DataProfile is a powerful, user-friendly tool for performing exploratory data analysis without writing a single line of code. It leverages the capabilities of pandas-profiling to provide in-depth insights into your data, all within a sleek Streamlit interface.
- Upload CSV files for instant analysis
- Generate comprehensive data profiles with a single click
- Choose between Minimal, Explorative, and Default profiling modes
- Interactive web interface powered by Streamlit
- Detailed visualizations and statistics for each variable in your dataset
Follow these steps to set up DataProfile on your local machine:
-
Clone the repository:
git clone https://github.com/DataScientistTX/dataprofile.git
-
Navigate to the project directory:
cd dataprofile -
(Optional) Ensure you have the latest version:
git pull origin main
-
Install the required Python packages:
pip install -r requirements.txt
-
Make sure you're in the project directory:
cd dataprofile -
Launch the Streamlit app:
streamlit run app.py
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Open your web browser and go to
http://localhost:8501(or the address provided in the terminal). -
Upload your CSV file and select a profiling mode to start analyzing your data.
Alternatively, you can use the deployed version of DataProfile at https://dataprofile.streamlit.app/.
We welcome contributions to DataProfile! If you have suggestions for improvements or encounter any issues, please feel free to open an issue or submit a pull request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- pandas-profiling for the excellent profiling tool
- Streamlit for making it easy to create data apps
Your Name - @DataScientistTX - [email protected]
Project Link: https://github.com/DataScientistTX/dataprofile