \[
+   (110 + 120 + 130 + 110 + 140 + 150 + 80) / 7 = 120
+   \]
+3. **Third moving average (2019-01-03 to 2019-01-09)**  
+   \[
+   (120 + 130 + 110 + 140 + 150 + 80 + 110) / 7 = 120
+   \]
+4. **Fourth moving average (2019-01-04 to 2019-01-10)**  
+   \[
+   (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150) / 7 = 142.86
+   \]
+
+---
+
+## 🖥 SQL Solutions
+
+### 1️⃣ **Using `WINDOW FUNCTION` (`SUM() OVER` + `RANK() OVER`)**
+#### **Explanation:**
+- First, **group transactions per day** using `SUM(amount)`.
+- Then, use `SUM() OVER (ROWS 6 PRECEDING)` to calculate **moving sum** over 7 days.
+- Use `RANK()` to track row numbers and filter rows with `rk > 6`.
+- Finally, compute `ROUND(amount / 7, 2)`.
+
+```sql
+WITH t AS (
+    SELECT 
+        visited_on, 
+        SUM(amount) OVER (
+            ORDER BY visited_on 
+            ROWS 6 PRECEDING
+        ) AS amount,
+        RANK() OVER (
+            ORDER BY visited_on 
+            ROWS 6 PRECEDING
+        ) AS rk
+    FROM (
+        SELECT visited_on, SUM(amount) AS amount
+        FROM Customer
+        GROUP BY visited_on
+    ) AS tt
+)
+SELECT visited_on, amount, ROUND(amount / 7, 2) AS average_amount
+FROM t
+WHERE rk > 6;
+```
+
+---
+
+### 2️⃣ **Using `JOIN` + `DATEDIFF()`**
+#### **Explanation:**
+- Use a **self-join** to find transactions **within a 7-day range**.
+- Sum the `amount` for each window and calculate the moving average.
+- Use `DATEDIFF(a.visited_on, b.visited_on) BETWEEN 0 AND 6` to filter records.
+- Ensure only complete 7-day windows are included.
+
+```sql
+SELECT 
+    a.visited_on,
+    SUM(b.amount) AS amount,
+    ROUND(SUM(b.amount) / 7, 2) AS average_amount
+FROM 
+    (SELECT DISTINCT visited_on FROM customer) AS a
+    JOIN customer AS b 
+    ON DATEDIFF(a.visited_on, b.visited_on) BETWEEN 0 AND 6
+WHERE 
+    a.visited_on >= (SELECT MIN(visited_on) FROM customer) + 6
+GROUP BY a.visited_on
+ORDER BY a.visited_on;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+- **Group by `visited_on`** and sum `amount` per day.
+- **Use `.rolling(7).sum()`** to compute the moving sum over 7 days.
+- **Drop NaN values** to exclude incomplete windows.
+- **Round the average to 2 decimal places**.
+
+```python
+import pandas as pd
+
+def restaurant_growth(customers: pd.DataFrame) -> pd.DataFrame:
+    # Aggregate daily amounts
+    daily_amount = customers.groupby("visited_on")["amount"].sum().reset_index()
+
+    # Compute rolling 7-day sum and moving average
+    daily_amount["amount"] = daily_amount["amount"].rolling(7).sum()
+    daily_amount["average_amount"] = (daily_amount["amount"] / 7).round(2)
+
+    # Drop incomplete windows
+    daily_amount = daily_amount.dropna().reset_index(drop=True)
+
+    return daily_amount
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Restaurant-Growth
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/restaurant-growth/)
+- 📚 [SQL `WINDOW FUNCTIONS` Documentation](https://www.w3schools.com/sql/sql_window.asp)
+- 🐍 [Pandas Rolling Window](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem statement with table structure**  
+✅ **Examples with detailed calculations**  
+✅ **SQL and Pandas solutions with explanations**  
+✅ **Alternative SQL query for flexibility**  
+✅ **File structure for GitHub organization**  
+✅ **Useful reference links**  
+
+Would you like any refinements? 🚀
\ No newline at end of file

From 29c2563985e5af473dbd0d43c32c1629c5568872 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:31:38 +0530
Subject: [PATCH 300/353] Update readme.md

---
 .../1321. Restaurant Growth/readme.md              | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md b/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md
index 96e7d5f..2c666c1 100644
--- a/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md	
+++ b/LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md	
@@ -1,6 +1,3 @@
-Here's a well-structured `README.md` for **LeetCode 321 - Restaurant Growth**, formatted for a GitHub repository:  
-
-```md
 # 🍽️ Restaurant Growth - LeetCode 321
 
 ## 📌 Problem Statement
@@ -172,14 +169,3 @@ def restaurant_growth(customers: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/restaurant-growth/)
 - 📚 [SQL `WINDOW FUNCTIONS` Documentation](https://www.w3schools.com/sql/sql_window.asp)
 - 🐍 [Pandas Rolling Window](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem statement with table structure**  
-✅ **Examples with detailed calculations**  
-✅ **SQL and Pandas solutions with explanations**  
-✅ **Alternative SQL query for flexibility**  
-✅ **File structure for GitHub organization**  
-✅ **Useful reference links**  
-
-Would you like any refinements? 🚀
\ No newline at end of file

From 2037dbd56147771711b891506a705f4a56b40148 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:32:42 +0530
Subject: [PATCH 301/353] Update readme.md

---
 .../readme.md                                 | 170 ++++++++++++++++++
 1 file changed, 170 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md
index e69de29..eb329ee 100644
--- a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md	
+++ b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md	
@@ -0,0 +1,170 @@
+Here’s a well-structured `README.md` for **LeetCode 1327 - List the Products Ordered in a Period**, formatted for a GitHub repository:  
+
+```md
+# 🛒 List the Products Ordered in a Period - LeetCode 1327
+
+## 📌 Problem Statement
+You are given two tables: **Products** and **Orders**.  
+Your task is to **list the product names** that had at least **100 units ordered in February 2020** along with the total amount ordered.
+
+---
+
+## 📊 Table Structure
+
+### **Products Table**
+| Column Name      | Type    |
+| ---------------- | ------- |
+| product_id       | int     |
+| product_name     | varchar |
+| product_category | varchar |
+
+- `product_id` is the **primary key** (unique identifier).
+- This table contains details about products.
+
+---
+
+### **Orders Table**
+| Column Name | Type |
+| ----------- | ---- |
+| product_id  | int  |
+| order_date  | date |
+| unit        | int  |
+
+- `product_id` is a **foreign key** referencing the `Products` table.
+- `order_date` represents when the order was placed.
+- `unit` represents the **number of products ordered** on that date.
+- The table **may contain duplicate rows**.
+
+---
+
+## 🔢 Goal:
+Find all products that had **at least 100 units ordered** during **February 2020** and display:
+- `product_name`
+- Total `unit` ordered in that period
+
+---
+
+## 📊 Example 1:
+### **Input:**
+#### **Products Table**
+| product_id | product_name          | product_category |
+| ---------- | --------------------- | ---------------- |
+| 1          | Leetcode Solutions    | Book             |
+| 2          | Jewels of Stringology | Book             |
+| 3          | HP                    | Laptop           |
+| 4          | Lenovo                | Laptop           |
+| 5          | Leetcode Kit          | T-shirt          |
+
+#### **Orders Table**
+| product_id | order_date | unit |
+| ---------- | ---------- | ---- |
+| 1          | 2020-02-05 | 60   |
+| 1          | 2020-02-10 | 70   |
+| 2          | 2020-01-18 | 30   |
+| 2          | 2020-02-11 | 80   |
+| 3          | 2020-02-17 | 2    |
+| 3          | 2020-02-24 | 3    |
+| 4          | 2020-03-01 | 20   |
+| 4          | 2020-03-04 | 30   |
+| 4          | 2020-03-04 | 60   |
+| 5          | 2020-02-25 | 50   |
+| 5          | 2020-02-27 | 50   |
+| 5          | 2020-03-01 | 50   |
+
+### **Output:**
+| product_name       | unit |
+| ------------------ | ---- |
+| Leetcode Solutions | 130  |
+| Leetcode Kit       | 100  |
+
+### **Explanation:**
+- **Leetcode Solutions** (ID=1) was ordered in February:  
+  \[
+  60 + 70 = 130 \quad (\text{✓ included})
+  \]
+- **Jewels of Stringology** (ID=2) was ordered **only 80** times in February. (**✗ not included**)
+- **HP Laptop** (ID=3) was ordered **5 times** in February. (**✗ not included**)
+- **Lenovo Laptop** (ID=4) was **not ordered** in February. (**✗ not included**)
+- **Leetcode Kit** (ID=5) was ordered **100 times** in February. (**✓ included**)
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Using `JOIN` + `GROUP BY` + `HAVING`**
+#### **Explanation:**
+1. **Join** the `Products` and `Orders` tables on `product_id`.
+2. **Filter orders** placed in **February 2020** (`BETWEEN '2020-02-01' AND '2020-02-29'`).
+3. **Sum up the `unit` ordered** for each product.
+4. **Use `HAVING` to filter products with at least 100 units.**
+5. Return results in **any order**.
+
+```sql
+SELECT P.PRODUCT_NAME, SUM(O.UNIT) AS UNIT  
+FROM PRODUCTS P  
+INNER JOIN ORDERS O  
+ON P.PRODUCT_ID = O.PRODUCT_ID  
+WHERE O.ORDER_DATE BETWEEN '2020-02-01' AND '2020-02-29'  
+GROUP BY P.PRODUCT_NAME  
+HAVING SUM(O.UNIT) >= 100;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+1. **Merge** `products` and `orders` on `product_id`.
+2. **Filter only February 2020 orders**.
+3. **Group by `product_name`** and **sum `unit`**.
+4. **Filter products with at least 100 units**.
+5. **Return the final DataFrame**.
+
+```python
+import pandas as pd
+
+def products_ordered(products: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame:
+    # Merge both tables on product_id
+    merged_df = pd.merge(orders, products, on="product_id", how="inner")
+
+    # Convert order_date to datetime format and filter February 2020
+    merged_df["order_date"] = pd.to_datetime(merged_df["order_date"])
+    feb_orders = merged_df[
+        (merged_df["order_date"] >= "2020-02-01") & (merged_df["order_date"] <= "2020-02-29")
+    ]
+
+    # Group by product_name and sum the units
+    result = feb_orders.groupby("product_name")["unit"].sum().reset_index()
+
+    # Filter products with at least 100 units
+    result = result[result["unit"] >= 100]
+
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 List-Products-Ordered
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/list-the-products-ordered-in-a-period/)
+- 📚 [SQL `HAVING` Clause](https://www.w3schools.com/sql/sql_having.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+```
+
+### Features of this `README.md`:
+✅ **Detailed problem statement with tables**  
+✅ **Example with step-by-step calculations**  
+✅ **SQL and Pandas solutions with explanations**  
+✅ **File structure for easy organization**  
+✅ **Helpful references for further reading**  
+
+Would you like any modifications? 🚀 
\ No newline at end of file

From c64843c9072914747f5301fb44f0b7f3598e3085 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:32:55 +0530
Subject: [PATCH 302/353] Update readme.md

---
 .../readme.md                                       | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md
index eb329ee..7ab2066 100644
--- a/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md	
+++ b/LeetCode SQL 50 Solution/1327. List the Products Ordered in a Period/readme.md	
@@ -1,6 +1,3 @@
-Here’s a well-structured `README.md` for **LeetCode 1327 - List the Products Ordered in a Period**, formatted for a GitHub repository:  
-
-```md
 # 🛒 List the Products Ordered in a Period - LeetCode 1327
 
 ## 📌 Problem Statement
@@ -158,13 +155,3 @@ def products_ordered(products: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFra
 - 📖 [LeetCode Problem](https://leetcode.com/problems/list-the-products-ordered-in-a-period/)
 - 📚 [SQL `HAVING` Clause](https://www.w3schools.com/sql/sql_having.asp)
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-```
-
-### Features of this `README.md`:
-✅ **Detailed problem statement with tables**  
-✅ **Example with step-by-step calculations**  
-✅ **SQL and Pandas solutions with explanations**  
-✅ **File structure for easy organization**  
-✅ **Helpful references for further reading**  
-
-Would you like any modifications? 🚀 
\ No newline at end of file

From b6ddb0facada7ba242a3bf23e13da21501bb2ec0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:33:23 +0530
Subject: [PATCH 303/353] Update readme.md

---
 .../1341. Movie Rating/readme.md              | 208 ++++++++++++++++++
 1 file changed, 208 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md b/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md
index e69de29..40a5226 100644
--- a/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md	
+++ b/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md	
@@ -0,0 +1,208 @@
+Here's a well-structured `README.md` for **LeetCode 1341 - Movie Rating**, formatted for a GitHub repository:  
+
+```md
+# 🎬 Movie Rating - LeetCode 1341
+
+## 📌 Problem Statement
+You are given three tables: **Movies**, **Users**, and **MovieRating**.  
+
+Your task is to:
+1. Find the **user who has rated the greatest number of movies**.  
+   - In case of a tie, return the **lexicographically smaller** name.  
+2. Find the **movie with the highest average rating** in **February 2020**.  
+   - In case of a tie, return the **lexicographically smaller** movie title.
+
+---
+
+## 📊 Table Structure
+
+### **Movies Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| movie_id    | int     |
+| title       | varchar |
+
+- `movie_id` is the **primary key** (unique identifier).
+- `title` is the **name of the movie**.
+
+---
+
+### **Users Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| user_id     | int     |
+| name        | varchar |
+
+- `user_id` is the **primary key** (unique identifier).
+- `name` is **unique** for each user.
+
+---
+
+### **MovieRating Table**
+| Column Name | Type |
+| ----------- | ---- |
+| movie_id    | int  |
+| user_id     | int  |
+| rating      | int  |
+| created_at  | date |
+
+- `(movie_id, user_id)` is the **primary key** (ensuring unique user-movie ratings).
+- `created_at` represents the **review date**.
+
+---
+
+## 🔢 Goal:
+- Return a **single-column result** containing:
+  1. **User name** with the most ratings.
+  2. **Movie title** with the highest **average rating** in **February 2020**.
+
+---
+
+## 📊 Example 1:
+### **Input:**
+#### **Movies Table**
+| movie_id | title    |
+| -------- | -------- |
+| 1        | Avengers |
+| 2        | Frozen 2 |
+| 3        | Joker    |
+
+#### **Users Table**
+| user_id | name   |
+| ------- | ------ |
+| 1       | Daniel |
+| 2       | Monica |
+| 3       | Maria  |
+| 4       | James  |
+
+#### **MovieRating Table**
+| movie_id | user_id | rating | created_at |
+| -------- | ------- | ------ | ---------- |
+| 1        | 1       | 3      | 2020-01-12 |
+| 1        | 2       | 4      | 2020-02-11 |
+| 1        | 3       | 2      | 2020-02-12 |
+| 1        | 4       | 1      | 2020-01-01 |
+| 2        | 1       | 5      | 2020-02-17 |
+| 2        | 2       | 2      | 2020-02-01 |
+| 2        | 3       | 2      | 2020-03-01 |
+| 3        | 1       | 3      | 2020-02-22 |
+| 3        | 2       | 4      | 2020-02-25 |
+
+### **Output:**
+| results  |
+| -------- |
+| Daniel   |
+| Frozen 2 |
+
+### **Explanation:**
+- **Most Active User:**  
+  - `Daniel` and `Monica` both rated **3 movies**.
+  - Since `Daniel` is **lexicographically smaller**, he is chosen.
+
+- **Highest Average Movie Rating in February 2020:**
+  - **Frozen 2**: `(5 + 2) / 2 = 3.5`
+  - **Joker**: `(3 + 4) / 2 = 3.5`
+  - Since **Frozen 2** is **lexicographically smaller**, it is chosen.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Using `JOIN` + `GROUP BY` + `HAVING`**
+#### **Explanation:**
+1. **Find the most active user:**
+   - Count the number of ratings per user.
+   - Use `ORDER BY COUNT(*) DESC, name` to get the **user with the most ratings**, breaking ties lexicographically.
+   - Limit the result to **1 user**.
+
+2. **Find the highest-rated movie in February 2020:**
+   - Filter rows where `created_at` is **in February 2020**.
+   - **Calculate the average rating per movie**.
+   - Use `ORDER BY AVG(rating) DESC, title` to get the **highest-rated movie**, breaking ties lexicographically.
+   - Limit the result to **1 movie**.
+
+```sql
+(
+    SELECT name AS results
+    FROM
+        Users
+        JOIN MovieRating USING (user_id)
+    GROUP BY user_id
+    ORDER BY COUNT(1) DESC, name
+    LIMIT 1
+)
+UNION ALL
+(
+    SELECT title
+    FROM
+        MovieRating
+        JOIN Movies USING (movie_id)
+    WHERE DATE_FORMAT(created_at, '%Y-%m') = '2020-02'
+    GROUP BY movie_id
+    ORDER BY AVG(rating) DESC, title
+    LIMIT 1
+);
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+1. **Find the user with the most ratings:**
+   - Group by `user_id`, count the ratings.
+   - Merge with `Users` table to get `name`.
+   - Sort by **count descending**, then **lexicographically**.
+
+2. **Find the highest-rated movie in February 2020:**
+   - Filter only `created_at` **in February 2020**.
+   - Group by `movie_id` and calculate **average rating**.
+   - Merge with `Movies` to get `title`.
+   - Sort by **rating descending**, then **lexicographically**.
+
+```python
+import pandas as pd
+
+def movie_rating(users: pd.DataFrame, movies: pd.DataFrame, movie_rating: pd.DataFrame) -> pd.DataFrame:
+    # Most active user
+    user_counts = movie_rating.groupby("user_id")["rating"].count().reset_index()
+    most_active_user = user_counts.merge(users, on="user_id")
+    most_active_user = most_active_user.sort_values(by=["rating", "name"], ascending=[False, True]).iloc[0]["name"]
+
+    # Highest-rated movie in February 2020
+    movie_rating["created_at"] = pd.to_datetime(movie_rating["created_at"])
+    feb_ratings = movie_rating[movie_rating["created_at"].dt.strftime('%Y-%m') == "2020-02"]
+    
+    avg_ratings = feb_ratings.groupby("movie_id")["rating"].mean().reset_index()
+    highest_rated_movie = avg_ratings.merge(movies, on="movie_id")
+    highest_rated_movie = highest_rated_movie.sort_values(by=["rating", "title"], ascending=[False, True]).iloc[0]["title"]
+
+    return pd.DataFrame({"results": [most_active_user, highest_rated_movie]})
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Movie-Rating
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/movie-rating/)
+- 📚 [SQL `GROUP BY` Clause](https://www.w3schools.com/sql/sql_groupby.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem description with tables**  
+✅ **Example with step-by-step explanation**  
+✅ **SQL and Pandas solutions with detailed breakdowns**  
+✅ **File structure for easy organization**  
+✅ **Helpful references for further learning**  
+
+Would you like any changes or additions? 🚀
\ No newline at end of file

