dataclass-csv VS Flake8

CSV – csv Reader or dataclass-csv

Automating code checks with static code analysis allows us to enforce code styling effectively. By integrating tools into our workflow, we can identify errors at an early stage, while coding instead of blocking us at the end. For instance, flake8 checks Python code for style and errors, eslint performs similar checks for JavaScript, and prettier automatically formats code to maintain consistency.

Flake8. This library is a wrapper around pycodestyle (PEP8), pyflakes, and Ned Batchelder’s McCabe script. It is a great toolkit for checking your code base against coding style (PEP8), programming errors (like SyntaxError, NameError, etc) and to check cyclomatic complexity.

Flake8 combines the functionalities of the PyFlakes, pycodestyle, and McCabe libraries. It provides a streamlined approach to code linting by detecting coding errors, enforcing style conventions, and measuring code complexity.

He made yesqa and pyupgrade (among others), and also works on flake8. His main job is for https://sentry.io/.

repos: - repo: https://github.com/psf/black rev: 21.7b0 hooks: - id: black language_version: python3.8 - repo: https://github.com/PyCQA/flake8 rev: 3.9.2 hooks: - id: flake8

https://github.com/psf/black it’s a PEP8 compliant formatter for Python codebases. If you don’t like auto formatting files you can use https://github.com/PyCQA/flake8 it just lists out all of the style issues so you can fix them manually.

I have no stake in that, but my observation is that the actual discussion appears to have both supporters and detractors rather than overwhelming support. Either way, it has nothing to do with whether or not it is realistic to say that Ruff is the "one Python linter to rule them all".