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Python Linters at Scale

PyCon US via YouTube

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Overview

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This talk explains how to configure popular Python linters and scale their use across large or multiple codebases. It covers faster CI runs, reusable workflows, observability, and autofixes for common issues.

Syllabus

Intro
Python Codebases
Black: code formatting
isort: import sorting
Flake8: code style, syntax errors and bugs
mypy: type checking
Version Control
Continuous Integration (CI) Runs
Large Codebase
Multiple Codebases
Checklist for Speeding up Linters
Only run necessary analysis on updated code
pre-commit: manage pre-commit hooks
Remote Cache
Ruff: fast linter implementation using rust
Checklist for Improving Developer Experience
Understand Developer Experience
fixit: Python linters and autofixes using LibCST
Our Custom Python Linters: Github Check with annotations
Our Custom non-Python Linters: rebase reminder
Our Custom Python Linters: deprecation toolkit
Reusable Workflows
Automated Refactoring
Our Custom Python Autofixes: FlakeB
Our Custom non-Python Autofixes: notify-reviewer-teams
Recap

Taught by

PyCon US

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