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Simple \u0026 Elegant Data Exploration System\n\nSEDES is a powerful, interactive Exploratory Data Analysis (EDA) tool built with Streamlit that allows users to easily upload, transform, visualize, and analyze data without writing code.\n\n## Features\n\n- **Data Upload**: Import CSV files with customizable separators\n- **Data Transformation**: Apply filters, aggregations, and cleaning operations\n- **Dynamic Visualization**: Create and customize various chart types using Plotly Express\n- **Data Info Components**: Display various types of information about your dataframes\n- **State Management**: Save and load application state, generate Jupyter notebooks\n- **Operation History**: Track all data operations with the ability to edit or delete them\n- **Interactive UI**: User-friendly interface with modal dialogs for all operations\n- **Sample Data**: Includes sample dataset to get started quickly\n\n## Chart Types\n\nSEDES supports a variety of chart types:\n- Line Charts\n- Bar Charts\n- Histograms\n- Scatter Charts\n- Pie Charts\n- Box Plots\n- Violin Plots\n- Heatmaps\n- Area Charts\n- Funnel Charts\n\n## Data Info Components\n\nThe Data Info feature allows you to display various types of information about your dataframes:\n- DataFrame Preview\n- Shape (rows \u0026 columns)\n- Statistics (using `describe()`)\n- Column Types\n- Missing Values\n- All Information (combining all aspects)\n\n## Screenshots\n\n### EDA Tab\n![EDA Tab](img/eda.png)\n\n### Data Cleaning Operation\n![Data Cleaning Operation](img/data_clean.png)\n\n### Edit Filter Operation\n![Edit Filter Operation](img/filter_edit.png)\n\n### Data Preview Tab\n![Data Preview Tab](img/data_prev.png)\n\n\n## Installation\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/yourusername/Sedes.git\ncd Sedes\n```\n\n2. Install dependencies using uv:\n```bash\nuv sync\n```\n\n## Usage\n\nRun the application with:\n```bash\nuv run streamlit run src/app.py\n```\n\nThe application will open in your default web browser.\n\n### Getting Started\n\n1. **Load Data**: Click the \"📂\" button in the sidebar to load a CSV file\n2. **Add Operations**: Use the sidebar buttons to add filters, aggregations, or data cleaning operations\n3. **Add Components**: Create charts, text components, and data info displays in the EDA tab\n4. **View Data**: Explore your data in the Data Preview tab\n5. **Manage State**: Save your work, load previous sessions, or generate Jupyter notebooks\n\n## Project Structure\n\n- `src/app.py`: Main application file with UI components and logic\n- `src/state.py`: Application state management\n- `src/components.py`: UI component definitions\n- `src/charts.py`: Chart creation and customization\n- `src/df_operations.py`: Data operations (filter, aggregate, clean)\n- `src/dfinfo.py`: DataFrame information utilities\n- `src/codegen.py`: Code generation for Jupyter notebooks\n\n## License\n\n[MIT License](LICENSE)\n\n\n## Acknowledgments\n\n- Built with [Streamlit](https://streamlit.io/)\n- Visualizations powered by [Plotly Express](https://plotly.com/python/plotly-express/)\n- Data manipulation with [Pandas](https://pandas.pydata.org/)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicpec%2Fsedes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmicpec%2Fsedes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicpec%2Fsedes/lists"}