How to Comment your Python Code as a Data Scientist 

Types of Comments: Single-Line Comments: Use # to comment out a single line. Use these for brief explanations of code blocks.

Multi-Line Comments: Use triple quotes (""") for longer comments or docstrings. These are great for explaining complex functions, data sources, and overall script purpose.

General Tips:

Be clear and concise: Use simple, understandable language. Avoid jargon unless your audience is familiar with it.

Focus on the "why": Explain the intent and logic behind your code, not just the "what."

Comment non-obvious code: Don't comment on self-explanatory code, but clarify anything complex or less intuitive.

Be consistent: Use the same commenting style throughout your code. PEP 8 is a good guideline.

Update your comments: Keep them relevant as your code evolves.

Use docstrings: Write comprehensive docstrings for functions and classes, explaining their purpose, arguments, and return values.

Specific Tips for Data Scientists: Explain data sources: Describe where the data comes from, its format, and any preprocessing steps.

Document assumptions and limitations: Mention any assumptions made in the analysis and potential limitations of the results.

Annotate key calculations: Explain the logic behind complex calculations, especially if they involve specific techniques.

Link to external resources: Reference relevant documentation or tutorials for specific libraries or algorithms used.

Use notebooks effectively: Add comments within code cells and markdown cells for better organization and explanation.