EgonDS - Data Science UI Suite

EgonDS is a collection of Python-based GUI applications designed to simplify data science workflows. It provides intuitive interfaces for data visualization, NumPy operations, and Pandas DataFrame manipulation, making these powerful libraries accessible without writing extensive code.

🚀 Projects

1. Egon Visualization

A comprehensive GUI for creating various Matplotlib visualizations.

• Plot Types: Graph, Histogram, Bar, Pie, Stem, Scatter Plot, ImShow, Contour, Error Bar, Box Plot.
• Features:
  • Import data directly from CSV files.
  • Customize markers, lines, colors, and grid styles.
  • Adjust transparency and font sizes.
  • Theme selection (Dracula/Light).

2. (Egon) NumpyGui

A modern GUI for performing NumPy and SciPy operations.

• Core Functions: Arithmetic, Rounding, Trigonometry, Statistics, and Calculus.
• Advanced Features:
  • Matrix Support: Input multi-line text to automatically create and manipulate 2D Matrices.
  • Inline Controls: Generate random numbers, create linspaces, and filter data directly within the UI.
  • Calculator Grid: Visual interface for common mathematical operations.
  • State Management: Persists your theme (Light/Dark) and view preferences.
  • Dual Interface: Choose between a side tab view or a traditional top menu.
  • Modern UI: Consistent Dark/Light theming across all windows and popups, with immediate status feedback and glitch-free theme switching.

3. (Egon) PandasGui

A powerful tool for viewing, cleaning, and analyzing Pandas DataFrames.

• Data Management: Open and save CSV, JSON, and Excel files.
• History: Full Undo/Redo support for all data modifications.
• Cleaning:
  • Remove empty rows/duplicates, drop columns/rows, rename columns, and replace values.
  • Smart Fill NA: Auto-fill missing data using Mean, Median, or Mode.
  • One-Hot Encoding: Convert categorical data for ML readiness.
• Analysis:
  • Statistics: Detailed stats (Mean, Median, Mode, etc.) and dataset descriptions.
  • Hypothesis Testing: Perform T-Tests and Chi-Squared tests.
  • Machine Learning: Run simple Linear Regression models directly in the app.
• Visualization:
  • Tabular view of your data with vertical scrolling.
  • Integrated Plotting: Create Histograms, Scatter, Line, Bar, and Box plots directly from your data.
• Modern Architecture:
  • Persistent State: Remembers your theme, window size, and layout preferences.
  • Multiple Layouts: Choose from Tabs, Phone (compact grid), or Menu Only modes.
  • Async Performance: Heavy operations run in the background to keep the UI responsive.

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/Ariel4545/data_science_ui.git
   cd data_science_ui

2. Install dependencies:

   pip install -r requirements.txt

   Dependencies include: pandas, numpy, customtkinter, scipy, matplotlib, pyperclip, scikit-learn

🖥️ Tested On

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

MIT License