From 7895ac377ba8c7a452c01ac653ad431b0e0168ee Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:33:46 +0530
Subject: [PATCH 304/353] Update readme.md

---
 .../1341. Movie Rating/readme.md                    | 13 +------------
 1 file changed, 1 insertion(+), 12 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md b/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md
index 40a5226..bcfc6ac 100644
--- a/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md	
+++ b/LeetCode SQL 50 Solution/1341. Movie Rating/readme.md	
@@ -1,6 +1,3 @@
-Here's a well-structured `README.md` for **LeetCode 1341 - Movie Rating**, formatted for a GitHub repository:  
-
-```md
 # 🎬 Movie Rating - LeetCode 1341
 
 ## 📌 Problem Statement
@@ -196,13 +193,5 @@ def movie_rating(users: pd.DataFrame, movies: pd.DataFrame, movie_rating: pd.Dat
 - 📖 [LeetCode Problem](https://leetcode.com/problems/movie-rating/)
 - 📚 [SQL `GROUP BY` Clause](https://www.w3schools.com/sql/sql_groupby.asp)
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem description with tables**  
-✅ **Example with step-by-step explanation**  
-✅ **SQL and Pandas solutions with detailed breakdowns**  
-✅ **File structure for easy organization**  
-✅ **Helpful references for further learning**  
 
-Would you like any changes or additions? 🚀
\ No newline at end of file
+## Would you like any changes or additions? 🚀
\ No newline at end of file

From 3b9bdc3f3a9007b91fcd1c29c276fa94e20bf1f3 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:34:40 +0530
Subject: [PATCH 305/353] Update readme.md

---
 .../readme.md                                 | 154 ++++++++++++++++++
 1 file changed, 154 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md
index e69de29..90efa05 100644
--- a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md	
+++ b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md	
@@ -0,0 +1,154 @@
+Here’s a well-structured `README.md` for **LeetCode 1378 - Replace Employee ID With The Unique Identifier**, formatted for a GitHub repository:  
+
+```md
+# 🏢 Replace Employee ID With The Unique Identifier - LeetCode 1378
+
+## 📌 Problem Statement
+You are given two tables: **Employees** and **EmployeeUNI**.
+
+Your task is to return a table with:
+- Each employee's **unique ID** if it exists.
+- If an employee **does not** have a unique ID, return `NULL`.
+
+The result can be returned in **any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Employees Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| id          | int     |
+| name        | varchar |
+
+- `id` is the **primary key** (unique for each employee).
+- `name` is the **employee's name**.
+
+---
+
+### **EmployeeUNI Table**
+| Column Name | Type |
+| ----------- | ---- |
+| id          | int  |
+| unique_id   | int  |
+
+- `(id, unique_id)` is the **primary key** (ensuring unique mapping of employee IDs to unique IDs).
+- Each employee **may or may not** have a corresponding **unique ID**.
+
+---
+
+## 📊 Example 1:
+### **Input:**
+#### **Employees Table**
+| id  | name     |
+| --- | -------- |
+| 1   | Alice    |
+| 7   | Bob      |
+| 11  | Meir     |
+| 90  | Winston  |
+| 3   | Jonathan |
+
+#### **EmployeeUNI Table**
+| id  | unique_id |
+| --- | --------- |
+| 3   | 1         |
+| 11  | 2         |
+| 90  | 3         |
+
+### **Output:**
+| unique_id | name     |
+| --------- | -------- |
+| null      | Alice    |
+| null      | Bob      |
+| 2         | Meir     |
+| 3         | Winston  |
+| 1         | Jonathan |
+
+### **Explanation:**
+- `Alice` and `Bob` **do not have** a unique ID, so we return `NULL`.
+- The **unique ID** of `Meir` is **2**.
+- The **unique ID** of `Winston` is **3**.
+- The **unique ID** of `Jonathan` is **1**.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Using `LEFT JOIN`**
+#### **Explanation:**
+- Use a **LEFT JOIN** to **include all employees**.
+- If an employee **does not have** a matching `unique_id`, return `NULL`.
+
+```sql
+SELECT eu.unique_id, e.name
+FROM Employees e
+LEFT JOIN EmployeeUNI eu
+ON e.id = eu.id;
+```
+
+### ✅ **Using `USING(id)`**
+#### **Explanation:**
+- `USING(id)` is a cleaner alternative when both tables share a column.
+
+```sql
+SELECT unique_id, name
+FROM Employees
+LEFT JOIN EmployeeUNI
+USING (id);
+```
+
+### ✅ **Sorting by `id` (Optional)**
+#### **Explanation:**
+- If you want to return the result **sorted by `id`**, add `ORDER BY e.id`:
+
+```sql
+SELECT eu.unique_id, e.name
+FROM Employees e
+LEFT JOIN EmployeeUNI eu
+ON e.id = eu.id
+ORDER BY e.id;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+- Merge `Employees` with `EmployeeUNI` **using `left` join** on `id`.
+- Fill missing values (`NaN`) with `None`.
+
+```python
+import pandas as pd
+
+def replace_employee_id(employees: pd.DataFrame, employee_uni: pd.DataFrame) -> pd.DataFrame:
+    merged_df = employees.merge(employee_uni, on="id", how="left")
+    return merged_df[["unique_id", "name"]]
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Replace-Employee-ID
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/replace-employee-id-with-the-unique-identifier/)
+- 📚 [SQL `LEFT JOIN`](https://www.w3schools.com/sql/sql_join_left.asp)
+- 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem description with tables**  
+✅ **Example with step-by-step explanation**  
+✅ **SQL and Pandas solutions with detailed breakdowns**  
+✅ **File structure for easy organization**  
+✅ **Helpful references for further learning**  
+
+Would you like any modifications? 🚀
\ No newline at end of file

From 3c444d4979a77eb885701abe91cbc9d811c97b66 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:34:53 +0530
Subject: [PATCH 306/353] Update readme.md

---
 .../readme.md                                        | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md
index 90efa05..6c034f4 100644
--- a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md	
+++ b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md	
@@ -1,6 +1,4 @@
-Here’s a well-structured `README.md` for **LeetCode 1378 - Replace Employee ID With The Unique Identifier**, formatted for a GitHub repository:  
 
-```md
 # 🏢 Replace Employee ID With The Unique Identifier - LeetCode 1378
 
 ## 📌 Problem Statement
@@ -142,13 +140,5 @@ def replace_employee_id(employees: pd.DataFrame, employee_uni: pd.DataFrame) ->
 - 📖 [LeetCode Problem](https://leetcode.com/problems/replace-employee-id-with-the-unique-identifier/)
 - 📚 [SQL `LEFT JOIN`](https://www.w3schools.com/sql/sql_join_left.asp)
 - 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem description with tables**  
-✅ **Example with step-by-step explanation**  
-✅ **SQL and Pandas solutions with detailed breakdowns**  
-✅ **File structure for easy organization**  
-✅ **Helpful references for further learning**  
 
-Would you like any modifications? 🚀
\ No newline at end of file
+## Would you like any modifications? 🚀
\ No newline at end of file

From 79eaf898020b64fc12fd9ca06282ae82269265fe Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:34:59 +0530
Subject: [PATCH 307/353] Update readme.md

---
 .../readme.md                                                    | 1 -
 1 file changed, 1 deletion(-)

diff --git a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md
index 6c034f4..a92e9b1 100644
--- a/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md	
+++ b/LeetCode SQL 50 Solution/1378. Replace Employee ID With The Unique Identifier/readme.md	
@@ -1,4 +1,3 @@
-
 # 🏢 Replace Employee ID With The Unique Identifier - LeetCode 1378
 
 ## 📌 Problem Statement

From d05784b96be88a5cbe71a03334b5be5c2d93ca89 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:35:15 +0530
Subject: [PATCH 308/353] Update readme.md

---
 .../readme.md                                 | 125 ++++++++++++++++++
 1 file changed, 125 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md
index e69de29..e968b83 100644
--- a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md	
+++ b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md	
@@ -0,0 +1,125 @@
+Here’s a well-structured `README.md` for **LeetCode 1484 - Group Sold Products By The Date**, formatted for a GitHub repository:  
+
+```md
+# 🛍️ Group Sold Products By The Date - LeetCode 1484
+
+## 📌 Problem Statement
+You are given a table **Activities** that contains records of products sold on different dates.
+
+Your task is to return:
+- The **number of distinct products** sold on each date.
+- A **comma-separated string** of the product names, sorted **lexicographically**.
+
+The result should be **ordered by `sell_date`**.
+
+---
+
+## 📊 Table Structure
+
+### **Activities Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| sell_date   | date    |
+| product     | varchar |
+
+- This table **does not** have a **primary key**.
+- It may **contain duplicate entries**.
+
+---
+
+## 📊 Example 1:
+### **Input:**
+#### **Activities Table**
+| sell_date  | product    |
+| ---------- | ---------- |
+| 2020-05-30 | Headphone  |
+| 2020-06-01 | Pencil     |
+| 2020-06-02 | Mask       |
+| 2020-05-30 | Basketball |
+| 2020-06-01 | Bible      |
+| 2020-06-02 | Mask       |
+| 2020-05-30 | T-Shirt    |
+
+### **Output:**
+| sell_date  | num_sold | products                     |
+| ---------- | -------- | ---------------------------- |
+| 2020-05-30 | 3        | Basketball,Headphone,T-Shirt |
+| 2020-06-01 | 2        | Bible,Pencil                 |
+| 2020-06-02 | 1        | Mask                         |
+
+### **Explanation:**
+- `2020-05-30`: Sold items → _(Headphone, Basketball, T-Shirt)_  
+  - Sorted → **"Basketball, Headphone, T-Shirt"**  
+- `2020-06-01`: Sold items → _(Pencil, Bible)_  
+  - Sorted → **"Bible, Pencil"**  
+- `2020-06-02`: Sold item → _(Mask)_  
+  - **"Mask"** (only one item)
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Using `GROUP_CONCAT` with `DISTINCT`**
+#### **Explanation:**
+- Use `COUNT(DISTINCT product)` to get the **number of distinct products**.
+- Use `GROUP_CONCAT(DISTINCT product ORDER BY product ASC)` to **join product names in alphabetical order**.
+- Group by `sell_date`, then order the result by `sell_date`.
+
+```sql
+SELECT 
+    sell_date, 
+    COUNT(DISTINCT product) AS num_sold,
+    GROUP_CONCAT(DISTINCT product ORDER BY product ASC SEPARATOR ',') AS products
+FROM Activities
+GROUP BY sell_date
+ORDER BY sell_date ASC;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+- **Group by `sell_date`**.
+- Use `.nunique()` to count distinct products.
+- Use `', '.join(sorted(set(products)))` to sort and concatenate product names.
+
+```python
+import pandas as pd
+
+def group_sold_products(activities: pd.DataFrame) -> pd.DataFrame:
+    grouped_df = (
+        activities.groupby("sell_date")["product"]
+        .agg(lambda x: ", ".join(sorted(set(x))))
+        .reset_index()
+    )
+    grouped_df["num_sold"] = grouped_df["product"].apply(lambda x: len(x.split(",")))
+    return grouped_df.rename(columns={"product": "products"})
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Group-Sold-Products
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/group-sold-products-by-the-date/)
+- 📚 [SQL `GROUP BY`](https://www.w3schools.com/sql/sql_groupby.asp)
+- 🐍 [Pandas `groupby()` Documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem statement with tables**  
+✅ **Example with step-by-step explanation**  
+✅ **SQL and Pandas solutions with detailed breakdowns**  
+✅ **File structure for easy organization**  
+✅ **Helpful references for further learning**  
+
+Would you like any modifications? 🚀
\ No newline at end of file

From 249bbb90f455ba4c14d93f28e76b8458448cd9d0 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:35:35 +0530
Subject: [PATCH 309/353] Update readme.md

---
 .../1484. Group Sold Products By The Date/readme.md | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md
index e968b83..318b5fd 100644
--- a/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md	
+++ b/LeetCode SQL 50 Solution/1484. Group Sold Products By The Date/readme.md	
@@ -1,6 +1,3 @@
-Here’s a well-structured `README.md` for **LeetCode 1484 - Group Sold Products By The Date**, formatted for a GitHub repository:  
-
-```md
 # 🛍️ Group Sold Products By The Date - LeetCode 1484
 
 ## 📌 Problem Statement
@@ -113,13 +110,3 @@ def group_sold_products(activities: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/group-sold-products-by-the-date/)
 - 📚 [SQL `GROUP BY`](https://www.w3schools.com/sql/sql_groupby.asp)
 - 🐍 [Pandas `groupby()` Documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem statement with tables**  
-✅ **Example with step-by-step explanation**  
-✅ **SQL and Pandas solutions with detailed breakdowns**  
-✅ **File structure for easy organization**  
-✅ **Helpful references for further learning**  
-
-Would you like any modifications? 🚀
\ No newline at end of file

From 9a6ceb10d09b35b66d537a47b7f774730213d365 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:37:28 +0530
Subject: [PATCH 310/353] Update readme.md

