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📊 DataViz Explorer

A beautiful web app for automated data visualization and exploratory data analysis (EDA)

App Screenshot

Streamlit Python Pandas Plotly

🚀 Features

  • Multi-format support: Upload CSV, Excel, JSON, or Parquet files
  • Interactive Visualizations:
    • Scatter, Line, Bar, Histogram, Box, Violin, and Pie charts
    • Fully customizable axes and colors
    • Zoom, pan, and hover interactions
  • Automated EDA Reports:
    • Complete statistical summary
    • Correlation analysis
    • Missing values detection
    • Downloadable HTML report
  • Beautiful UI:
    • Modern, responsive design
    • Dark/light mode compatible
    • Informative data metrics cards

🛠️ Installation

  1. Clone the repository:
  git clone https://github.com/yourusername/data-viz-explorer.git
  cd data-viz-explorer
  1. Create and activate a virtual environment:
  python -m venv venv
  source venv/bin/activate  # On Windows use `venv\Scripts\activate`
  1. Install dependencies:
  pip install -r requirements.txt

🏃 Running the App

  streamlit run app.py

The app will open in your default browser at http://localhost:8501

🌐 Deployment

Streamlit Sharing (Recommended)

  1. Push your code to GitHub
  2. Sign up at Streamlit Sharing
  3. Click "New App" and connect your repository

Heroku

heroku create your-app-name
git push heroku main

📸 Screenshots

Data Preview Interactive Visualization EDA Report
Data Preview Visualization EDA Report

🛠️ Tech Stack

  • Frontend: Streamlit
  • Backend: Python
  • Data Processing: Pandas
  • Visualization: Plotly, Altair
  • EDA: Pandas-profiling

📂 File Structure

data-viz-explorer/
├── app.py                # Main application code
├── style.css             # Custom CSS styles
├── requirements.txt      # Python dependencies
├── README.md             # This file
└── .gitignore            # Git ignore file

🤝 Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the project
  2. Create your 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

📜 License

Distributed under the MIT License. See LICENSE for more information.

✉️ Contact

Dheeraj Kumar - 13kumardheeraj@gmail.com

Project Link: PROJECT LINK

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A Streamlit Application for dataset visualization on the go without any code

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