📢 Notice: This package was previously known as
prepup-linux. If you're upgrading fromprepup-linux, please uninstall it first before installingride-cli.
RIDE-CLI (Rapid Insights Data Engine) is a powerful, user-friendly command-line tool designed to simplify and streamline your data analysis workflow. Whether you're a data scientist, analyst, or researcher, RIDE provides an intuitive interface for exploring, cleaning, and preparing your datasets - all from your terminal!
- Documentation: https://sudhanshumukherjeexx.github.io/ride-cli/
- Github: https://github.com/sudhanshumukherjeexx/ride-cli
- PyPI: https://pypi.org/project/ride-cli/
- Previous Package (prepup-linux): https://github.com/sudhanshumukherjeexx/prepup-linux
- 📊 Load datasets from various formats (CSV, Excel, Parquet)
- 🔍 Comprehensive data inspection
- 📈 Advanced data exploration
- 🧹 Missing value handling
- 📊 Feature visualization
- 🤖 Auto Machine Learning (AutoML) model selection
- Data Loading
- Data Type Conversion
- Feature Inspection
- Correlation Analysis
- Distribution Checking
- Outlier Detection
- Missing Value Imputation
- Feature Encoding
- Feature Scaling and Transformation
- Automatic Model Training
⚠️ Important: Creating a virtual environment is highly recommended when installing ride-cli.
If you're currently using prepup-linux, please follow these steps:
# Uninstall the old package
pip uninstall prepup-linux
# Install the new package
pip install ride-cli# Create virtual environment
python -m venv ride-env
# Activate virtual environment
ride-env\Scripts\activate
# Deactivate when done
deactivate# Create virtual environment
python3 -m venv ride-env
# Activate virtual environment
source ride-env/bin/activate
# Deactivate when done
deactivate# Inside your activated virtual environment
pip install ride-cli# Inside your activated virtual environment
git clone https://github.com/sudhanshumukherjeexx/ride-cli.git
cd ride-cli
pip install .rideor
ride-cliride path/to/your/dataset.csv- Load Dataset
- Inspect Data
- Change Data Type
- Explore Data
- Visualize Data
- Impute Missing Values
- Feature Encoding
- Feature Scaling and Transformation
- Export Data
- AutoML (Train & Evaluate Models)
-
Launch RIDE:
ride -
Load Your Dataset: Choose option 1 and enter your dataset path
-
Inspect Data: Use option 2 to explore features, data types, and missing values
-
Preprocess:
- Change data types if needed
- Impute missing values
- Encode categorical features
- Scale and transform features
-
Analyze:
- Visualize data distributions
- Perform correlation analysis
- Run AutoML for model selection
- Supports both Classification and Regression tasks
- Evaluates multiple machine learning algorithms
- Provides performance metrics
- Saves results to CSV for further analysis
- CSV (.csv)
- Excel (.xlsx, .xls)
- Parquet (.parquet)
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- Plotext (for terminal-based plotting)
- and more (see requirements.txt)
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
- 🎉 Renamed from
prepup-linuxtoride-cli - 🌍 Added cross-platform support
- ✨ Enhanced user interface
- 🔧 Improved stability and performance
Special thanks to all contributors and users of the previous prepup-linux package. Your feedback and support made this evolution possible!
Made with ❤️ by Sudhanshu Mukherjee