Table of Contents

• Introduction
• Variables
  • Variable names should reveal intent
  • Use meaningful and pronounceable variable names
  • Use the same vocabulary for the same type of variable
  • Avoid magic numbers and magic strings
  • Use variables to keep code "DRY" ("Don't Repeat Yourself")
  • Use explanatory variables
  • Avoid mental mapping
  • Don't add unneeded context
• Dispensables
  • Avoid comments
  • Remove dead code
  • Avoid print statements (even glorified print statements such as df.head(), df.describe(), df.plot())
• Functions
  • Use functions to keep code "DRY"
  • Functions should do one thing
  • Functions should only be one level of abstraction
  • Function names should say what they do
  • Use type hints to improve readability
  • Avoid side effects
  • Avoid unexpected side effects on values passed as function parameters
  • Function arguments (2 or fewer ideally)
  • Use default arguments instead of short circuiting or conditionals
  • Don't use flags as function parameters

Introduction

Clean code practices (from Clean Code and Refactoring) adapted for machine learning / data science workflows in Python. This is not a style guide. It's a guide to producing readable, reusable, and refactorable software.

Targets Python3.7+