---
 .../readme.md                                 | 108 ++++++++++++++++++
 1 file changed, 108 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md
index e69de29..5c101eb 100644
--- a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md	
+++ b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md	
@@ -0,0 +1,108 @@
+Here’s a well-structured `README.md` for **LeetCode 1517 - Find Users With Valid E-Mails**, formatted for a GitHub repository:  
+
+```md
+# 📩 Find Users With Valid E-Mails - LeetCode 1517
+
+## 📌 Problem Statement
+You are given a table **Users** that contains user registration details, including their emails. Some of these emails may be **invalid**.
+
+A valid email:
+- Has a **prefix name** and a **domain**.
+- The prefix:
+  - **Must start with a letter** (uppercase or lowercase).
+  - Can contain **letters**, **digits**, **underscore (`_`)**, **period (`.`)**, and/or **dash (`-`)**.
+- The domain must be **"@leetcode.com"**.
+
+Your task is to **find users with valid emails**.
+
+---
+
+## 📊 Table Structure
+
+### **Users Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| user_id     | int     |
+| name        | varchar |
+| mail        | varchar |
+
+- `user_id` is the **primary key**.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Users Table**
+| user_id | name      | mail                    |
+| ------- | --------- | ----------------------- |
+| 1       | Winston   | winston@leetcode.com    |
+| 2       | Jonathan  | jonathanisgreat         |
+| 3       | Annabelle | bella-@leetcode.com     |
+| 4       | Sally     | sally.come@leetcode.com |
+| 5       | Marwan    | quarz#2020@leetcode.com |
+| 6       | David     | david69@gmail.com       |
+| 7       | Shapiro   | .shapo@leetcode.com     |
+
+### **Output:**
+| user_id | name      | mail                    |
+| ------- | --------- | ----------------------- |
+| 1       | Winston   | winston@leetcode.com    |
+| 3       | Annabelle | bella-@leetcode.com     |
+| 4       | Sally     | sally.come@leetcode.com |
+
+### **Explanation:**
+- ✅ **Valid emails:**
+  - `winston@leetcode.com` ✅ (Starts with a letter, correct domain)
+  - `bella-@leetcode.com` ✅ (Starts with a letter, correct domain)
+  - `sally.come@leetcode.com` ✅ (Starts with a letter, correct domain)
+- ❌ **Invalid emails:**
+  - `jonathanisgreat` ❌ (No domain)
+  - `quarz#2020@leetcode.com` ❌ (Contains `#`, which is not allowed)
+  - `david69@gmail.com` ❌ (Wrong domain)
+  - `.shapo@leetcode.com` ❌ (Starts with a period `.`)
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Using `REGEXP_LIKE` (MySQL)**
+#### **Explanation:**
+- Use `REGEXP_LIKE(mail, '^[A-Za-z]+[A-Za-z0-9_.-]*@leetcode\\.com$')`
+  - `^` → Start of string.
+  - `[A-Za-z]+` → First character **must** be a **letter**.
+  - `[A-Za-z0-9_.-]*` → Rest can be **letters, numbers, `_`, `.`, or `-`**.
+  - `@leetcode\\.com$` → Must end with `"@leetcode.com"`.
+
+```sql
+SELECT *
+FROM Users
+WHERE REGEXP_LIKE(mail, '^[A-Za-z]+[A-Za-z0-9_.-]*@leetcode\\.com$');
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Find-Users-With-Valid-Emails
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/find-users-with-valid-e-mails/)
+- 🔍 [MySQL REGEXP_LIKE Documentation](https://dev.mysql.com/doc/refman/8.0/en/regexp.html)
+- 📝 [SQL Regular Expressions Cheatsheet](https://www.w3schools.com/sql/sql_regex.asp)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem statement with tables**  
+✅ **Example with detailed explanation**  
+✅ **SQL solution breakdown**  
+✅ **File structure for easy organization**  
+✅ **Helpful links for further learning**  
+
+Would you like any modifications? 🚀
\ No newline at end of file

From a1724559a101d4bad3996489e9c07c604da93edc Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:37:45 +0530
Subject: [PATCH 311/353] Update readme.md

---
 .../1517. Find Users With Valid E-Mails/readme.md | 15 ++-------------
 1 file changed, 2 insertions(+), 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md
index 5c101eb..4162dc3 100644
--- a/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md	
+++ b/LeetCode SQL 50 Solution/1517. Find Users With Valid E-Mails/readme.md	
@@ -1,6 +1,3 @@
-Here’s a well-structured `README.md` for **LeetCode 1517 - Find Users With Valid E-Mails**, formatted for a GitHub repository:  
-
-```md
 # 📩 Find Users With Valid E-Mails - LeetCode 1517
 
 ## 📌 Problem Statement
@@ -96,13 +93,5 @@ WHERE REGEXP_LIKE(mail, '^[A-Za-z]+[A-Za-z0-9_.-]*@leetcode\\.com$');
 - 📖 [LeetCode Problem](https://leetcode.com/problems/find-users-with-valid-e-mails/)
 - 🔍 [MySQL REGEXP_LIKE Documentation](https://dev.mysql.com/doc/refman/8.0/en/regexp.html)
 - 📝 [SQL Regular Expressions Cheatsheet](https://www.w3schools.com/sql/sql_regex.asp)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem statement with tables**  
-✅ **Example with detailed explanation**  
-✅ **SQL solution breakdown**  
-✅ **File structure for easy organization**  
-✅ **Helpful links for further learning**  
-
-Would you like any modifications? 🚀
\ No newline at end of file
+her learning**  
+## Would you like any modifications? 🚀
\ No newline at end of file

From e7c1d6ede61ededb95b13049b1f07d8c694baad9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:38:15 +0530
Subject: [PATCH 312/353] Update readme.md

---
 .../1527. Patients With a Condition/readme.md | 89 +++++++++++++++++++
 1 file changed, 89 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md b/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md
index e69de29..40cca81 100644
--- a/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md	
+++ b/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md	
@@ -0,0 +1,89 @@
+Here’s a structured `README.md` for **LeetCode 1527 - Patients With a Condition**, formatted for a GitHub repository:  
+
+```md
+# 🏥 Patients With a Condition - LeetCode 1527
+
+## 📌 Problem Statement
+You are given a **Patients** table that stores patient health records, including their medical conditions.
+
+Each patient's **conditions** column contains **0 or more condition codes**, separated by spaces.
+
+Your task is to **find all patients who have Type I Diabetes**.  
+- Type I Diabetes is identified by a condition **starting with the prefix "DIAB1"**.
+
+Return the result **in any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Patients Table**
+| Column Name  | Type    |
+| ------------ | ------- |
+| patient_id   | int     |
+| patient_name | varchar |
+| conditions   | varchar |
+
+- `patient_id` is the **primary key**.
+- `conditions` contains **space-separated condition codes**.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Patients Table**
+| patient_id | patient_name | conditions   |
+| ---------- | ------------ | ------------ |
+| 1          | Daniel       | YFEV COUGH   |
+| 2          | Alice        |              |
+| 3          | Bob          | DIAB100 MYOP |
+| 4          | George       | ACNE DIAB100 |
+| 5          | Alain        | DIAB201      |
+
+### **Output:**
+| patient_id | patient_name | conditions   |
+| ---------- | ------------ | ------------ |
+| 3          | Bob          | DIAB100 MYOP |
+| 4          | George       | ACNE DIAB100 |
+
+### **Explanation:**
+- ✅ **Bob's condition:** `"DIAB100 MYOP"` → **Starts with `"DIAB1"`** → ✅ **Valid**
+- ✅ **George's condition:** `"ACNE DIAB100"` → **Contains `"DIAB1"`** → ✅ **Valid**
+- ❌ **Daniel's condition:** `"YFEV COUGH"` → **No `"DIAB1"`**
+- ❌ **Alice's condition:** `""` (Empty)
+- ❌ **Alain's condition:** `"DIAB201"` → Does **not** start with `"DIAB1"`
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Using `LIKE` with wildcards**
+#### **Explanation:**
+- We need to check if `"DIAB1"` appears **at the beginning** or **somewhere in the conditions column**.
+- `LIKE 'DIAB1%'` → Matches if `"DIAB1"` is at the **start** of the column.
+- `LIKE '% DIAB1%'` → Matches if `"DIAB1"` appears **after a space (as part of multiple conditions).**
+
+```sql
+SELECT patient_id, patient_name, conditions
+FROM Patients
+WHERE conditions LIKE 'DIAB1%' OR conditions LIKE '% DIAB1%';
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Patients-With-Condition
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/patients-with-a-condition/)
+- 🔍 [SQL LIKE Operator](https://www.w3schools.com/sql/sql_like.asp)
+- 📝 [MySQL String Functions](https://dev.mysql.com/doc/refman/8.0/en/string-functions.html)
+## Would you like any refinements? 🚀
\ No newline at end of file

From dad7b780bab1182681c6d11208e8e130f9ede0f6 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:38:30 +0530
Subject: [PATCH 313/353] Update readme.md

---
 .../1527. Patients With a Condition/readme.md                  | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md b/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md
index 40cca81..9eb9694 100644
--- a/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md	
+++ b/LeetCode SQL 50 Solution/1527. Patients With a Condition/readme.md	
@@ -1,6 +1,3 @@
-Here’s a structured `README.md` for **LeetCode 1527 - Patients With a Condition**, formatted for a GitHub repository:  
-
-```md
 # 🏥 Patients With a Condition - LeetCode 1527
 
 ## 📌 Problem Statement

From 269dde09e5f47053fb6225ce2fd6b6888005823b Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:40:56 +0530
Subject: [PATCH 314/353] Update readme.md

---
 .../readme.md                                 | 120 ++++++++++++++++++
 1 file changed, 120 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md
index e69de29..118695f 100644
--- a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md	
+++ b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md	
@@ -0,0 +1,120 @@
+Here’s a structured `README.md` for **LeetCode 1581 - Customer Who Visited but Did Not Make Any Transactions**, formatted for a GitHub repository:  
+
+```md
+# 🏬 Customer Who Visited but Did Not Make Any Transactions - LeetCode 1581
+
+## 📌 Problem Statement
+You are given two tables, **Visits** and **Transactions**.
+
+- The **Visits** table records the customers who visited the mall.
+- The **Transactions** table records the **transactions made** during a visit.
+
+Your task is to **find customers who visited but did not make any transactions**.  
+Also, return the **number of times** these customers **visited without making any transactions**.
+
+Return the result **in any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Visits Table**
+| Column Name | Type |
+| ----------- | ---- |
+| visit_id    | int  |
+| customer_id | int  |
+
+- `visit_id` is the **unique identifier** for each visit.
+
+### **Transactions Table**
+| Column Name    | Type |
+| -------------- | ---- |
+| transaction_id | int  |
+| visit_id       | int  |
+| amount         | int  |
+
+- `transaction_id` is the **unique identifier** for each transaction.
+- `visit_id` represents the visit **associated with this transaction**.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Visits Table**
+| visit_id | customer_id |
+| -------- | ----------- |
+| 1        | 23          |
+| 2        | 9           |
+| 4        | 30          |
+| 5        | 54          |
+| 6        | 96          |
+| 7        | 54          |
+| 8        | 54          |
+
+#### **Transactions Table**
+| transaction_id | visit_id | amount |
+| -------------- | -------- | ------ |
+| 2              | 5        | 310    |
+| 3              | 5        | 300    |
+| 9              | 5        | 200    |
+| 12             | 1        | 910    |
+| 13             | 2        | 970    |
+
+### **Output:**
+| customer_id | count_no_trans |
+| ----------- | -------------- |
+| 54          | 2              |
+| 30          | 1              |
+| 96          | 1              |
+
+### **Explanation:**
+- ✅ **Customer 23:** Visited **once**, made **1 transaction** → ❌ Not included  
+- ✅ **Customer 9:** Visited **once**, made **1 transaction** → ❌ Not included  
+- ✅ **Customer 30:** Visited **once**, made **0 transactions** → ✅ Included (`count_no_trans = 1`)  
+- ✅ **Customer 54:** Visited **3 times**, made **transactions in 1 visit**, **but 2 visits had no transactions** → ✅ Included (`count_no_trans = 2`)  
+- ✅ **Customer 96:** Visited **once**, made **0 transactions** → ✅ Included (`count_no_trans = 1`)  
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Using `NOT IN` to Find Visits Without Transactions**
+#### **Explanation:**
+- First, **find all visit IDs** that **had at least one transaction** (`SELECT DISTINCT visit_id FROM Transactions`).
+- Then, filter out these visit IDs from the **Visits** table.
+- Finally, count the number of such visits **per customer**.
+
+```sql
+SELECT customer_id, COUNT(*) AS count_no_trans
+FROM Visits
+WHERE visit_id NOT IN (SELECT DISTINCT visit_id FROM Transactions)
+GROUP BY customer_id;
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Customer-No-Transaction
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/customer-who-visited-but-did-not-make-any-transactions/)
+- 🔍 [SQL NOT IN Operator](https://www.w3schools.com/sql/sql_in.asp)
+- 📝 [MySQL Aggregate Functions](https://dev.mysql.com/doc/refman/8.0/en/group-by-functions.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem description with table formatting**  
+✅ **Example with detailed explanation**  
+✅ **SQL solution breakdown for better understanding**  
+✅ **Organized file structure for repository**  
+✅ **Helpful links for further learning**  
+
+Would you like any refinements? 🚀
\ No newline at end of file

From 3b4320e3b9de060a83ce2a2cf500cca3f16b7d25 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:41:04 +0530
Subject: [PATCH 315/353] Update readme.md

---
 .../readme.md                                          | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md
index 118695f..f5c4636 100644
--- a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md	
+++ b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md	
@@ -108,13 +108,3 @@ GROUP BY customer_id;
 - 📖 [LeetCode Problem](https://leetcode.com/problems/customer-who-visited-but-did-not-make-any-transactions/)
 - 🔍 [SQL NOT IN Operator](https://www.w3schools.com/sql/sql_in.asp)
 - 📝 [MySQL Aggregate Functions](https://dev.mysql.com/doc/refman/8.0/en/group-by-functions.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem description with table formatting**  
-✅ **Example with detailed explanation**  
-✅ **SQL solution breakdown for better understanding**  
-✅ **Organized file structure for repository**  
-✅ **Helpful links for further learning**  
-
-Would you like any refinements? 🚀
\ No newline at end of file

From 005548bdc8af6a7523a2f067002708326e348a1f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:41:14 +0530
Subject: [PATCH 316/353] Update readme.md

---
 .../readme.md                                                  | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md
index f5c4636..188444c 100644
--- a/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md	
+++ b/LeetCode SQL 50 Solution/1581. Customer Who Visited but Did Not Make Any Transactions/readme.md	
@@ -1,6 +1,3 @@
-Here’s a structured `README.md` for **LeetCode 1581 - Customer Who Visited but Did Not Make Any Transactions**, formatted for a GitHub repository:  
-
-```md
 # 🏬 Customer Who Visited but Did Not Make Any Transactions - LeetCode 1581
 
 ## 📌 Problem Statement

From 807c510c6f67624339dbca622d39d5e8a04b96bf Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:43:50 +0530
Subject: [PATCH 317/353] Update readme.md

---
 .../readme.md                                 | 115 ++++++++++++++++++
 1 file changed, 115 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/readme.md b/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/readme.md
index e69de29..4922922 100644
--- a/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/readme.md	
+++ b/LeetCode SQL 50 Solution/1633. Percentage of Users Attended a Contest/readme.md	
@@ -0,0 +1,115 @@
+# 🎯 Percentage of Users Attended a Contest - LeetCode 1633
+
+## 📌 Problem Statement
+You are given two tables, **Users** and **Register**.
+
+- The **Users** table contains the users who are registered on the platform.
+- The **Register** table records which user registered for which contest.
+
+Your task is to **calculate the percentage of users who attended each contest**.
+
+- Round the percentage to **two decimal places**.
+- **Sort the results** by the percentage in descending order. If the percentages are the same, sort them by **contest_id** in ascending order.
+
+---
+
+## 📊 Table Structure
+
+### **Users Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| user_id     | int     |
+| user_name   | varchar |
+
+- `user_id` is the **primary key** for this table.
+
+### **Register Table**
+| Column Name | Type |
+| ----------- | ---- |
+| contest_id  | int  |
+| user_id     | int  |
+
+- `(contest_id, user_id)` is the **primary key** for this table.
+- This table keeps track of which user has registered for which contest.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Users Table**
+| user_id | user_name |
+| ------- | --------- |
+| 6       | Alice     |
+| 2       | Bob       |
+| 7       | Alex      |
+
+#### **Register Table**
+| contest_id | user_id |
+| ---------- | ------- |
+| 215        | 6       |
+| 209        | 2       |
+| 208        | 2       |
+| 210        | 6       |
+| 208        | 6       |
+| 209        | 7       |
+| 209        | 6       |
+| 215        | 7       |
+| 208        | 7       |
+| 210        | 2       |
+| 207        | 2       |
+| 210        | 7       |
+
+### **Output:**
+| contest_id | percentage |
+| ---------- | ---------- |
+| 208        | 100.00     |
+| 209        | 100.00     |
+| 210        | 100.00     |
+| 215        | 66.67      |
+| 207        | 33.33      |
+
+### **Explanation:**
+- For **contest 208, 209, and 210**, **100%** of users attended these contests.
+- For **contest 215**, **66.67%** of users attended it (Alice and Alex).
+- For **contest 207**, only **33.33%** of users attended it (Bob).
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+1. Calculate the total number of users in the **Users** table using `SELECT COUNT(*) FROM Users`.
+2. For each contest, count how many users registered for that contest in the **Register** table.
+3. Calculate the percentage of users who registered for each contest using the formula:
+
+   \[
+   \text{Percentage} = \left( \frac{\text{Registered Users}}{\text{Total Users}} \right) \times 100
+   \]
+
+4. Round the percentage to **two decimal places**.
+5. Sort the results by **percentage** in descending order. If there’s a tie, sort by **contest_id** in ascending order.
+
+```sql
+SELECT contest_id, ROUND((COUNT(user_id) / (SELECT COUNT(*) FROM Users) * 100), 2) AS percentage
+FROM Register
+GROUP BY contest_id
+ORDER BY percentage DESC, contest_id ASC;
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Contest-Percentage
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/percentage-of-users-attended-a-contest/)
+- 📝 [MySQL Aggregate Functions](https://dev.mysql.com/doc/refman/8.0/en/group-by-functions.html)
+- 🔍 [SQL ROUND Function](https://www.w3schools.com/sql/func_mysql_round.asp)

