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.
- 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
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
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)
- Clone the repository:
git clone https://github.com/yourusername/Sedes.git
cd Sedes- Install dependencies using uv:
uv syncRun the application with:
uv run streamlit run src/app.pyThe application will open in your default web browser.
- Load Data: Click the "📂" button in the sidebar to load a CSV file
- Add Operations: Use the sidebar buttons to add filters, aggregations, or data cleaning operations
- Add Components: Create charts, text components, and data info displays in the EDA tab
- View Data: Explore your data in the Data Preview tab
- Manage State: Save your work, load previous sessions, or generate Jupyter notebooks
src/app.py: Main application file with UI components and logicsrc/state.py: Application state managementsrc/components.py: UI component definitionssrc/charts.py: Chart creation and customizationsrc/df_operations.py: Data operations (filter, aggregate, clean)src/dfinfo.py: DataFrame information utilitiessrc/codegen.py: Code generation for Jupyter notebooks
- Built with Streamlit
- Visualizations powered by Plotly Express
- Data manipulation with Pandas