Exploring 194 Countries Across Their Population, Economy, Health, Energy, and Politics — With Python and Pandas
A Beginner-Level Pandas Project That Digs Into a Dataset of 194 Countries to Answer Real-World Analytical Questions — Clean Data Exploration and Aggregation, the Way an Actual Analyst Would Approach It
Built as Hands-On Practice on the Road to Becoming A Data Analyst, This Project Focuses on Filtering, Grouping, and Summarizing Data Rather Than Just Displaying It
Countries.csv — 194 Rows × 64 Columns
| Category | Fields |
|---|---|
| 🌐 Identity | Country, Capital, Region, Continent, Currency |
| 👥 Population | Total, Male, Female, Urban, Rural |
| 💰 Economy | GDP, Inflation, Tax Revenue, Unemployment |
| 🏥 Health | Expenditure, Life Expectancy, Birth/Death Rate |
| ⚡ Energy | Coal, Hydro, Nuclear, Renewables |
| 🌱 Environment | CO₂, Methane, Greenhouse Gas Emissions |
| 🏛️ Politics | Democracy Score, Democracy Type, Political Leader |
The notebook opens with .info() and .describe() to get oriented, then works through 10 questions using nlargest, value_counts, .apply(), and boolean indexing — each with a short comment explaining the logic.
| 01 | Capital of the Most Populated Country |
| 02 | Least Populated Country |
| 03 | Capital of the Least Populated Country |
| 04 | Top 5 Countries by Democracy Score |
| 05 | Total Number of Regions |
| 06 | Countries in Eastern Europe |
| 07 | Political Leader of the 2nd Most Populous Country |
| 08 | Countries with an Unknown Political Leader |
| 09 | Countries With "Republic" In Their Full Name |
| 10 | Most Populous Country in Africa |
Countries-Data-Analysis
│
├── countries.csv
├── Countries_Questions.ipynb
└── README.md
# Clone the repo
git clone https://github.com/waleed4we/Global-Countries-Data-Analysis.git
cd Global-Countries-Data-Analysis
# Install dependencies
pip install -r requirements.txt
# Launch the notebook
jupyter notebook notebook/Countries_Questions.ipynb