From 8953febd9b38c319b53b7d8fda304869eadfce7a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:44:36 +0530
Subject: [PATCH 318/353] Update readme.md

---
 .../1667. Fix Names in a Table/readme.md      | 93 +++++++++++++++++++
 1 file changed, 93 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md
index e69de29..0d588e3 100644
--- a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md	
+++ b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md	
@@ -0,0 +1,93 @@
+Here’s a structured `README.md` for **LeetCode 1667 - Fix Names in a Table**, formatted for a GitHub repository:
+
+```md
+# 📝 Fix Names in a Table - LeetCode 1667
+
+## 📌 Problem Statement
+You are given a table **Users** that contains user IDs and names.
+
+- The **name column** contains names that are written in a **mixed-case format** (e.g., `aLice`, `bOB`).
+- Your task is to **correct the formatting** so that:
+  - The **first letter** of the name is **uppercase**.
+  - The **remaining letters** are **lowercase**.
+- The result should be **ordered by user_id**.
+
+---
+
+## 📊 Table Structure
+
+### **Users Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| user_id     | int     |
+| name        | varchar |
+
+- `user_id` is the **primary key** for this table.
+- `name` contains only **uppercase and lowercase** letters.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Users Table**
+| user_id | name  |
+| ------- | ----- |
+| 1       | aLice |
+| 2       | bOB   |
+
+### **Output:**
+| user_id | name  |
+| ------- | ----- |
+| 1       | Alice |
+| 2       | Bob   |
+
+### **Explanation:**
+- The first letter of each name is converted to **uppercase**.
+- The remaining letters are converted to **lowercase**.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+1. Use the `UPPER()` function to **capitalize** the **first letter** of the name.
+2. Use the `LOWER()` function to convert the **rest of the name** to **lowercase**.
+3. Use `LEFT(name, 1)` to extract the **first character** of the name.
+4. Use `SUBSTRING(name, 2)` to extract the **rest of the name**.
+5. Use `CONCAT()` to combine the capitalized first letter and the lowercase remaining part.
+6. **Sort the output** by `user_id`.
+
+```sql
+SELECT user_id, 
+       CONCAT(UPPER(LEFT(name, 1)), LOWER(SUBSTRING(name, 2))) AS name 
+FROM Users 
+ORDER BY user_id;
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Fix-Names
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/fix-names-in-a-table/)
+- 🔍 [SQL CONCAT Function](https://www.w3schools.com/sql/func_mysql_concat.asp)
+- 📝 [MySQL String Functions](https://dev.mysql.com/doc/refman/8.0/en/string-functions.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem description with table formatting**  
+✅ **Example with detailed explanation**  
+✅ **SQL solution breakdown for better understanding**  
+✅ **Organized file structure for repository**  
+✅ **Helpful links for further learning**  
+
+Would you like any refinements? 🚀
\ No newline at end of file

From e2d1ada84e6dba18cc57524b765dd3efadecfd76 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:44:48 +0530
Subject: [PATCH 319/353] Update readme.md

---
 .../1667. Fix Names in a Table/readme.md             | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md
index 0d588e3..da1a91e 100644
--- a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md	
+++ b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md	
@@ -1,6 +1,4 @@
-Here’s a structured `README.md` for **LeetCode 1667 - Fix Names in a Table**, formatted for a GitHub repository:
 
-```md
 # 📝 Fix Names in a Table - LeetCode 1667
 
 ## 📌 Problem Statement
@@ -81,13 +79,3 @@ ORDER BY user_id;
 - 📖 [LeetCode Problem](https://leetcode.com/problems/fix-names-in-a-table/)
 - 🔍 [SQL CONCAT Function](https://www.w3schools.com/sql/func_mysql_concat.asp)
 - 📝 [MySQL String Functions](https://dev.mysql.com/doc/refman/8.0/en/string-functions.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem description with table formatting**  
-✅ **Example with detailed explanation**  
-✅ **SQL solution breakdown for better understanding**  
-✅ **Organized file structure for repository**  
-✅ **Helpful links for further learning**  
-
-Would you like any refinements? 🚀
\ No newline at end of file

From 215e8eb1f2144d7f97d6cb85cff5b3bf51d94137 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:44:55 +0530
Subject: [PATCH 320/353] Update readme.md

---
 LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md
index da1a91e..cd2e4c9 100644
--- a/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md	
+++ b/LeetCode SQL 50 Solution/1667. Fix Names in a Table/readme.md	
@@ -1,4 +1,3 @@
-
 # 📝 Fix Names in a Table - LeetCode 1667
 
 ## 📌 Problem Statement

From dd2b670f03cabec520a8263f87ad84c4de9c3b29 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:46:09 +0530
Subject: [PATCH 321/353] Update readme.md

---
 .../1683. Invalid Tweets/readme.md            | 85 +++++++++++++++++++
 1 file changed, 85 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md b/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md
index e69de29..0888aae 100644
--- a/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md	
+++ b/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md	
@@ -0,0 +1,85 @@
+Here's a well-structured `README.md` for **LeetCode 1683 - Invalid Tweets**, formatted for a GitHub repository:
+
+```md
+# 🐦 Invalid Tweets - LeetCode 1683
+
+## 📌 Problem Statement
+You are given a table **Tweets** that contains tweet IDs and their content.
+
+- A tweet is **invalid** if its content exceeds **15 characters**.
+- Your task is to find and return the **IDs of all invalid tweets**.
+
+---
+
+## 📊 Table Structure
+
+### **Tweets Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| tweet_id    | int     |
+| content     | varchar |
+
+- `tweet_id` is the **primary key**.
+- `content` consists of **alphanumeric characters, '!', and spaces**.
+- Tweets can have **a maximum of 15 characters** to be valid.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Tweets Table**
+| tweet_id | content                           |
+| -------- | --------------------------------- |
+| 1        | Let us Code                       |
+| 2        | More than fifteen chars are here! |
+
+### **Output:**
+| tweet_id |
+| -------- |
+| 2        |
+
+### **Explanation:**
+- **Tweet 1** has **11 characters**, so it is **valid**.
+- **Tweet 2** has **33 characters**, so it is **invalid**.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+1. Use the `LENGTH()` function to get the **character length** of the tweet.
+2. Filter rows where **content length > 15**.
+3. Return only the `tweet_id` of invalid tweets.
+
+```sql
+SELECT tweet_id
+FROM Tweets
+WHERE LENGTH(content) > 15;
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Invalid-Tweets
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/invalid-tweets/)
+- 📝 [MySQL LENGTH Function](https://www.w3schools.com/sql/func_mysql_length.asp)
+```
+
+### Features of this `README.md`:
+✅ **Concise problem description**  
+✅ **Example with detailed explanation**  
+✅ **SQL solution breakdown for clarity**  
+✅ **Organized repository structure**  
+✅ **Useful links for learning**  
+
+Would you like any modifications? 🚀
\ No newline at end of file

From f76b8c2a87422d53005009a6922203b70b819ca7 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:46:18 +0530
Subject: [PATCH 322/353] Update readme.md

---
 .../1683. Invalid Tweets/readme.md                     | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md b/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md
index 0888aae..b5fb105 100644
--- a/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md	
+++ b/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md	
@@ -73,13 +73,3 @@ WHERE LENGTH(content) > 15;
 ## 🔗 Useful Links
 - 📖 [LeetCode Problem](https://leetcode.com/problems/invalid-tweets/)
 - 📝 [MySQL LENGTH Function](https://www.w3schools.com/sql/func_mysql_length.asp)
-```
-
-### Features of this `README.md`:
-✅ **Concise problem description**  
-✅ **Example with detailed explanation**  
-✅ **SQL solution breakdown for clarity**  
-✅ **Organized repository structure**  
-✅ **Useful links for learning**  
-
-Would you like any modifications? 🚀
\ No newline at end of file

From 0466952d1fd5d6ac394365e725cb91bf1578b2fc Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:46:27 +0530
Subject: [PATCH 323/353] Update readme.md

---
 LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md b/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md
index b5fb105..586e52c 100644
--- a/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md	
+++ b/LeetCode SQL 50 Solution/1683. Invalid Tweets/readme.md	
@@ -1,6 +1,3 @@
-Here's a well-structured `README.md` for **LeetCode 1683 - Invalid Tweets**, formatted for a GitHub repository:
-
-```md
 # 🐦 Invalid Tweets - LeetCode 1683
 
 ## 📌 Problem Statement

From 3bb28e45231d46cb108e88d860f7429c7b146569 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:46:50 +0530
Subject: [PATCH 324/353] Update readme.md

---
 .../1729. Find Followers Count/readme.md      | 82 +++++++++++++++++++
 1 file changed, 82 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md
index e69de29..a4ddc75 100644
--- a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
+++ b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
@@ -0,0 +1,82 @@
+Here’s a well-structured `README.md` for **LeetCode 1729 - Find Followers Count**, formatted for a GitHub repository:
+
+```md
+# 📊 Find Followers Count - LeetCode 1729
+
+## 📌 Problem Statement
+You are given a table **Followers** that contains the following information:
+
+- `user_id`: The ID of the user being followed.
+- `follower_id`: The ID of the user who is following.
+
+Your task is to return a list of users with their **follower count**, sorted in **ascending order of `user_id`**.
+
+---
+
+## 📊 Table Structure
+
+### **Followers Table**
+| Column Name | Type |
+| ----------- | ---- |
+| user_id     | int  |
+| follower_id | int  |
+
+- `(user_id, follower_id)` is the **primary key**.
+- Each row represents a **follower relationship** between two users.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Followers Table**
+| user_id | follower_id |
+| ------- | ----------- |
+| 0       | 1           |
+| 1       | 0           |
+| 2       | 0           |
+| 2       | 1           |
+
+### **Output:**
+| user_id | followers_count |
+| ------- | --------------- |
+| 0       | 1               |
+| 1       | 1               |
+| 2       | 2               |
+
+### **Explanation:**
+- **User 0** has **1 follower** `{1}`.
+- **User 1** has **1 follower** `{0}`.
+- **User 2** has **2 followers** `{0, 1}`.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+1. Use `COUNT(follower_id)` to count the number of followers for each `user_id`.
+2. Use `GROUP BY user_id` to group the followers for each user.
+3. Sort the result by `user_id` in **ascending order**.
+
+```sql
+SELECT user_id, COUNT(follower_id) AS followers_count
+FROM Followers
+GROUP BY user_id
+ORDER BY user_id;
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Find-Followers-Count
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/find-followers-count/)
+- 📝 [MySQL COUNT Function](https://www.w3schools.com/sql/sql_count.asp)

From b10b65c6206170cabdfc94c961e320fe2ad8a339 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:47:01 +0530
Subject: [PATCH 325/353] Update readme.md

---
 LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md
index a4ddc75..861539c 100644
--- a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
+++ b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
@@ -1,6 +1,3 @@
-Here’s a well-structured `README.md` for **LeetCode 1729 - Find Followers Count**, formatted for a GitHub repository:
-
-```md
 # 📊 Find Followers Count - LeetCode 1729
 
 ## 📌 Problem Statement

From 3229152cdacfcc9ad51a01ab95a1d20e439149f9 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:48:31 +0530
Subject: [PATCH 326/353] Update readme.md

---
 .../1729. Find Followers Count/readme.md      | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md
index 861539c..4805d92 100644
--- a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
+++ b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
@@ -1,3 +1,6 @@
+Here's an improved `README.md` with a **Python (Pandas) solution** alongside the **SQL solution**:  
+
+```md
 # 📊 Find Followers Count - LeetCode 1729
 
 ## 📌 Problem Statement
@@ -64,12 +67,36 @@ ORDER BY user_id;
 
 ---
 
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. Use `groupby("user_id")` to count followers for each user.
+2. Use `reset_index(name="followers_count")` to format the result properly.
+3. Sort the result by `user_id`.
+
+```python
+import pandas as pd
+
+def find_followers_count(followers: pd.DataFrame) -> pd.DataFrame:
+    result = (
+        followers.groupby("user_id")["follower_id"]
+        .count()
+        .reset_index(name="followers_count")
+        .sort_values("user_id")
+    )
+    return result
+```
+
+---
+
 ## 📁 File Structure
 ```
 📂 Find-Followers-Count
 │── 📜 README.md
 │── 📜 solution.sql
+│── 📜 solution.py
 │── 📜 test_cases.sql
+│── 📜 test_cases.csv
 ```
 
 ---
@@ -77,3 +104,14 @@ ORDER BY user_id;
 ## 🔗 Useful Links
 - 📖 [LeetCode Problem](https://leetcode.com/problems/find-followers-count/)
 - 📝 [MySQL COUNT Function](https://www.w3schools.com/sql/sql_count.asp)
+- 🐍 [Pandas GroupBy](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html)
+```
+
+---
+
+### 🚀 **What's New in this Version?**
+✅ **Added Python (Pandas) Solution**  
+✅ **Structured File Organization**  
+✅ **Includes Helpful Links for Learning**  
+
+Would you like any further enhancements? 🚀
\ No newline at end of file

From bb626d00c179811ae02fc1e01adb7b12fe7a9f13 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:48:44 +0530
Subject: [PATCH 327/353] Update readme.md

---
 .../1729. Find Followers Count/readme.md              | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md
index 4805d92..54fa1e2 100644
--- a/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
+++ b/LeetCode SQL 50 Solution/1729. Find Followers Count/readme.md	
@@ -1,6 +1,3 @@
-Here's an improved `README.md` with a **Python (Pandas) solution** alongside the **SQL solution**:  
-
-```md
 # 📊 Find Followers Count - LeetCode 1729
 
