prepup-linux 0.2.3

Prepup is a free, open-source package for data preprocessing in terminal

💻 Prepup: Interactive Data Preprocessing Toolkit

⚠️ PACKAGE RENAMED: prepup-linux → ride-cli

IMPORTANT: This package has been renamed to ride-cli. Please use the new package for all future installations and updates.

Migration Instructions

To migrate to the new package:

# Uninstall the old package
pip uninstall prepup-linux

# Install the new package
pip install ride-cli

All functionality remains the same. The only change is the package name and command:

• Old command: prepup
• New command: ride or ride-cli

Why the Change?

Prepup began in summer 2023 as the Preprocessing Utility Package (PrePUP) with just 5 terminal flags—a learning project that evolved into a comprehensive data tool. After creating prepup-linux to address cross-platform compatibility issues, we realized the name incorrectly suggested Linux exclusivity, when our vision has always been platform independence. We also tested our first menu-driven approach in prepup-linux. We're now transitioning to RIDE-CLI (Rapid Insights Data Engine), a name that better reflects our tool's capabilities: rapid data preprocessing, meaningful insights generation, and cross-platform functionality. This rebranding represents our growth from a simple utility to a robust data engine, while maintaining our commitment to continuous improvements and expanded features across all platforms.

---

🚀 Quick Overview

Prepup is a powerful, user-friendly data preprocessing tool designed to simplify and streamline your data analysis workflow directly from the terminal. Whether you're a data scientist, analyst, or researcher, Prepup provides an intuitive interface for exploring, cleaning, and preparing your datasets.

✨ Features

Interactive Mode

• 📊 Load datasets from various formats (CSV, Excel, Parquet)
• 🔍 Comprehensive data inspection
• 📈 Advanced data exploration
• 🧹 Missing value handling
• 📊 Feature visualization
• 🤖 Automatic Machine Learning (AutoML) model selection

Key Functionalities

• Data Loading
• Feature Inspection
• Correlation Analysis
• Distribution Checking
• Outlier Detection
• Missing Value Imputation
• Feature Standardization
• Automatic Model Training

🛠 Installation

⚠️ Important: Creating a virtual environment is highly recommended when installing prepup-linux. As a data processing library, it has various dependencies that may conflict with your existing packages.

Setting Up a Virtual Environment

Windows

# Create virtual environment
python -m venv prepup-env

# Activate virtual environment
prepup-env\Scripts\activate

# Deactivate when done
deactivate

Linux/macOS

# Create virtual environment
python3 -m venv prepup-env

# Activate virtual environment
source prepup-env/bin/activate

# Deactivate when done
deactivate

Using pip

# Inside your activated virtual environment
pip install prepup-linux

From Source

# Inside your activated virtual environment
git clone https://github.com/sudhanshumukherjeexx/prepup-linux.git
cd prepup-linux
pip install .

💻 Usage

Interactive Mode

prepup

Loading a Specific Dataset

prepup path/to/your/dataset.csv

Main Menu Options

1. Load Dataset
2. Inspect Data
3. Explore Data
4. Visualize Data
5. Impute Missing Values
6. Standardize Features
7. Export Data
8. AutoML (Train & Evaluate Models)

🎮 Interactive Workflow Example

1. Launch Prepup prepup

2. Load Your Dataset: Choose option 1 and enter your dataset path

3. Inspect Data: Use option 2 to explore features, data types, and missing values

4. Preprocess: Impute missing values | Standardize features

5. Analyze: Visualize data distributions | Perform correlation analysis | Run AutoML for model selection

🤖 AutoML Capabilities

• Supports both Classification and Regression tasks
• Evaluates multiple machine learning algorithms
• Provides performance metrics
• Saves results to CSV

📦 Dependencies

• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• and more (see requirements.txt)

🤝 Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📋 License

Distributed under the MIT License. See LICENSE for more information.

🔄 Migration Notice

This package is deprecated and will no longer receive updates. Please migrate to ride-cli for the latest features and support.

New Package Links

• GitHub: https://github.com/sudhanshumukherjeexx/ride-cli
• PyPI: https://pypi.org/project/ride-cli/