hypermodern-python-tuto 1.0.1

Repo to follow the Claudio Jolowicz's tutorial about Hypermodern Python (https://cjolowicz.github.io/posts/hypermodern-python-01-setup/)

hypermodern-python-tuto

Repo to follow the Claudio Jolowicz's tutorial about Hypermodern Python.

Table of contents

• Description of the application
• Install
• Use
• Tools used
  • Generic tools
  • Generic Python tools
    • Multi-purpose
    • Setup
    • Test
    • Linting
    • Security
    • Formatting
    • Type checking
    • Documentation
  • Specific Python tools
    • UI
    • Communication
    • Data validation

Description of the application

The app created is a CLI application that queries a random Wikipedia page and displays its title and summary.

Install

TODO

Use

TODO

Tools used

Generic tools

Tools that can be used in every development project, no matter if it's a Python project or not.

• git, to manage versions of the source code
• GitHub, to host the git repository and automate tasks with GitHub Actions:
  • Release Drafter, to create release templates
• pre-commit, to manage pre-commit hooks
• Codecov, to mesure code coverage on repos

Generic Python tools

Tools that can be used in every Python project, no matter its content.

Multi-purpose

• PyPI, to install and publish Python packages
• poetry, to make development and distribution easy (packaging, virtualization, dependencies, launching and publishing)
• nox, to run tasks in multiple Python environments (like tests, linting, reformatting, etc.)

Setup

• pyenv, to manage Python versions

Test

• pytest, a framework to write unit tests. Also used to run doctests
• pytest-cov, to mesure the code coverage (degree to which the source code of a program is executed while running its test suite)
• pytest-mock, to use the unittest mocking in the pytest way
• xdoctest, to execute the doctests (tests in documentation strings)

Linting

• flake8, a linter aggregator
• flake8-import-order, to verify that imports are grouped and ordered in a consistent way
• flake8-bugbear, to find bugs and design problems
• flake8-annotations, to detect the absence of type annotations
• flake8-black, to check if the code follows black formatting
• flake8-docstrings, to check that the code is correctly documented
• darglint, to check that docstrings match function definitions

Security

• Bandit, to find security issues (used inside linting with flake8-bandit)
• Safety, to check if some packages are insecure

Formatting

• black, to format the code

Type checking

• mypy, the classic type checker
• pytype, a static type checker
• typeguard, a runtime type check

Documentation

• Sphinx, the documentation tool used by the official Python documentation.
• autodoc, Sphinx official plugin to generate API documentation from the docstrings.
• napoleon, Sphinx official plugin to allow compatibility with Google-style docstrings.
• sphinx-autodoc-typehints, Sphinx plugin to detect type hints in generated documentation.

Specific Python tools

Tools to match specific needs of the projet.

UI

• click, to create CLI applications

Communication

• requests, to make HTTP requests

Data validation

• marshmallow, to serialize, deserialize and validate data
• dessert, to generate marshmallow serialization schemas → not used because too limited (can't work with data where fields names are different from the ones of the target dataclass)

I used marshmallow to follow the tutorial, but pydantic is more known, and I find it easier to use.