 ## 📌 Problem Statement
@@ -107,11 +104,3 @@ def find_followers_count(followers: pd.DataFrame) -> pd.DataFrame:
 - 🐍 [Pandas GroupBy](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html)
 ```
 
----
-
-### 🚀 **What's New in this Version?**
-✅ **Added Python (Pandas) Solution**  
-✅ **Structured File Organization**  
-✅ **Includes Helpful Links for Learning**  
-
-Would you like any further enhancements? 🚀
\ No newline at end of file

From 94f7c1c328615ccacdfa583e929406f7a4ad27a3 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:54:27 +0530
Subject: [PATCH 328/353] Update readme.md

---
 .../readme.md                                 | 113 ++++++++++++++++++
 1 file changed, 113 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md
index e69de29..c05e719 100644
--- a/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md	
+++ b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md	
@@ -0,0 +1,113 @@
+Here’s a well-structured `README.md` for **LeetCode 1757 - Recyclable and Low Fat Products**, formatted for a GitHub repository:
+
+```md
+# ♻️ Recyclable and Low Fat Products - LeetCode 1757
+
+## 📌 Problem Statement
+You are given a table **Products** that contains information about products with respect to their fat content and recyclability.
+
+- The **low_fats** column is an ENUM with values `'Y'` and `'N'`, where `'Y'` indicates the product is low fat.
+- The **recyclable** column is an ENUM with values `'Y'` and `'N'`, where `'Y'` indicates the product is recyclable.
+
+Your task is to **find the IDs of products that are both low fat and recyclable**.
+
+Return the result in **any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Products Table**
+| Column Name | Type |
+| ----------- | ---- |
+| product_id  | int  |
+| low_fats    | enum |
+| recyclable  | enum |
+
+- `product_id` is the **primary key**.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Products Table**
+| product_id | low_fats | recyclable |
+| ---------- | -------- | ---------- |
+| 0          | Y        | N          |
+| 1          | Y        | Y          |
+| 2          | N        | Y          |
+| 3          | Y        | Y          |
+| 4          | N        | N          |
+
+### **Output:**
+| product_id |
+| ---------- |
+| 1          |
+| 3          |
+
+### **Explanation:**
+- Only products with `product_id` **1** and **3** are **both low fat and recyclable**.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+- Filter the **Products** table for rows where `low_fats = 'Y'` and `recyclable = 'Y'`.
+- Return the corresponding `product_id`.
+
+```sql
+SELECT product_id
+FROM Products
+WHERE low_fats = 'Y' AND recyclable = 'Y';
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. Load the **Products** table into a Pandas DataFrame.
+2. Filter the DataFrame to keep rows where both `low_fats` and `recyclable` are `'Y'`.
+3. Select and return the `product_id` column.
+
+```python
+import pandas as pd
+
+def recyclable_low_fat_products(products: pd.DataFrame) -> pd.DataFrame:
+    # Filter rows where both low_fats and recyclable are 'Y'
+    filtered = products[(products['low_fats'] == 'Y') & (products['recyclable'] == 'Y')]
+    # Select only the product_id column
+    result = filtered[['product_id']]
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Recyclable-Low-Fat-Products
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+│── 📜 sample_data.csv
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/recyclable-and-low-fat-products/)
+- 🔍 [MySQL WHERE Clause](https://www.w3schools.com/sql/sql_where.asp)
+- 🐍 [Pandas DataFrame Filtering](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem description with table structure**  
+✅ **Example with detailed explanation**  
+✅ **SQL and Python (Pandas) solutions with breakdown**  
+✅ **Organized file structure for repository**  
+✅ **Helpful links for further learning**
+
+Let me know if you need any modifications!
\ No newline at end of file

From 882a0fcbc2c504e6407da2e3a0a3e50ab6c0a1db Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:54:39 +0530
Subject: [PATCH 329/353] Update readme.md

---
 .../1757. Recyclable and Low Fat Products/readme.md | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md
index c05e719..74614f9 100644
--- a/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md	
+++ b/LeetCode SQL 50 Solution/1757. Recyclable and Low Fat Products/readme.md	
@@ -1,6 +1,3 @@
-Here’s a well-structured `README.md` for **LeetCode 1757 - Recyclable and Low Fat Products**, formatted for a GitHub repository:
-
-```md
 # ♻️ Recyclable and Low Fat Products - LeetCode 1757
 
 ## 📌 Problem Statement
@@ -101,13 +98,3 @@ def recyclable_low_fat_products(products: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/recyclable-and-low-fat-products/)
 - 🔍 [MySQL WHERE Clause](https://www.w3schools.com/sql/sql_where.asp)
 - 🐍 [Pandas DataFrame Filtering](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem description with table structure**  
-✅ **Example with detailed explanation**  
-✅ **SQL and Python (Pandas) solutions with breakdown**  
-✅ **Organized file structure for repository**  
-✅ **Helpful links for further learning**
-
-Let me know if you need any modifications!
\ No newline at end of file

From b3413762867ad39c9ac2870a59bf50bb4d078259 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:55:03 +0530
Subject: [PATCH 330/353] Update readme.md

---
 .../readme.md                                 | 148 ++++++++++++++++++
 1 file changed, 148 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md
index e69de29..0740626 100644
--- a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md	
+++ b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md	
@@ -0,0 +1,148 @@
+Here’s a well-structured `README.md` for **LeetCode 1731 - The Number of Employees Which Report to Each Employee**, formatted for a GitHub repository:
+
+```md
+# 👥 The Number of Employees Which Report to Each Employee - LeetCode 1731
+
+## 📌 Problem Statement
+You are given a table **Employees** that contains the following columns:
+- `employee_id`: The unique ID of the employee.
+- `name`: The name of the employee.
+- `reports_to`: The `employee_id` of the manager the employee reports to (can be `NULL` if the employee does not report to anyone).
+- `age`: The age of the employee.
+
+A manager is defined as an employee who has **at least 1 direct report**.  
+Your task is to report:
+- The **IDs and names of all managers**.
+- The **number of employees** who report **directly** to them.
+- The **average age** of their direct reports, rounded to the nearest integer.
+
+Return the result **ordered by `employee_id`** in ascending order.
+
+---
+
+## 📊 Table Structure
+
+### **Employees Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| employee_id | int     |
+| name        | varchar |
+| reports_to  | int     |
+| age         | int     |
+
+- `employee_id` is the **primary key**.
+- `reports_to` may be `NULL` for employees who do not report to anyone.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+| employee_id | name    | reports_to | age |
+| ----------- | ------- | ---------- | --- |
+| 9           | Hercy   | NULL       | 43  |
+| 6           | Alice   | 9          | 41  |
+| 4           | Bob     | 9          | 36  |
+| 2           | Winston | NULL       | 37  |
+
+### **Output:**
+| employee_id | name  | reports_count | average_age |
+| ----------- | ----- | ------------- | ----------- |
+| 9           | Hercy | 2             | 39          |
+
+### **Explanation:**
+- **Hercy** (employee_id = 9) is a manager with two direct reports: **Alice** (age 41) and **Bob** (age 36).  
+- The average age of these reports is (41 + 36) / 2 = 38.5, which is rounded to **39**.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+1. Use a **self-join** on the **Employees** table where the employee’s `reports_to` matches the manager’s `employee_id`.
+2. Count the number of direct reports for each manager.
+3. Compute the average age of the direct reports and round the result to the nearest integer.
+4. Group by the manager’s `employee_id` and order the results by `employee_id`.
+
+```sql
+SELECT
+  Manager.employee_id,
+  Manager.name,
+  COUNT(Employee.employee_id) AS reports_count,
+  ROUND(AVG(Employee.age)) AS average_age
+FROM Employees AS Manager
+INNER JOIN Employees AS Employee
+  ON Employee.reports_to = Manager.employee_id
+GROUP BY Manager.employee_id
+ORDER BY Manager.employee_id;
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. Filter the DataFrame to create a join between managers and their direct reports.
+2. Group by the manager’s `employee_id` and compute:
+   - The count of direct reports.
+   - The average age of the reports, then round the average.
+3. Merge the results with the original manager information.
+4. Sort the result by `employee_id`.
+
+```python
+import pandas as pd
+
+def employees_reporting(employees: pd.DataFrame) -> pd.DataFrame:
+    # Merge the table with itself: one for managers and one for employees reporting to them.
+    merged = employees.merge(
+        employees, 
+        left_on='employee_id', 
+        right_on='reports_to', 
+        suffixes=('_manager', '_report')
+    )
+    
+    # Group by manager's employee_id and name, then compute the count and average age of reports.
+    result = merged.groupby(['employee_id_manager', 'name_manager']).agg(
+        reports_count=('employee_id_report', 'count'),
+        average_age=('age_report', lambda x: round(x.mean()))
+    ).reset_index()
+    
+    # Rename columns to match expected output.
+    result.rename(columns={
+        'employee_id_manager': 'employee_id',
+        'name_manager': 'name'
+    }, inplace=True)
+    
+    # Sort by employee_id in ascending order.
+    result = result.sort_values('employee_id').reset_index(drop=True)
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Employees-Reporting
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/the-number-of-employees-which-report-to-each-employee/)
+- 🔍 [MySQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem description with table structure**  
+✅ **Example with detailed explanation**  
+✅ **SQL solution with breakdown**  
+✅ **Python (Pandas) solution with code and explanation**  
+✅ **Organized repository structure**  
+✅ **Helpful links for further learning**  
+
+Would you like any further modifications?
\ No newline at end of file

From 03dc4ce9ed80fa77a99d9c6457ca01cbe509538a Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:55:19 +0530
Subject: [PATCH 331/353] Update readme.md

---
 .../readme.md                                      | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md
index 0740626..8d6ef33 100644
--- a/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md	
+++ b/LeetCode SQL 50 Solution/1731. The Number of Employees Which Report to Each Employee/readme.md	
@@ -1,6 +1,3 @@
-Here’s a well-structured `README.md` for **LeetCode 1731 - The Number of Employees Which Report to Each Employee**, formatted for a GitHub repository:
-
-```md
 # 👥 The Number of Employees Which Report to Each Employee - LeetCode 1731
 
 ## 📌 Problem Statement
@@ -135,14 +132,3 @@ def employees_reporting(employees: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/the-number-of-employees-which-report-to-each-employee/)
 - 🔍 [MySQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-```
-
-### Features of this `README.md`:
-✅ **Clear problem description with table structure**  
-✅ **Example with detailed explanation**  
-✅ **SQL solution with breakdown**  
-✅ **Python (Pandas) solution with code and explanation**  
-✅ **Organized repository structure**  
-✅ **Helpful links for further learning**  
-
-Would you like any further modifications?
\ No newline at end of file

From 545d0a98149504319c59ada9b43452eae7ba76b4 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:56:28 +0530
Subject: [PATCH 332/353] Update readme.md

---
 .../readme.md                                 | 140 ++++++++++++++++++
 1 file changed, 140 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md
index e69de29..bc876ce 100644
--- a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md	
+++ b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md	
@@ -0,0 +1,140 @@
+Below is a well-structured `README.md` for **LeetCode 1789 - Primary Department for Each Employee**, including both the SQL and Pandas (Python) solutions:
+
+```md
+# 🏢 Primary Department for Each Employee - LeetCode 1789
+
+## 📌 Problem Statement
+You are given a table **Employee** that contains the following columns:
+
+- **employee_id**: The ID of the employee.
+- **department_id**: The ID of the department to which the employee belongs.
+- **primary_flag**: An ENUM ('Y', 'N').  
+  - If `primary_flag` is `'Y'`, then the department is the primary department for that employee.
+  - If `primary_flag` is `'N'`, then the department is not primary.
+
+**Note:**  
+- An employee can belong to multiple departments. When an employee joins multiple departments, they decide which one is their primary (set to `'Y'`).
+- If an employee belongs to only one department, then their `primary_flag` is `'N'`, but that department is still considered their primary department.
+
+Your task is to **report all employees with their primary department**.  
+For employees who belong to only one department, report that department.
+
+Return the result table in **any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Employee Table**
+| Column Name   | Type    |
+| ------------- | ------- |
+| employee_id   | int     |
+| department_id | int     |
+| primary_flag  | varchar |
+
+- `(employee_id, department_id)` is the **primary key** for this table.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Employee Table**
+| employee_id | department_id | primary_flag |
+| ----------- | ------------- | ------------ |
+| 1           | 1             | N            |
+| 2           | 1             | Y            |
+| 2           | 2             | N            |
+| 3           | 3             | N            |
+| 4           | 2             | N            |
+| 4           | 3             | Y            |
+| 4           | 4             | N            |
+
+### **Output:**
+| employee_id | department_id |
+| ----------- | ------------- |
+| 1           | 1             |
+| 2           | 1             |
+| 3           | 3             |
+| 4           | 3             |
+
+### **Explanation:**
+- **Employee 1** belongs to only one department (1), so department 1 is their primary.
+- **Employee 2** belongs to departments 1 and 2. The row with `primary_flag = 'Y'` indicates that department 1 is their primary.
+- **Employee 3** belongs only to department 3.
+- **Employee 4** belongs to departments 2, 3, and 4. The row with `primary_flag = 'Y'` indicates that department 3 is their primary.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+- **Step 1:** For employees who have `primary_flag = 'Y'`, choose those rows.
+- **Step 2:** For employees who belong to only one department, return that row.
+- Combine the results using `UNION DISTINCT`.
+
+```sql
+SELECT employee_id, department_id
+FROM Employee
+WHERE primary_flag = 'Y'
+UNION DISTINCT
+SELECT employee_id, department_id
+FROM Employee
+GROUP BY employee_id
+HAVING COUNT(*) = 1;
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. **Group** the DataFrame by `employee_id`.
+2. For each group:
+   - If any row has `primary_flag == 'Y'`, choose the first such row.
+   - Otherwise (i.e., employee belongs to only one department), choose that row.
+3. Return the resulting DataFrame with only `employee_id` and `department_id`.
+
+```python
+import pandas as pd
+
+def primary_department(employees: pd.DataFrame) -> pd.DataFrame:
+    def select_primary(group):
+        # If there's any row with primary_flag 'Y', choose the first one
+        if (group['primary_flag'] == 'Y').any():
+            return group[group['primary_flag'] == 'Y'].iloc[0]
+        else:
+            # For employees with only one department
+            return group.iloc[0]
+    
+    result = employees.groupby('employee_id').apply(select_primary).reset_index(drop=True)
+    return result[['employee_id', 'department_id']]
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Primary-Department
+│── README.md
+│── solution.sql
+│── solution_pandas.py
+│── test_cases.sql
+│── sample_data.csv
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/primary-department-for-each-employee/)
+- 🔍 [MySQL UNION Operator](https://www.w3schools.com/sql/sql_union.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+```
+
+This `README.md` includes:
+- A **clear problem statement** with table structure and example.
+- A detailed **SQL solution** with an explanation.
+- A detailed **Pandas (Python) solution** with an explanation.
+- An organized **file structure** and helpful external links.
+
+Let me know if you need any modifications!
\ No newline at end of file

From a9bf21ac2f732ee001f00735e67c07f441b27f60 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:56:38 +0530
Subject: [PATCH 333/353] Update readme.md

---
 .../readme.md                                        | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md
index bc876ce..db5ce3b 100644
--- a/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md	
+++ b/LeetCode SQL 50 Solution/1789. Primary Department for Each Employee/readme.md	
@@ -1,6 +1,3 @@
-Below is a well-structured `README.md` for **LeetCode 1789 - Primary Department for Each Employee**, including both the SQL and Pandas (Python) solutions:
-
-```md
 # 🏢 Primary Department for Each Employee - LeetCode 1789
 
 ## 📌 Problem Statement
@@ -129,12 +126,3 @@ def primary_department(employees: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/primary-department-for-each-employee/)
 - 🔍 [MySQL UNION Operator](https://www.w3schools.com/sql/sql_union.asp)
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-```
-
-This `README.md` includes:
-- A **clear problem statement** with table structure and example.
-- A detailed **SQL solution** with an explanation.
-- A detailed **Pandas (Python) solution** with an explanation.
-- An organized **file structure** and helpful external links.
-
-Let me know if you need any modifications!
\ No newline at end of file

From 0b3070084d98f6f4be4f51a247bec087f7f355c8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:57:55 +0530
Subject: [PATCH 334/353] Update readme.md

---
 .../1907. Count Salary Categories/readme.md   | 197 ++++++++++++++++++
 1 file changed, 197 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md b/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md
index e69de29..2ef90fc 100644
--- a/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md	
+++ b/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md	
@@ -0,0 +1,197 @@
+Below is a well-structured `README.md` for **LeetCode 907 - Count Salary Categories**. It includes detailed explanations for the SQL solution using CTEs and a corresponding Pandas solution.
+
+```md
+# 💰 Count Salary Categories - LeetCode 907
+
+## 📌 Problem Statement
+You are given a table **Accounts** that contains information about bank accounts, including their monthly income.  
+Your task is to calculate the number of bank accounts in each salary category.
+
+The salary categories are defined as follows:
+- **"Low Salary"**: Salaries strictly less than \$20,000.
+- **"Average Salary"**: Salaries in the inclusive range [\$20,000, \$50,000].
+- **"High Salary"**: Salaries strictly greater than \$50,000.
+
+The result table must contain **all three categories**. If there are no accounts in a category, return 0.
+
+Return the result in **any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Accounts Table**
+| Column Name | Type |
+| ----------- | ---- |
+| account_id  | int  |
+| income      | int  |
+
+- `account_id` is the **primary key** for this table.
+- Each row contains the monthly income for one bank account.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Accounts Table**
+| account_id | income |
+| ---------- | ------ |
+| 3          | 108939 |
+| 2          | 12747  |
+| 8          | 87709  |
+| 6          | 91796  |
+
+### **Output:**
+| category       | accounts_count |
+| -------------- | -------------- |
+| Low Salary     | 1              |
+| Average Salary | 0              |
+| High Salary    | 3              |
+
+### **Explanation:**
+- **Low Salary**: Account with income 12747.
+- **Average Salary**: No accounts have an income in the range [20000, 50000].
+- **High Salary**: Accounts with incomes 108939, 87709, and 91796.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+1. **CTE "S"**: Create a static table with the three salary categories.
+   ```sql
+   WITH S AS (
+       SELECT 'Low Salary' AS category
+       UNION
+       SELECT 'Average Salary'
+       UNION
+       SELECT 'High Salary'
+   ),
+   ```
+   - This defines the three salary categories to ensure every category appears in the final result.
+
+2. **CTE "T"**: Categorize each account from the **Accounts** table using a `CASE` statement and count the number of accounts in each category.
+   ```sql
+   T AS (
+       SELECT
+           CASE
+               WHEN income < 20000 THEN 'Low Salary'
+               WHEN income > 50000 THEN 'High Salary'
+               ELSE 'Average Salary'
+           END AS category,
+           COUNT(1) AS accounts_count
+       FROM Accounts
+       GROUP BY 1
+   )
+   ```
+   - The `CASE` statement assigns a salary category based on the income.
+   - `COUNT(1)` counts the number of accounts in each category.
+
+3. **Final SELECT with LEFT JOIN**: Combine the static category table `S` with the computed counts from `T` to ensure every category is included, using `IFNULL` to convert any missing count to 0.
+   ```sql
+   SELECT S.category, IFNULL(T.accounts_count, 0) AS accounts_count
+   FROM S
+   LEFT JOIN T USING (category);
+   ```
+
+### ✅ **Complete SQL Query:**
+```sql
+WITH S AS (
+    SELECT 'Low Salary' AS category
+    UNION
+    SELECT 'Average Salary'
+    UNION
+    SELECT 'High Salary'
+),
+T AS (
+    SELECT
+        CASE
+            WHEN income < 20000 THEN 'Low Salary'
+            WHEN income > 50000 THEN 'High Salary'
+            ELSE 'Average Salary'
+        END AS category,
+        COUNT(1) AS accounts_count
+    FROM Accounts
+    GROUP BY 1
+)
+SELECT S.category, IFNULL(T.accounts_count, 0) AS accounts_count
+FROM S
+LEFT JOIN T USING (category);
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. **Categorize Accounts**: Create a new column `category` in the DataFrame by applying the salary conditions.
+2. **Group and Count**: Group by the `category` column and count the number of accounts.
+3. **Merge with Static Categories**: Ensure all three salary categories appear by merging with a predefined DataFrame that contains all categories, filling missing counts with 0.
+
+```python
+import pandas as pd
+
+def count_salary_categories(accounts: pd.DataFrame) -> pd.DataFrame:
+    # Define the salary categorization function
+    def categorize(income):
+        if income < 20000:
+            return 'Low Salary'
+        elif income > 50000:
+            return 'High Salary'
+        else:
+            return 'Average Salary'
+    
+    # Apply categorization
+    accounts['category'] = accounts['income'].apply(categorize)
+    
+    # Count accounts in each category
+    counts = accounts.groupby('category').size().reset_index(name='accounts_count')
+    
+    # Define static categories DataFrame
+    categories = pd.DataFrame({
+        'category': ['Low Salary', 'Average Salary', 'High Salary']
+    })
+    
+    # Merge to ensure all categories are present, fill missing values with 0
+    result = categories.merge(counts, on='category', how='left')
+    result['accounts_count'] = result['accounts_count'].fillna(0).astype(int)
+    
+    return result
+
+# Example usage:
+# df = pd.read_csv("sample_accounts.csv")
+# print(count_salary_categories(df))
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Count-Salary-Categories
+│── README.md
+│── solution.sql
+│── solution_pandas.py
+│── test_cases.sql
+│── sample_accounts.csv
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/count-salary-categories/)
+- 📝 [MySQL WITH Clause (CTE)](https://www.w3schools.com/sql/sql_with.asp)
+- 🔍 [MySQL IFNULL Function](https://www.w3schools.com/sql/func_mysql_ifnull.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+```
+
+---
+
+### Features of this `README.md`:
+- **Clear Problem Statement** with table structure and detailed example.
+- **SQL Solution** with detailed explanation using CTEs and LEFT JOIN.
+- **Python (Pandas) Solution** with step-by-step categorization and merging.
+- **Organized File Structure** for repository management.
+- **Helpful External Links** for further learning.
+
+Let me know if you need any modifications or additional details!
\ No newline at end of file

From eb83cae9a65d6c10c2db441d9c8fb851f7615408 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:58:07 +0530
Subject: [PATCH 335/353] Update readme.md

---
 .../1907. Count Salary Categories/readme.md         | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md b/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md
index 2ef90fc..e5fc42d 100644
--- a/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md	
+++ b/LeetCode SQL 50 Solution/1907. Count Salary Categories/readme.md	
@@ -1,6 +1,3 @@
-Below is a well-structured `README.md` for **LeetCode 907 - Count Salary Categories**. It includes detailed explanations for the SQL solution using CTEs and a corresponding Pandas solution.
-
-```md
 # 💰 Count Salary Categories - LeetCode 907
 
 ## 📌 Problem Statement
@@ -185,13 +182,3 @@ def count_salary_categories(accounts: pd.DataFrame) -> pd.DataFrame:
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
 ```
 
