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+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
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diff --git a/LeetCode SQL 50 Solution/595. Big Countries/readme.md b/LeetCode SQL 50 Solution/595. Big Countries/readme.md
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+# **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/)
+