Actionable Insights vs Ranking - How to Use and How NOT to Use Code Quality Metrics

Explore the nuanced world of code quality metrics in this insightful PyCon US talk. Discover how metrics can facilitate better conversations about code quality, focusing on technical problems and improvements rather than personal preferences. Learn about various metrics including Method Length, Cyclomatic Complexity, Cognitive Complexity, and Working Memory, understanding their calculations, interpretations, and limitations through Python code examples. Gain actionable insights on how to use these metrics effectively, such as extracting functions and writing more Pythonic code. Critically examine the limitations and potential pitfalls of relying too heavily on metrics, including their potential for misuse and inability to capture important aspects like naming conventions and project structure. By the end of this 28-minute presentation, develop a balanced approach to using code quality metrics as valuable warning signs rather than definitive measures of excellence in software development.