----
-
-### Features of this `README.md`:
-- **Clear Problem Statement** with table structure and detailed example.
-- **SQL Solution** with detailed explanation using CTEs and LEFT JOIN.
-- **Python (Pandas) Solution** with step-by-step categorization and merging.
-- **Organized File Structure** for repository management.
-- **Helpful External Links** for further learning.
-
-Let me know if you need any modifications or additional details!
\ No newline at end of file

From 2a246d453a93ac0cd41f8b7b127cb67975ceaca1 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:58:25 +0530
Subject: [PATCH 336/353] Update readme.md

---
 .../1934. Confirmation Rate/readme.md         | 187 ++++++++++++++++++
 1 file changed, 187 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md b/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md
index e69de29..22e7ab7 100644
--- a/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md	
+++ b/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md	
@@ -0,0 +1,187 @@
+Below is a well-structured `README.md` file for **LeetCode 1934 - Confirmation Rate**, including detailed explanations for the SQL solution using both the shorthand and CASE statement approaches, along with a Pandas (Python) solution.
+
+```md
+# 📩 Confirmation Rate - LeetCode 1934
+
+## 📌 Problem Statement
+You are given two tables: **Signups** and **Confirmations**.
+
+- The **Signups** table contains the signup time for each user.
+- The **Confirmations** table records each confirmation request made by a user along with the outcome (either `'confirmed'` or `'timeout'`).
+
+The **confirmation rate** for a user is defined as:
+\[
+\text{confirmation rate} = \frac{\text{Number of confirmed messages}}{\text{Total number of confirmation requests}}
+\]
+If a user did not request any confirmation messages, their confirmation rate is defined as 0.
+
+Your task is to calculate the confirmation rate for each user and round it to two decimal places.
+
+Return the result table in **any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Signups Table**
+| Column Name | Type     |
+| ----------- | -------- |
+| user_id     | int      |
+| time_stamp  | datetime |
+
+- `user_id` is unique for each user.
+
+### **Confirmations Table**
+| Column Name | Type     |
+| ----------- | -------- |
+| user_id     | int      |
+| time_stamp  | datetime |
+| action      | ENUM     |
+
+- `(user_id, time_stamp)` is the primary key.
+- `action` is either `'confirmed'` or `'timeout'`.
+- `user_id` in Confirmations is a foreign key to Signups.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Signups Table**
+| user_id | time_stamp          |
+| ------- | ------------------- |
+| 3       | 2020-03-21 10:16:13 |
+| 7       | 2020-01-04 13:57:59 |
+| 2       | 2020-07-29 23:09:44 |
+| 6       | 2020-12-09 10:39:37 |
+
+#### **Confirmations Table**
+| user_id | time_stamp          | action    |
+| ------- | ------------------- | --------- |
+| 3       | 2021-01-06 03:30:46 | timeout   |
+| 3       | 2021-07-14 14:00:00 | timeout   |
+| 7       | 2021-06-12 11:57:29 | confirmed |
+| 7       | 2021-06-13 12:58:28 | confirmed |
+| 7       | 2021-06-14 13:59:27 | confirmed |
+| 2       | 2021-01-22 00:00:00 | confirmed |
+| 2       | 2021-02-28 23:59:59 | timeout   |
+
+### **Output:**
+| user_id | confirmation_rate |
+| ------- | ----------------- |
+| 6       | 0.00              |
+| 3       | 0.00              |
+| 7       | 1.00              |
+| 2       | 0.50              |
+
+### **Explanation:**
+- **User 6** did not request any confirmation messages, so the rate is **0.00**.
+- **User 3** made 2 requests; both were timeouts, so the rate is **0.00**.
+- **User 7** made 3 requests; all were confirmed, so the rate is **1.00**.
+- **User 2** made 2 requests; 1 confirmed and 1 timeout, so the rate is **0.50**.
+
+---
+
+## 🖥 SQL Solutions
+
+### ✅ **Solution 1: Using Shorthand Boolean Expressions**
+#### **Explanation:**
+- `SUM(action = 'confirmed')` counts the number of rows where the action is `'confirmed'` (in MySQL, boolean expressions return 1 if true, 0 if false).
+- `COUNT(1)` counts all confirmation requests.
+- We use a `LEFT JOIN` between **Signups** and **Confirmations** so that users without any confirmation requests are included (their rate becomes 0).
+- `IFNULL` is used to handle cases where a user has no confirmation requests.
+
+```sql
+SELECT
+    user_id,
+    ROUND(IFNULL(SUM(action = 'confirmed') / COUNT(1), 0), 2) AS confirmation_rate
+FROM Signups
+LEFT JOIN Confirmations USING (user_id)
+GROUP BY user_id;
+```
+
+### ✅ **Solution 2: Using a CASE Statement**
+#### **Explanation:**
+- The `CASE` statement explicitly counts 1 for `'confirmed'` actions and 0 otherwise.
+- The rest of the query logic remains similar.
+
+```sql
+SELECT
+    user_id,
+    ROUND(IFNULL(SUM(CASE WHEN action = 'confirmed' THEN 1 ELSE 0 END) / COUNT(1), 0), 2) AS confirmation_rate
+FROM Signups
+LEFT JOIN Confirmations USING (user_id)
+GROUP BY user_id;
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. **Merge** the **Signups** and **Confirmations** DataFrames on `user_id` using a left join, so that all users are included.
+2. **Count** the total number of confirmation requests and the number of confirmed requests for each user.
+3. **Calculate** the confirmation rate as the number of confirmed requests divided by the total requests.
+4. **Handle** users with no confirmation requests by setting their rate to 0.
+5. **Round** the confirmation rate to two decimal places.
+
+```python
+import pandas as pd
+
+def confirmation_rate(signups: pd.DataFrame, confirmations: pd.DataFrame) -> pd.DataFrame:
+    # Merge the dataframes to include all users from signups
+    merged = pd.merge(signups, confirmations, on='user_id', how='left')
+    
+    # Group by user_id and calculate total requests and confirmed requests
+    summary = merged.groupby('user_id').agg(
+        total_requests=('action', 'count'),
+        confirmed_requests=('action', lambda x: (x == 'confirmed').sum())
+    ).reset_index()
+    
+    # Calculate confirmation rate; if total_requests is 0, rate is 0.
+    summary['confirmation_rate'] = summary.apply(
+        lambda row: round(row['confirmed_requests'] / row['total_requests'], 2) if row['total_requests'] > 0 else 0.00,
+        axis=1
+    )
+    
+    # Select the relevant columns
+    result = summary[['user_id', 'confirmation_rate']]
+    return result
+
+# Example usage:
+# signups_df = pd.read_csv("signups.csv")
+# confirmations_df = pd.read_csv("confirmations.csv")
+# print(confirmation_rate(signups_df, confirmations_df))
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Confirmation-Rate
+│── README.md
+│── solution.sql
+│── solution_pandas.py
+│── test_cases.sql
+│── sample_signups.csv
+│── sample_confirmations.csv
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/confirmation-rate/)
+- 📝 [MySQL IFNULL Function](https://www.w3schools.com/sql/func_mysql_ifnull.asp)
+- 🔍 [MySQL ROUND Function](https://www.w3schools.com/sql/func_mysql_round.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+```
+
+---
+
+### Features of this `README.md`:
+- **Clear Problem Statement** with detailed table structures and examples.
+- **SQL Solutions** with both a shorthand boolean expression and a CASE statement approach, complete with explanations.
+- **Python (Pandas) Solution** with step-by-step instructions on merging, grouping, and calculating the confirmation rate.
+- **Organized File Structure** and helpful external links for further learning.
+
+Let me know if you need any further modifications or additions!
\ No newline at end of file

From 7cfa939577874b930c570fa48a9a812743d9052d Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:58:44 +0530
Subject: [PATCH 337/353] Update readme.md

---
 .../1934. Confirmation Rate/readme.md              | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md b/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md
index 22e7ab7..eab61d4 100644
--- a/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md	
+++ b/LeetCode SQL 50 Solution/1934. Confirmation Rate/readme.md	
@@ -1,6 +1,3 @@
-Below is a well-structured `README.md` file for **LeetCode 1934 - Confirmation Rate**, including detailed explanations for the SQL solution using both the shorthand and CASE statement approaches, along with a Pandas (Python) solution.
-
-```md
 # 📩 Confirmation Rate - LeetCode 1934
 
 ## 📌 Problem Statement
@@ -174,14 +171,3 @@ def confirmation_rate(signups: pd.DataFrame, confirmations: pd.DataFrame) -> pd.
 - 📝 [MySQL IFNULL Function](https://www.w3schools.com/sql/func_mysql_ifnull.asp)
 - 🔍 [MySQL ROUND Function](https://www.w3schools.com/sql/func_mysql_round.asp)
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-```
-
----
-
-### Features of this `README.md`:
-- **Clear Problem Statement** with detailed table structures and examples.
-- **SQL Solutions** with both a shorthand boolean expression and a CASE statement approach, complete with explanations.
-- **Python (Pandas) Solution** with step-by-step instructions on merging, grouping, and calculating the confirmation rate.
-- **Organized File Structure** and helpful external links for further learning.
-
-Let me know if you need any further modifications or additions!
\ No newline at end of file

From 5d578c397a099ed5f3500d904a3fab9c7e2f8ccb Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 11:59:57 +0530
Subject: [PATCH 338/353] Update readme.md

---
 .../readme.md                                 | 159 ++++++++++++++++++
 1 file changed, 159 insertions(+)

