https://github.com/micpec/sedes
SEDES 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.
https://github.com/micpec/sedes
eda ipynb pandas streamlit
Last synced: 2 months ago
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SEDES 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.
- Host: GitHub
- URL: https://github.com/micpec/sedes
- Owner: MicPec
- License: mit
- Created: 2025-04-06T20:03:29.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-04-07T20:31:25.000Z (about 1 year ago)
- Last Synced: 2025-04-10T02:51:19.227Z (about 1 year ago)
- Topics: eda, ipynb, pandas, streamlit
- Language: Python
- Homepage:
- Size: 1.28 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚽 SEDES - Simple & Elegant Data Exploration System
SEDES 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.
## Features
- **Data Upload**: Import CSV files with customizable separators
- **Data Transformation**: Apply filters, aggregations, and cleaning operations
- **Dynamic Visualization**: Create and customize various chart types using Plotly Express
- **Data Info Components**: Display various types of information about your dataframes
- **State Management**: Save and load application state, generate Jupyter notebooks
- **Operation History**: Track all data operations with the ability to edit or delete them
- **Interactive UI**: User-friendly interface with modal dialogs for all operations
- **Sample Data**: Includes sample dataset to get started quickly
## Chart Types
SEDES supports a variety of chart types:
- Line Charts
- Bar Charts
- Histograms
- Scatter Charts
- Pie Charts
- Box Plots
- Violin Plots
- Heatmaps
- Area Charts
- Funnel Charts
## Data Info Components
The Data Info feature allows you to display various types of information about your dataframes:
- DataFrame Preview
- Shape (rows & columns)
- Statistics (using `describe()`)
- Column Types
- Missing Values
- All Information (combining all aspects)
## Screenshots
### EDA Tab
### Data Cleaning Operation
### Edit Filter Operation
### Data Preview Tab
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/Sedes.git
cd Sedes
```
2. Install dependencies using uv:
```bash
uv sync
```
## Usage
Run the application with:
```bash
uv run streamlit run src/app.py
```
The application will open in your default web browser.
### Getting Started
1. **Load Data**: Click the "📂" button in the sidebar to load a CSV file
2. **Add Operations**: Use the sidebar buttons to add filters, aggregations, or data cleaning operations
3. **Add Components**: Create charts, text components, and data info displays in the EDA tab
4. **View Data**: Explore your data in the Data Preview tab
5. **Manage State**: Save your work, load previous sessions, or generate Jupyter notebooks
## Project Structure
- `src/app.py`: Main application file with UI components and logic
- `src/state.py`: Application state management
- `src/components.py`: UI component definitions
- `src/charts.py`: Chart creation and customization
- `src/df_operations.py`: Data operations (filter, aggregate, clean)
- `src/dfinfo.py`: DataFrame information utilities
- `src/codegen.py`: Code generation for Jupyter notebooks
## License
[MIT License](LICENSE)
## Acknowledgments
- Built with [Streamlit](https://streamlit.io/)
- Visualizations powered by [Plotly Express](https://plotly.com/python/plotly-express/)
- Data manipulation with [Pandas](https://pandas.pydata.org/)