sourcing-kaggle-datasets 0.1.3

Sourcing Kaggle Datasets with Python

Sourcing Kaggle Datasets with Python

       

👉 About 👈

Minimum Viable Product:

😎 Built With 😎

• pytest-dev/pytest
• PyCQA/pylint
• PyCQA/bandit

📖 Getting Started 📖

✋ Prerequisites ✋

Change the project-specific attributes. See TODO for more information.

💪 Installation 💪

pip install --user sourcing-kaggle-datasets

🚀 Usage 🚀

🤓 Static Analysis 🤓

$ make types

$ make lint

$ make sast

# .gitlab.ci.yml

types:
    stage: Static Analysis
    script: [make types]
    allow_failure: true

linting:
    stage: Static Analysis
    script: [make lint]
    allow_failure: true

secure-code:
    stage: Static Analysis
    script: [make sast]
    allow_failure: false

🧐 Unit Testing 🧐

$ make unittests

# .gitlab.ci.yml

unittests:
    stage: Unit Testing
    script: [make unittests]
    allow_failure: false

🤩 Building 🤩

$ make build

# .gitlab.ci.yml

build:
    stage: Building
    script: [make build]
    artifacts:
        paths: [dist]
    only:
        - develop
        - main

🥳 Deployment 🥳

$ make deploy

# .gitlab.ci.yml

gitlab:
    stage: Deployment
    script: [make deploy]
    artifacts:
        paths: [dist]
    only:
        - develop
        - main

pypi:
    stage: Deployment
    script: [make release]
    artifacts:
        paths: [dist]
    only:
        - main

For more examples, please refer to the Documentation

⭐️ Features ⭐️

• Using pyTest as a paradigm for test-driven development
• Using Bandit as static application security testing code analyzer
• Automatic build and deploy on GitLab and PyPi Packages Regestries

📑 Changelog 📑

See CHANGELOG for more information.

📋 Roadmap 📋

• Implement Toml Config Parser as a Settings Mechanism

See the open issues for a list of proposed features (and known issues).

🤝 Contribute 🤝

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Please read the contribution guidelines first.
2. Fork the Project
3. Create your Feature Branch (git checkout -b feature/AmazingFeature)
4. Commit your Changes (git commit -m 'Add some AmazingFeature')
5. Push to the Branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

📜 License 📜

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

💌 Contact 💌

Bootcamp contributors - contributors @ bootcamp-project .com

🏆 Acknowledgements 🏆

Thanks for these awesome resources that were used during the development of the Bootcamp Project: Sourcing Kaggle Datasets with Python:

• Kaggle API – The Missing Python Documentation