📊 Data Visualization with Python

A comprehensive data visualization and analysis platform for India's Census Data, built with Python and Streamlit. This interactive dashboard provides insightful visualizations and analytics at national, state, and district levels.

✨ Features

• 📈 Overall Analysis: National-level demographic insights and trends
• 🗺️ Statewise Analysis: Detailed statistics for individual states
• 🏘️ Districtwise Analysis: Granular data visualization at the district level
• 📊 Interactive Visualizations: Dynamic charts powered by Plotly and Seaborn
• 🎯 User-Friendly Interface: Clean and intuitive Streamlit dashboard
• 📱 Responsive Design: Works seamlessly across different screen sizes

🎯 Key Metrics Analyzed

• Population demographics (Male/Female distribution)
• Sex ratio calculations
• Literacy vs Illiteracy rates
• District-wise comparative analysis
• State-level trends and patterns

🚀 Quick Start

Prerequisites

• Python 3.13 or higher
• pip or uv package manager

Installation

1. Clone the repository

   git clone <repository-url>
   cd "Data Visualization with Python"

2. Set up virtual environment (optional but recommended)

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies

   Using pip:

   pip install -r requirements.txt

   Or using uv:

   uv pip install -r requirements.txt

4. Run the application

   streamlit run app.py

5. Open your browser

   click here to view the dashboard

📁 Project Structure

Data Visualization with Python/
│
├── app.py                      # Main Streamlit application
├── overall.py                  # Overall analysis module
├── statewise.py               # State-level analysis module
├── distwise.py                # District-level analysis module
├── main.py                    # Additional main script
│
├── census_data/               # Census datasets
│   └── final_df.csv          # Processed census data
│
├── notebooks/                 # Jupyter notebooks for exploration
│   ├── 01_exp.ipynb
│   └── 02_exp.ipynb
│
├── messy_data/               # Raw/unprocessed data files
│
├── requirements.txt          # Project dependencies
├── pyproject.toml           # Project configuration
├── .gitignore               # Git ignore rules
├── .python-version          # Python version specification
└── README.md                # Project documentation (this file)

🛠️ Technologies Used

Core Libraries

• Streamlit - Web application framework
• Pandas - Data manipulation and analysis
• NumPy - Numerical computing
• Matplotlib - Static visualizations
• Seaborn - Statistical data visualization
• Plotly - Interactive charts and graphs

Development Tools

• Git - Version control
• Jupyter Notebook - Data exploration and prototyping
• VS Code - Development environment

📊 Usage

Overall Analysis

Click on the "Overall-Analysis" button in the sidebar to view:

• National demographic overview
• Population distribution patterns
• Key statistical insights

Statewise Analysis

1. Select "Statewise-Analysis" from the dropdown
2. Choose your desired state
3. Click "Show analysis" to view state-specific data

Districtwise Analysis

1. Select "Districtwise-Analysis" from the dropdown
2. Choose a state (or select "All India")
3. Select a specific district
4. Click "Show analysis" to view detailed district data

🔍 Data Insights

The dashboard automatically calculates and displays:

• Sex Ratio: (Female/Male) × 100
• Illiteracy Ratio: (Illiterate/Literate) × 100
• District codes for easy identification
• Comparative visualizations across regions

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

🐛 Known Issues

• Working on optimizing large dataset loading times
• Enhancing mobile responsiveness for complex visualizations

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Pravat Patra

🙏 Acknowledgments

• Census data provided by the Government of India
• Streamlit community for excellent documentation
• Python data science community for amazing libraries

📧 Contact

For questions, suggestions, or feedback, please open an issue in the repository.

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⭐ Star this repository if you find it helpful!

Made with ❤️ and Python