diff --git a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md
index e69de29..2fb26b2 100644
--- a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md	
+++ b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md	
@@ -0,0 +1,159 @@
+Below is a well-structured `README.md` for **LeetCode 1978 - Employees Whose Manager Left the Company**. It includes a detailed explanation of the SQL solution and a corresponding Python (Pandas) solution.
+
+```md
+# 🏢 Employees Whose Manager Left the Company - LeetCode 1978
+
+## 📌 Problem Statement
+You are given a table **Employees** that contains information about employees, including their salary and the ID of the manager they report to.  
+When a manager leaves the company, their row is deleted from the **Employees** table, but the `manager_id` in the records of their reports still remains.
+
+Your task is to **find the IDs of employees** who:
+- Have a salary **strictly less than $30000**.
+- Have a **manager** (i.e., `manager_id` is not `NULL`) whose record is **missing** in the table (i.e., the manager left the company).
+
+Return the result table **ordered by `employee_id`** in ascending order.
+
+---
+
+## 📊 Table Structure
+
+### **Employees Table**
+| Column Name | Type    |
+| ----------- | ------- |
+| employee_id | int     |
+| name        | varchar |
+| manager_id  | int     |
+| salary      | int     |
+
+- `employee_id` is the **primary key**.
+- `manager_id` may be `NULL` if an employee does not have a manager.
+- When a manager leaves, their row is deleted, but the `manager_id` remains in the reports' records.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Employees Table**
+| employee_id | name      | manager_id | salary |
+| ----------- | --------- | ---------- | ------ |
+| 3           | Mila      | 9          | 60301  |
+| 12          | Antonella | NULL       | 31000  |
+| 13          | Emery     | NULL       | 67084  |
+| 1           | Kalel     | 11         | 21241  |
+| 9           | Mikaela   | NULL       | 50937  |
+| 11          | Joziah    | 6          | 28485  |
+
+### **Output:**
+| employee_id |
+| ----------- |
+| 11          |
+
+### **Explanation:**
+- **Employees with salary < $30000:**  
+  - **Kalel (ID 1)** with salary 21241, whose manager is employee 11.
+  - **Joziah (ID 11)** with salary 28485, whose manager is employee 6.
+- **Kalel's manager (ID 11)** is still in the table.  
+- **Joziah's manager (ID 6)** is missing from the table, meaning that manager left the company.  
+Thus, only **employee 11 (Joziah)** meets the criteria.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+1. **Self-Join:**  
+   - Use a `LEFT JOIN` on the **Employees** table with itself to check if an employee's manager exists.
+   - Alias `e1` represents the employee, and alias `e2` represents the manager.
+2. **Filter Conditions:**
+   - The employee's `salary` must be strictly less than 30000.
+   - The employee must have a manager (`e1.manager_id IS NOT NULL`).
+   - The join should fail for the manager (`e2.employee_id IS NULL`), indicating the manager left.
+3. **Order the Result:**  
+   - Order the final result by `employee_id`.
+
+### ✅ **SQL Query:**
+```sql
+SELECT e1.employee_id
+FROM Employees AS e1
+LEFT JOIN Employees AS e2 ON e1.manager_id = e2.employee_id
+WHERE e1.salary < 30000
+  AND e1.manager_id IS NOT NULL
+  AND e2.employee_id IS NULL
+ORDER BY e1.employee_id;
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. **Self-Merge:**  
+   - Merge the **Employees** DataFrame with itself on `manager_id` (from the employee side) and `employee_id` (from the manager side) using a left join.
+2. **Filter Rows:**  
+   - Keep rows where:
+     - `salary` is less than 30000.
+     - `manager_id` is not null.
+     - The merged manager information is missing (i.e., the manager left).
+3. **Sort Result:**  
+   - Sort the result by `employee_id`.
+
+### ✅ **Pandas Code:**
+```python
+import pandas as pd
+
+def employees_with_left_manager(employees: pd.DataFrame) -> pd.DataFrame:
+    # Perform a left merge on the Employees table to find existing managers
+    merged = employees.merge(
+        employees[['employee_id']], 
+        left_on='manager_id', 
+        right_on='employee_id', 
+        how='left', 
+        suffixes=('', '_manager')
+    )
+    
+    # Filter: salary < 30000, manager_id is not null, and manager does not exist (NaN in employee_id_manager)
+    filtered = merged[
+        (merged['salary'] < 30000) &
+        (merged['manager_id'].notnull()) &
+        (merged['employee_id_manager'].isna())
+    ]
+    
+    # Select the required column and sort by employee_id
+    result = filtered[['employee_id']].sort_values('employee_id')
+    return result
+
+# Example usage:
+# employees_df = pd.read_csv("employees.csv")
+# print(employees_with_left_manager(employees_df))
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Employees-Manager-Left
+│── README.md
+│── solution.sql
+│── solution_pandas.py
+│── test_cases.sql
+│── sample_data.csv
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/employees-whose-manager-left-the-company/)
+- 🔍 [MySQL LEFT JOIN Documentation](https://www.w3schools.com/sql/sql_join_left.asp)
+- 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
+```
+
+---
+
+### Features of this `README.md`:
+- **Clear problem description** with table structure and an example.
+- **SQL solution** with step-by-step explanation.
+- **Python (Pandas) solution** with detailed code and explanation.
+- **Organized file structure** and useful external links for further learning.
+
+Let me know if you need any further modifications or additions!
\ No newline at end of file

From a8cb4305909534ba704da41a3f18db8d8c9f2c56 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:00:08 +0530
Subject: [PATCH 339/353] Update readme.md

---
 .../readme.md                                        | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md
index 2fb26b2..66aea43 100644
--- a/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md	
+++ b/LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company/readme.md	
@@ -1,6 +1,3 @@
-Below is a well-structured `README.md` for **LeetCode 1978 - Employees Whose Manager Left the Company**. It includes a detailed explanation of the SQL solution and a corresponding Python (Pandas) solution.
-
-```md
 # 🏢 Employees Whose Manager Left the Company - LeetCode 1978
 
