![Modern Python Environments - Poetry
Powerful CLI used for creating and managing Python projects.
● Create a new project: $ poetry new <project-name>
● Install a dependency: $ poetry add [--dev] <package name>
● Run a command inside the virtual environment: $ poetry run python -m pytest
● Dependencies are managed inside pyproject.toml](https://image.slidesharecdn.com/completepythontoolboxformoderndevelopers-210413081325/85/Complete-python-toolbox-for-modern-developers-8-320.jpg)
![Modern Python Environments - pipenv
Managing dependencies and virtual environments.
● Create virtual environment: $ pipenv --python 3.8
● Install a dependency: $ pipenv install [--dev] <package name>
● Run a command inside the virtual environment: $ pipenv run python -m pytest
● Dependencies are managed inside Pipfile](https://image.slidesharecdn.com/completepythontoolboxformoderndevelopers-210413081325/85/Complete-python-toolbox-for-modern-developers-10-320.jpg)
Complete python toolbox for modern developers
- 1. Complete Python toolbox for modern developers Jan Giacomelli Python Web Conference 2021
- 2. About me ● Tech lead and co-founder typless.com ● Author at testdriven.io ● Twitter: @jangiacomelli
- 3. Python development ● creating and re-creating virtual environments ● installing, uninstalling, and updating dependencies ● type checking ● writing and running tests ● following consistent code style ● avoiding security vulnerabilities ● documenting code
- 4. Modern Python Environments - pyenv Lets you easily switch between multiple versions of Python. ● Install specific Python version: $ pyenv install 3.8.5 ● List available Python versions: $ pyenv versions
- 5. Modern Python Environments - pyenv ● Select a specific version as a global default: $ pyenv global 3.8.5 ● Select a specific version for the current project: $ pyenv local 3.9.0
- 6. Modern Python Environments - pip + venv Fairly simple to use. Pre-installed with most versions of Python. ● Create virtual environment: $ python -m venv my_venv ● Activate virtual environment: $ source my_venv/bin/activate ● Install a dependency: (my_venv)$ python -m pip install <package-name> ● Create list of dependencies: (my_venv)$ python -m pip freeze > requirements.txt
- 7. Modern Python Environments - pip + venv
- 8. Modern Python Environments - Poetry Powerful CLI used for creating and managing Python projects. ● Create a new project: $ poetry new <project-name> ● Install a dependency: $ poetry add [--dev] <package name> ● Run a command inside the virtual environment: $ poetry run python -m pytest ● Dependencies are managed inside pyproject.toml
- 9. Modern Python Environments - Poetry
- 10. Modern Python Environments - pipenv Managing dependencies and virtual environments. ● Create virtual environment: $ pipenv --python 3.8 ● Install a dependency: $ pipenv install [--dev] <package name> ● Run a command inside the virtual environment: $ pipenv run python -m pytest ● Dependencies are managed inside Pipfile
- 11. Modern Python Environments - pipenv
- 12. Which one to choose? ● pip + venv ● poetry ● pipenv
- 13. Python testing - pytest Go-to testing framework for testing Python code. Compared to unittest: ● less boilerplate code -> more readable test suites ● uses built-in assert statement -> more readable and easier to remember ● updated more frequently (not part of the Python standard library) ● simpler setting up and tearing down test state with its fixture system ● uses a functional approach
- 14. Python testing - pytest
- 15. Python testing - pytest
- 16. Python testing - pytest Plugins: ● pytest-django - set of tools made specifically for testing Django applications ● pytest-xdist - run tests in parallel ● pytest-cov - adds code coverage support ● pytest-instafail - shows failures and errors immediately instead of waiting until the end of a run
- 17. Python testing - Mocking Practice of replacing real objects with mocked ones, which mimic their behavior, at runtime.
- 18. Python testing - Mocking ● pytest’s monkeypatch ● Python’s built-in untttest.mock library ○ Mock ○ MagicMock ○ create_autospec
- 19. Python testing - Code coverage A metric that tells you the ratio between the number of lines executed during test runs and the total number of all lines in your code base. pytest-cov - adds code coverage support
- 20. Code quality Code is considered to be of high quality when: ● It serves its purpose ● Its behavior can be tested ● It follows a consistent style ● It's understandable ● It doesn't contain security vulnerabilities ● It's documented well ● It's easy to maintain
- 21. Code quality - linters Linters flag programming errors, bugs, stylistic errors, and suspicious constructs through source code analysis.
- 22. Code quality - flake8 Wrapper around Pyflakes, pycodestyle, and McCabe.
- 23. Code quality - flake8
- 24. Code quality - formatters Reformat your code based on a set of standards. Code formatting is a dull job that should be performed by a computer. ● reduce merge conflicts ● easier to read code ● easier to find bugs ● easier to onboard new developers
- 25. Code quality - isort Automatically separates imports into groups standard library, third-party, local, and alphabetically orders them.
- 26. Code quality - black Code formatter that's used to reformat your code based on the Black's code style guide.
- 27. Code quality - black
- 28. Code quality - Security Vulnerability Scanners Your code is only as secure as its weakest link.
- 29. Code quality - bandit Tool designed to find common security issues in Python code. ● hardcoded password strings ● deserializing untrusted code ● using pass in except blocks
- 30. Code quality - safety Used to check your installed dependencies for known security vulnerabilities against Safety database.
- 31. Type checking - type hints Type hints allow developers to annotate expected types for variables, function parameters, and function returns inside Python code. ● not enforced by the Python interpreter ● better express the intent ● autocomplete ● less bugs
- 32. Type checking - type hints
- 33. Type checking - type hints
- 34. Type checking - mypy A tool for type checking at compile-time.
- 35. Type checking - pydantic Uses type hints to validate data on runtime. ● easy to use ● uses type casting ● use it when dealing with external data
- 36. Type checking - pydantic
- 37. Type checking - Marshmallow Helps to validate complex data and load/dump data from/to native Python types. ● doesn't use type casting ● schema and class defined separately
- 39. Type checking - Typeguard It enforces types while your program is running. ● typechecked decorator ● comes with pytest plugin ● for classes and functions
- 40. Type checking - Typeguard
- 41. Documenting Python Code Without proper documentation, it can be very difficult or impossible for internal and external stakeholders to use and/or maintain your code. ● standalone resource ● should always be present ● how and when to use something
- 42. Documenting Python Code - Docstrings A special "string literal that occurs as the first statement in a module, function, class, or method definition". ● __doc__ attribute of module/class/function ● multiline and single line ● different formats (Google, NumPy, reStructuredText, Epytext) ● code examples (doctest)
- 43. Documenting Python Code - Sphinx Converts your project's docstrings to HTML and CSS.
- 44. Documenting Python Code - Sphinx
- 45. Documenting Python Code - Sphinx
- 46. Documenting Python Code - OpenAPI Standard format for describing, producing, consuming, and visualizing RESTful APIs. ● used for SwaggerUI and ReDoc ● can be imported to Postman ● can be used to generate SDKs ● can be auto-generated
- 47. Documenting Python Code - OpenAPI
- 48. Conclusion ● creating and re-creating virtual environments - pyenv, pip + venv, poetry, pipenv ● installing, uninstalling, and updating dependencies - pip + venv, poetry, pipenv ● writing and running tests - pytest, pytest-cov, unittest.mock ● type checking - mypy, pydantic, marshmallow, typeguard ● following consistent code style - black, flake8, isort ● avoiding security vulnerabilities - bandit, security ● documenting code - Sphinx, OpenAPI You can learn more about these tools in Complete Python Guide