 ## 📌 Problem Statement
@@ -148,12 +145,3 @@ def employees_with_left_manager(employees: pd.DataFrame) -> pd.DataFrame:
 - 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
 ```
 
----
-
-### Features of this `README.md`:
-- **Clear problem description** with table structure and an example.
-- **SQL solution** with step-by-step explanation.
-- **Python (Pandas) solution** with detailed code and explanation.
-- **Organized file structure** and useful external links for further learning.
-
-Let me know if you need any further modifications or additions!
\ No newline at end of file

From 57e97a3c62ebc5fc841d1ea4ac5085b21ac586b3 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:01:11 +0530
Subject: [PATCH 340/353] Update readme.md

---
 .../readme.md                                 | 128 ++++++++++++++++++
 1 file changed, 128 insertions(+)

diff --git a/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md
index e69de29..d7a4983 100644
--- a/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md	
+++ b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md	
@@ -0,0 +1,128 @@
+Below is a well-structured `README.md` for **LeetCode 2356 - Number of Unique Subjects Taught by Each Teacher**, which includes both the SQL solution and a Python (Pandas) solution.
+
+```md
+# 📚 Number of Unique Subjects Taught by Each Teacher - LeetCode 2356
+
+## 📌 Problem Statement
+You are given a table **Teacher** that provides information about the subjects taught by teachers in various departments of a university.
+
+Your task is to calculate the **number of unique subjects** each teacher teaches.  
+Note that the table can have multiple rows for the same subject taught in different departments, but you should count each subject only once per teacher.
+
+Return the result table in **any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Teacher Table**
+| Column Name | Type |
+| ----------- | ---- |
+| teacher_id  | int  |
+| subject_id  | int  |
+| dept_id     | int  |
+
+- `(subject_id, dept_id)` is the **primary key**.
+- Each row indicates that the teacher with `teacher_id` teaches the subject `subject_id` in the department `dept_id`.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Teacher Table**
+| teacher_id | subject_id | dept_id |
+| ---------- | ---------- | ------- |
+| 1          | 2          | 3       |
+| 1          | 2          | 4       |
+| 1          | 3          | 3       |
+| 2          | 1          | 1       |
+| 2          | 2          | 1       |
+| 2          | 3          | 1       |
+| 2          | 4          | 1       |
+
+### **Output:**
+| teacher_id | cnt |
+| ---------- | --- |
+| 1          | 2   |
+| 2          | 4   |
+
+### **Explanation:**
+- **Teacher 1:**  
+  - Teaches subject **2** (in departments 3 and 4) and subject **3** (in department 3).  
+  - Unique subjects = {2, 3} → **2 subjects**.
+- **Teacher 2:**  
+  - Teaches subjects **1**, **2**, **3**, and **4** (all in department 1).  
+  - Unique subjects = {1, 2, 3, 4} → **4 subjects**.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+- Use `COUNT(DISTINCT subject_id)` to count the number of unique subjects taught by each teacher.
+- Group the results by `teacher_id`.
+
+```sql
+SELECT teacher_id, COUNT(DISTINCT subject_id) AS cnt
+FROM Teacher
+GROUP BY teacher_id;
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. **Group by `teacher_id`:**  
+   - Group the DataFrame by `teacher_id`.
+2. **Count Unique Subjects:**  
+   - Use the `nunique()` function on the `subject_id` column within each group to count unique subjects.
+3. **Reset Index and Rename:**  
+   - Reset the index and rename the column appropriately.
+
+```python
+import pandas as pd
+
+def count_unique_subjects(teacher: pd.DataFrame) -> pd.DataFrame:
+    # Group by teacher_id and count unique subject_id values
+    result = teacher.groupby('teacher_id')['subject_id'].nunique().reset_index()
+    result = result.rename(columns={'subject_id': 'cnt'})
+    return result
+
+# Example usage:
+# teacher_df = pd.DataFrame({
+#     'teacher_id': [1, 1, 1, 2, 2, 2, 2],
+#     'subject_id': [2, 2, 3, 1, 2, 3, 4],
+#     'dept_id': [3, 4, 3, 1, 1, 1, 1]
+# })
+# print(count_unique_subjects(teacher_df))
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Unique-Subjects-Per-Teacher
+│── README.md
+│── solution.sql
+│── solution_pandas.py
+│── test_cases.sql
+│── sample_data.csv
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/number-of-unique-subjects-taught-by-each-teacher/)
+- 🔍 [MySQL COUNT(DISTINCT) Documentation](https://www.w3schools.com/sql/sql_count_distinct.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+```
+
+This `README.md` provides:
+- A **clear problem statement** along with the table structure.
+- A **detailed example** with explanation.
+- Both **SQL and Python (Pandas) solutions** with step-by-step approaches.
+- An **organized file structure** and helpful external links.
+
+Let me know if you need any modifications!
\ No newline at end of file

From a0715298fc15f18fa05e8bad49f27a7d991a0223 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:01:19 +0530
Subject: [PATCH 341/353] Update readme.md

---
 .../readme.md                                            | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md
index d7a4983..8f7040e 100644
--- a/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md	
+++ b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md	
@@ -117,12 +117,3 @@ def count_unique_subjects(teacher: pd.DataFrame) -> pd.DataFrame:
 - 📖 [LeetCode Problem](https://leetcode.com/problems/number-of-unique-subjects-taught-by-each-teacher/)
 - 🔍 [MySQL COUNT(DISTINCT) Documentation](https://www.w3schools.com/sql/sql_count_distinct.asp)
 - 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
-```
-
-This `README.md` provides:
-- A **clear problem statement** along with the table structure.
-- A **detailed example** with explanation.
-- Both **SQL and Python (Pandas) solutions** with step-by-step approaches.
-- An **organized file structure** and helpful external links.
-
-Let me know if you need any modifications!
\ No newline at end of file

From c33fa4f5cb65aad92fe0502d1f444beb586dfe0f Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:01:29 +0530
Subject: [PATCH 342/353] Update readme.md

---
 .../readme.md                                                  | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md
index 8f7040e..e752e35 100644
--- a/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md	
+++ b/LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md	
@@ -1,6 +1,3 @@
-Below is a well-structured `README.md` for **LeetCode 2356 - Number of Unique Subjects Taught by Each Teacher**, which includes both the SQL solution and a Python (Pandas) solution.
-
-```md
 # 📚 Number of Unique Subjects Taught by Each Teacher - LeetCode 2356
 
 ## 📌 Problem Statement

From 16ca2730fdcf3d42e05e60567f2c6f6f5d434405 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:06:47 +0530
Subject: [PATCH 343/353] Update readme.md

---
 .../prosnal_database/readme.md                | 223 ++++++++++++++++++
 1 file changed, 223 insertions(+)

diff --git a/LeetCode SQL 50 Solution/prosnal_database/readme.md b/LeetCode SQL 50 Solution/prosnal_database/readme.md
index e69de29..3208f75 100644
--- a/LeetCode SQL 50 Solution/prosnal_database/readme.md	
+++ b/LeetCode SQL 50 Solution/prosnal_database/readme.md	
@@ -0,0 +1,223 @@
+Below is a well-structured `README.md` that documents the database schema, sample data, and queries for the provided SQL code. This file can be added to your repository for clarity and organization.
+
+```md
+# Prosnal Database Example
+
+This repository contains SQL scripts for creating and populating a sample database called `prosnal_database`. The database includes multiple tables and queries demonstrating joins and calculations. The following sections describe the database schema, sample data insertion, and example queries.
+
+---
+
+## Database and Tables
+
+### 1. Create and Use Database
+```sql
+CREATE DATABASE IF NOT EXISTS prosnal_database;
+USE prosnal_database;
+```
+- This command creates the database `prosnal_database` if it does not exist and sets it as the current working database.
+
+---
+
+### 2. Employees Table
+```sql
+CREATE TABLE IF NOT EXISTS Employees (
+    Id INT,
+    Name VARCHAR(255),
+    Salary INT,
+    Department VARCHAR(255)
+);
+```
+- **Description:**  
+  Contains employee information such as `Id`, `Name`, `Salary`, and `Department`.
+
+#### Sample Data:
+```sql
+INSERT INTO Employees (Id, Name, Salary, Department)
+VALUES (1, 'John Doe', 50000, 'HR'),
+       (2, 'Jane Smith', 60000, 'Finance'),
+       (3, 'Mike Johnson', 70000, 'IT'),
+       (4, 'Sarah Black', 60000, 'Finance'),
+       (5, 'David White', 70000, 'IT');
+```
+
+---
+
+### 3. Projects Table
+```sql
+CREATE TABLE IF NOT EXISTS Projects (
+    Id INT,
+    Name VARCHAR(255),
+    Department VARCHAR(255)
+);
+```
+- **Description:**  
+  Contains project details including project `Id`, `Name`, and the corresponding `Department`.
+
+#### Sample Data:
+```sql
+INSERT INTO Projects (Id, Name, Department)
+VALUES (1, 'Project A', 'IT'),
+       (2, 'Project B', 'Finance'),
+       (3, 'Project C', 'IT');
+```
+
+---
+
+### 4. EmployeeProjects Table
+```sql
+CREATE TABLE IF NOT EXISTS EmployeeProjects (
+    EmployeeId INT,
+    ProjectId INT
+);
+```
+- **Description:**  
+  Associates employees with projects.
+
+#### Sample Data:
+```sql
+INSERT INTO EmployeeProjects (EmployeeId, ProjectId)
+VALUES (1, 1),
+       (1, 2),
+       (2, 1),
+       (3, 1),
+       (3, 3),
+       (4, 2),
+       (5, 1);
+```
+
+---
+
+### 5. Users and Sessions Tables
+#### Users Table
+```sql
+CREATE TABLE users (
+    id INT PRIMARY KEY,
+    name VARCHAR(50),
+    email VARCHAR(100)
+);
+```
+- **Description:**  
+  Contains user information with unique `id`, `name`, and `email`.
+
+#### Sample Data:
+```sql
+INSERT INTO users (id, name, email)
+VALUES (1, 'John Doe', 'john.doe@example.com'),
+       (2, 'Jane Smith', 'antima@example.com');
+```
+
+#### Sessions Table
+```sql
+CREATE TABLE sessions (
+    id INT PRIMARY KEY,
+    user_id INT,
+    session_date DATE
+);
+```
+- **Description:**  
+  Records session data with session `id`, associated `user_id`, and the `session_date`.
+
+#### Sample Data:
+```sql
+INSERT INTO sessions (id, user_id, session_date)
+VALUES (1, 1, '2022-01-01'),
+       (2, 1, '2022-01-02'),
+       (3, 2, '2022-01-03');
+```
+
+---
+
+### 6. Products and Orders Tables
+#### Products Table
+```sql
+CREATE TABLE IF NOT EXISTS products (
+    id INT PRIMARY KEY,
+    name VARCHAR(50),
+    price DECIMAL(10, 2)
+);
+```
+- **Description:**  
+  Contains product details such as `id`, `name`, and `price`.
+
+#### Sample Data:
+```sql
+INSERT INTO products (id, name, price)
+VALUES (1, 'Product A', 10.99),
+       (2, 'Product B', 20.99),
+       (3, 'Product C', 30.99);
+```
+
+#### Orders Table
+```sql
+CREATE TABLE IF NOT EXISTS orders (
+    id INT PRIMARY KEY,
+    product_id INT,
+    quantity INT
+);
+```
+- **Description:**  
+  Stores order details including order `id`, associated `product_id`, and order `quantity`.
+
+#### Sample Data:
+```sql
+INSERT INTO orders (id, product_id, quantity)
+VALUES (1, 1, 5),
+       (2, 2, 3),
+       (3, 3, 1),
+       (4, 1, 2),
+       (5, 2, 4);
+```
+
+---
+
+## Example Queries
+
+### Query 1: List Employee Projects
+Join the **Employees**, **EmployeeProjects**, and **Projects** tables to list each employee's project details.
+```sql
+SELECT e.Name AS EmployeeName, e.Department, p.Name AS ProjectName, p.Department
+FROM Employees e
+JOIN EmployeeProjects ep ON e.Id = ep.EmployeeId
+JOIN Projects p ON ep.ProjectId = p.Id;
+```
+
+### Query 2: List User Sessions
+Join the **users** and **sessions** tables to list the session dates for each user.
+```sql
+SELECT u.name AS user_name, s.session_date
+FROM users u
+JOIN sessions s ON u.id = s.user_id;
+```
+
+### Query 3: Calculate Order Totals
+Join the **products** and **orders** tables to calculate the total price for each order.
+```sql
+SELECT p.name AS product_name, o.quantity, p.price * o.quantity AS total_price
+FROM products p
+JOIN orders o ON p.id = o.product_id;
+```
+
+---
+
+## File Structure
+```
+Prosnal_Database/
+│── README.md
+│── schema.sql        -- Contains all CREATE TABLE and INSERT statements.
+│── queries.sql       -- Contains sample SELECT queries.
+│── sample_data.csv   -- (Optional) CSV files for sample data.
+```
+
+---
+
+## Useful Links
+- [MySQL Documentation](https://dev.mysql.com/doc/)
+- [W3Schools SQL Tutorial](https://www.w3schools.com/sql/)
+- [Pandas Documentation](https://pandas.pydata.org/docs/)
+
+---
+
+This `README.md` provides a comprehensive overview of the database creation, data insertion, and query examples. Let me know if you need any modifications or further details!
+```
+
+This documentation should help users understand the purpose of each table and query, and it organizes your code in a clear and accessible way.
\ No newline at end of file

From 7f863520404354a62925de32ff8e450ca2d54534 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:11:16 +0530
Subject: [PATCH 344/353] Update readme.md

---
 LeetCode SQL 50 Solution/prosnal_database/readme.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/LeetCode SQL 50 Solution/prosnal_database/readme.md b/LeetCode SQL 50 Solution/prosnal_database/readme.md
index 3208f75..2fed417 100644
--- a/LeetCode SQL 50 Solution/prosnal_database/readme.md	
+++ b/LeetCode SQL 50 Solution/prosnal_database/readme.md	
@@ -1,6 +1,4 @@
-Below is a well-structured `README.md` that documents the database schema, sample data, and queries for the provided SQL code. This file can be added to your repository for clarity and organization.
 
-```md
 # Prosnal Database Example
 
 This repository contains SQL scripts for creating and populating a sample database called `prosnal_database`. The database includes multiple tables and queries demonstrating joins and calculations. The following sections describe the database schema, sample data insertion, and example queries.

From d332ac2cd65f0b00dd6581a3ce3f9a49ff8cfd83 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:25:15 +0530
Subject: [PATCH 345/353] Update prosnal_database.py

---
 .../prosnal_database/prosnal_database.py      | 141 ++++++++++++++++++
 1 file changed, 141 insertions(+)

diff --git a/LeetCode SQL 50 Solution/prosnal_database/prosnal_database.py b/LeetCode SQL 50 Solution/prosnal_database/prosnal_database.py
index e69de29..96e3009 100644
--- a/LeetCode SQL 50 Solution/prosnal_database/prosnal_database.py	
+++ b/LeetCode SQL 50 Solution/prosnal_database/prosnal_database.py	
@@ -0,0 +1,141 @@
+Below is a complete Python solution using a class with methods to solve the problem with Pandas. This code creates the data (mimicking the SQL tables), then provides methods to run each query.
+
+```python
+import pandas as pd
+
+class ProsnalDatabase:
+    def __init__(self):
+        # Create tables (as Pandas DataFrames)
+        self.create_tables()
+
+    def create_tables(self):
+        # Employees Table
+        self.employees = pd.DataFrame({
+            'Id': [1, 2, 3, 4, 5],
+            'Name': ['John Doe', 'Jane Smith', 'Mike Johnson', 'Sarah Black', 'David White'],
+            'Salary': [50000, 60000, 70000, 60000, 70000],
+            'Department': ['HR', 'Finance', 'IT', 'Finance', 'IT']
+        })
+
+        # Projects Table
+        self.projects = pd.DataFrame({
+            'Id': [1, 2, 3],
+            'Name': ['Project A', 'Project B', 'Project C'],
+            'Department': ['IT', 'Finance', 'IT']
+        })
+
+        # EmployeeProjects Table
+        self.employee_projects = pd.DataFrame({
+            'EmployeeId': [1, 1, 2, 3, 3, 4, 5],
+            'ProjectId': [1, 2, 1, 1, 3, 2, 1]
+        })
+
+        # Users Table
+        self.users = pd.DataFrame({
+            'id': [1, 2],
+            'name': ['John Doe', 'Jane Smith'],
+            'email': ['john.doe@example.com', 'antima@example.com']
+        })
+
+        # Sessions Table
+        self.sessions = pd.DataFrame({
+            'id': [1, 2, 3],
+            'user_id': [1, 1, 2],
+            'session_date': pd.to_datetime(['2022-01-01', '2022-01-02', '2022-01-03'])
+        })
+
+        # Products Table
+        self.products = pd.DataFrame({
+            'id': [1, 2, 3],
+            'name': ['Product A', 'Product B', 'Product C'],
+            'price': [10.99, 20.99, 30.99]
+        })
+
+        # Orders Table
+        self.orders = pd.DataFrame({
+            'id': [1, 2, 3, 4, 5],
+            'product_id': [1, 2, 3, 1, 2],
+            'quantity': [5, 3, 1, 2, 4]
+        })
+
+    def query_employee_projects(self):
+        """
+        Returns a DataFrame that lists each employee's project details.
+        This joins the Employees, EmployeeProjects, and Projects DataFrames.
+        """
+        # Merge Employees with EmployeeProjects
+        emp_proj = self.employees.merge(
+            self.employee_projects, left_on='Id', right_on='EmployeeId'
+        )
+        # Merge with Projects to get project details
+        emp_proj = emp_proj.merge(
+            self.projects, left_on='ProjectId', right_on='Id', suffixes=('_Employee', '_Project')
+        )
+        # Select and rename desired columns
+        result = emp_proj[['Name_Employee', 'Department_Employee', 'Name_Project', 'Department_Project']]
+        result = result.rename(columns={
+            'Name_Employee': 'EmployeeName',
+            'Department_Employee': 'EmployeeDepartment',
+            'Name_Project': 'ProjectName',
+            'Department_Project': 'ProjectDepartment'
+        })
+        return result
+
+    def query_user_sessions(self):
+        """
+        Returns a DataFrame that lists the session dates for each user.
+        This joins the Users and Sessions DataFrames.
+        """
+        user_sessions = self.users.merge(
+            self.sessions, left_on='id', right_on='user_id'
+        )
+        result = user_sessions[['name', 'session_date']].rename(columns={'name': 'user_name'})
+        return result
+
+    def query_order_totals(self):
+        """
+        Returns a DataFrame that calculates the total price for each order.
+        This joins the Products and Orders DataFrames and computes total price = price * quantity.
+        """
+        orders_merged = self.orders.merge(
+            self.products, left_on='product_id', right_on='id'
+        )
+        orders_merged['total_price'] = orders_merged['price'] * orders_merged['quantity']
+        result = orders_merged[['name', 'quantity', 'total_price']].rename(columns={'name': 'product_name'})
+        return result
+
+# Example usage:
+if __name__ == '__main__':
+    db = ProsnalDatabase()
+    
+    print("Employee Projects:")
+    print(db.query_employee_projects(), "\n")
+    
+    print("User Sessions:")
+    print(db.query_user_sessions(), "\n")
+    
+    print("Order Totals:")
+    print(db.query_order_totals())
+```
+
+### Explanation:
+- **Class Initialization (`__init__`)**:  
+  The `ProsnalDatabase` class initializes by calling `create_tables()`, which creates sample DataFrames for all tables.
+  
+- **`create_tables()`**:  
+  This method creates DataFrames for `Employees`, `Projects`, `EmployeeProjects`, `Users`, `Sessions`, `Products`, and `Orders` with sample data.
+  
+- **Query Methods**:
+  - **`query_employee_projects()`**:  
+    Merges the `employees`, `employee_projects`, and `projects` DataFrames to show which employee works on which project. Columns are renamed for clarity.
+    
+  - **`query_user_sessions()`**:  
+    Merges the `users` and `sessions` DataFrames to list session dates for each user.
+    
+  - **`query_order_totals()`**:  
+    Merges the `orders` DataFrame with the `products` DataFrame and calculates the total price for each order.
+
+- **Main Block**:  
+  An instance of `ProsnalDatabase` is created, and the query methods are executed to display results.
+
+This modular, class-based approach using Pandas helps encapsulate data and query logic in a single, easy-to-manage unit. Let me know if you need any further modifications!
\ No newline at end of file

From 10e5bb9b769006926305f65930c341cbc23f2b5c Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:57:21 +0530
Subject: [PATCH 346/353] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 52adbb5..2a3e6b9 100644
--- a/README.md
+++ b/README.md
@@ -50,5 +50,5 @@ Feel free to fork this repository, suggest improvements, or submit your own solu
 <img height="200px" src="./img/sql50.png">
 
 ### Progress
-🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩⬜⬜⬜⬜⬜ 75%
+🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩 100%
 

From 42a07b87a3b97ca6b27db90e0a5cf2b9342e36db Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Mon, 31 Mar 2025 12:58:37 +0530
Subject: [PATCH 347/353] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2a3e6b9..42d93d3 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ Feel free to fork this repository, suggest improvements, or submit your own solu
 
 ## Solved LeetCode SQL 50 Question
 
-<img src="./img/sql 50.gif">
+<a href="https://leetcode.com/u/antim_pal/"><img src="./img/sql 50.gif"></a>
 <img height="200px" src="./img/sql50.png">
 
 ### Progress

From 095d118eee83ddd70953140bba1f24e2288f4ec8 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 25 Apr 2025 15:02:31 +0530
Subject: [PATCH 348/353] Create topic.yml

---
 .github/workflows/topic.yml | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 .github/workflows/topic.yml

diff --git a/.github/workflows/topic.yml b/.github/workflows/topic.yml
new file mode 100644
index 0000000..78e0a1e
--- /dev/null
+++ b/.github/workflows/topic.yml
@@ -0,0 +1,35 @@
+# Visit https://github.com/lowlighter/metrics#-documentation for full reference
+name: Followup
+on:
+   # Schedule updates (daily)
+   schedule: [{ cron: "0 0 * * *" }]
+   # Lines below let you run workflow manually and on each commit
+   workflow_dispatch:
+   push: { branches: ["master", "main"] }
+jobs:
+   github-metrics:
+      runs-on: ubuntu-latest
+      permissions:
+         contents: write
+      steps:
+         - uses: lowlighter/metrics@latest
+           with:
+              # Your GitHub token
+              # The following scopes are required:
+              #  - public_access (default scope)
+              # The following additional scopes may be required:
+              #  - read:org      (for organization related metrics)
+              #  - read:user     (for user related data)
+              #  - read:packages (for some packages related data)
+              #  - repo          (optional, if you want to include private repositories)
+              token: ${{ secrets.METRICS_TOKEN }}
+
+              # Options
+              user: iamAntimPal
+              template: classic
+              base: header, activity, community, repositories, metadata
+              config_timezone: Germany/Berlin
+              plugin_followup: yes
+              plugin_followup_sections: repositories, user
+              plugin_followup_indepth: yes
+              plugin_followup_archived: no

From 17cb9ca67cbdb6bc9ad1599737ae7fd66b28a605 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 25 Apr 2025 15:08:04 +0530
Subject: [PATCH 349/353] Update topic.yml

---
 .github/workflows/topic.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/topic.yml b/.github/workflows/topic.yml
index 78e0a1e..6147ccf 100644
--- a/.github/workflows/topic.yml
+++ b/.github/workflows/topic.yml
@@ -22,6 +22,7 @@ jobs:
               #  - read:user     (for user related data)
               #  - read:packages (for some packages related data)
               #  - repo          (optional, if you want to include private repositories)
+              filename: SVG/followup.svg
               token: ${{ secrets.METRICS_TOKEN }}
 
               # Options

From 544d7fad58c6624e6bad6fc7b88aeb5e894535ff Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 25 Apr 2025 15:08:42 +0530
Subject: [PATCH 350/353] Create README.MD

---
 SVG/README.MD | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 SVG/README.MD

diff --git a/SVG/README.MD b/SVG/README.MD
new file mode 100644
index 0000000..941fc48
--- /dev/null
+++ b/SVG/README.MD
@@ -0,0 +1 @@
+# SVG

From e5ffe778ae1ce2bb72d7c6db4d2c85aed59b9930 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 25 Apr 2025 15:26:36 +0530
Subject: [PATCH 351/353] Update topic.yml

---
 .github/workflows/topic.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/topic.yml b/.github/workflows/topic.yml
index 6147ccf..f706f8d 100644
--- a/.github/workflows/topic.yml
+++ b/.github/workflows/topic.yml
@@ -23,7 +23,7 @@ jobs:
               #  - read:packages (for some packages related data)
               #  - repo          (optional, if you want to include private repositories)
               filename: SVG/followup.svg
-              token: ${{ secrets.METRICS_TOKEN }}
+              token: ${{ secrets.GH_METRICS_TOKEN }}
 
               # Options
               user: iamAntimPal

From e92547c96bd52235bf252811e9ceb65c25f010a2 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 25 Apr 2025 20:47:03 +0530
Subject: [PATCH 352/353] Update and rename topic.yml to followup.yml

---
 .github/workflows/followup.yml | 55 ++++++++++++++++++++++++++++++++++
 .github/workflows/topic.yml    | 36 ----------------------
 2 files changed, 55 insertions(+), 36 deletions(-)
 create mode 100644 .github/workflows/followup.yml
 delete mode 100644 .github/workflows/topic.yml

diff --git a/.github/workflows/followup.yml b/.github/workflows/followup.yml
new file mode 100644
index 0000000..5bcaaa8
--- /dev/null
+++ b/.github/workflows/followup.yml
@@ -0,0 +1,55 @@
+name: Generate Followup Metrics
+
+on:
+  schedule:
+    - cron: "0 0 * * *"         # 5:30 AM IST
+    - cron: "30 5 * * *"        # 10:00 AM IST
+    - cron: "30 11 * * *"       # 4:00 PM IST
+  workflow_dispatch:
+
+jobs:
+  generate-metrics:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
+
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Install Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y docker.io jq
+
+      - name: Generate Metrics SVG
+        uses: lowlighter/metrics@latest
+        with:
+          filename: SVG/followup.svg
+          token: ${{ secrets.GH_METRICS_TOKEN }}
+          user: iamAntimPal
+          base: ""
+          template: classic
+          config_timezone: Asia/Kolkata
+          plugin_followup: yes
+          plugin_followup_sections: repositories, user
+          plugin_followup_indepth: yes
+          plugin_followup_archived: no
+
+      - name: Commit and Push Changes
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          git checkout -b update-followup-metrics
+          git add SVG/followup.svg
+          git commit -m "Update followup.svg [skip ci]"
+          git push origin update-followup-metrics
+
+      - name: Create Pull Request
+        uses: peter-evans/create-pull-request@v6
+        with:
+          token: ${{ secrets.GH_METRICS_TOKEN }}
+          branch: update-followup-metrics
+          title: "chore: update followup metrics"
+          body: "Auto-generated followup metrics SVG"
diff --git a/.github/workflows/topic.yml b/.github/workflows/topic.yml
deleted file mode 100644
index f706f8d..0000000
--- a/.github/workflows/topic.yml
+++ /dev/null
@@ -1,36 +0,0 @@
-# Visit https://github.com/lowlighter/metrics#-documentation for full reference
-name: Followup
-on:
-   # Schedule updates (daily)
-   schedule: [{ cron: "0 0 * * *" }]
-   # Lines below let you run workflow manually and on each commit
-   workflow_dispatch:
-   push: { branches: ["master", "main"] }
-jobs:
-   github-metrics:
-      runs-on: ubuntu-latest
-      permissions:
-         contents: write
-      steps:
-         - uses: lowlighter/metrics@latest
-           with:
-              # Your GitHub token
-              # The following scopes are required:
-              #  - public_access (default scope)
-              # The following additional scopes may be required:
-              #  - read:org      (for organization related metrics)
-              #  - read:user     (for user related data)
-              #  - read:packages (for some packages related data)
-              #  - repo          (optional, if you want to include private repositories)
-              filename: SVG/followup.svg
-              token: ${{ secrets.GH_METRICS_TOKEN }}
-
-              # Options
-              user: iamAntimPal
-              template: classic
-              base: header, activity, community, repositories, metadata
-              config_timezone: Germany/Berlin
-              plugin_followup: yes
-              plugin_followup_sections: repositories, user
-              plugin_followup_indepth: yes
-              plugin_followup_archived: no

From 9846b7fa447e6f688d37683e23c5926b1dae3232 Mon Sep 17 00:00:00 2001
From: Antim Pal <134076504+iamAntimPal@users.noreply.github.com>
Date: Fri, 25 Apr 2025 20:49:40 +0530
Subject: [PATCH 353/353] Update followup.yml

---
 .github/workflows/followup.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/followup.yml b/.github/workflows/followup.yml
index 5bcaaa8..40c44fb 100644
--- a/.github/workflows/followup.yml
+++ b/.github/workflows/followup.yml
@@ -21,7 +21,8 @@ jobs:
       - name: Install Dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y docker.io jq
+          sudo apt-get remove -y containerd
+          sudo apt-get install -y docker.io jq containerd.io
 
       - name: Generate Metrics SVG
         uses: lowlighter/metrics@latest