Python Commenting Guide: Best Practices, Examples & Docstrings (2025)

Honestly? When I first started learning Python, I thought commenting was just typing those hash symbols because some teacher told me to. Big mistake. It wasn't until I joined a real project and had to debug someone else's spaghetti code at 2 AM that I truly appreciated proper commenting. So let's cut through the fluff - here's everything about how to comment in Python that actually matters.

You're probably looking for more than just syntax, right? Like when to comment, what makes a good comment, and how commenting in Python differs from other languages. Maybe you've seen messy codebases where comments make things more confusing. I've been there too, and we'll fix that.

Why Bother with Comments Anyway?

Look, I get it - writing code is already enough work without adding extra typing. But here's the reality check I got during my first internship:

Code gets read 10x more than it gets written. That function you wrote last month? When you revisit it at midnight before a deadline, you'll thank your past self for leaving clues.

I remember working on a data pipeline last year. Three months after building it, I got an emergency call about failing jobs. Without clear comments explaining why certain edge cases were handled, I'd have been completely lost. That's when commenting stopped being academic for me.

The Two Main Flavors of Python Comments

Single-Line Comments (The # Hero)

These are your bread and butter for explaining small code snippets. Just pop a # and everything after it gets ignored by Python:

# This calculates monthly growth rate - critical for Q3 reports growth = (current_month - previous_month) / previous_month

Where they shine:

  • Quick explanations above tricky lines
  • Temporarily disabling code during debugging (I call this "commenting out")
  • Short notes at end of lines

Multi-Line Comments (The Triple-Quote Approach)

Technically these aren't true comments - they're unassigned string literals. But everyone uses them for multi-line explanations:

""" DATA VALIDATION PIPELINE Version: 2.1 Last modified: 2023-06-15 This module handles: - Input sanitization - Boundary checks - Format conversion Critical: Do not modify without testing validate_test.py """

Notice how I included maintenance notes? That's saved my team from breaking things multiple times.

Comment Type When to Use My Personal Rule
# Single-line Explaining complex logic, temporary code disabling Keep under 80 characters if possible
""" Multi-line """ Module headers, algorithm explanations, documentation blocks Always include last modified date
End-of-line # Clarifying variable purpose in dense code Use sparingly - creates messy code

The Unwritten Rules of Commenting in Python

After maintaining several Python projects, here's what I've learned about practical commenting:

  • Explain WHY, not WHAT - The code already shows what's happening
  • Update comments with code changes - Outdated comments are worse than none
  • Write for future-you - Assume you'll forget everything in 6 months
  • Use complete sentences - "Fix calc bug" helps no one
  • Avoid stating the obvious - Please don't write "# increment counter" above i += 1

Here's an example I found in our legacy code that made me facepalm:

# Loop through users for user in users: # Add user to list user_list.append(user)

Seriously? That's just noise. Good commenting would explain why we looping matters:

# Compile active users for audit report (required by finance) active_users = [user for user in users if user.last_login > threshold]

Docstrings: Python's Secret Weapon

If I had to pick one Python commenting feature to teach every beginner, it would be docstrings. These triple-quoted strings become your code's documentation:

def calculate_tax(income: float, brackets: list) -> float: """ Compute tax liability based on progressive tax brackets. Args: income (float): Annual taxable income brackets (list): List of tuples (lower_bound, upper_bound, rate) Returns: float: Total tax amount Raises: ValueError: If income is negative Example: brackets = [(0, 10000, 0.1), (10000, 50000, 0.2)] calculate_tax(60000, brackets) # Returns 11000.0 """ if income < 0: raise ValueError("Income cannot be negative") # Computation logic here...

Why docstrings are awesome:

  • They appear in help() and IDE tooltips
  • Tools like Sphinx auto-generate documentation from them
  • Standardized format makes code self-documenting

Common Docstring Formats

Style Best For My Preference
Google Style Most projects, highly readable Recommended
NumPy Style Scientific computing For math-heavy code
Epytext Legacy systems Avoid unless required

Commenting Pitfalls I've Fallen Into (So You Don't Have To)

Nobody talks about the mistakes, so I will:

Over-commenting: My first project had more comments than code. It was unmaintainable chaos. Now I comment only when:

  • The logic isn't immediately obvious
  • There's an important business reason
  • I'm working around a weird bug

Zombie code: Leaving commented-out code "just in case". Trust me, Git exists for a reason. I once spent hours debugging why a feature wasn't working only to discover active code was commented out "temporarily" six months prior.

Mystery comments: Like "# Fix this later" with no context. Later never comes. Now I use:

# TEMP: Workaround for API rate limits (Ticket #API-117) # TODO: Implement proper retry logic by Q4

When Comments Become a Code Smell

If you find yourself writing lengthy comments to explain complex code, that's often a sign your code needs refactoring, not more comments. I learned this the hard way when my 20-line comment could've been replaced with two well-named helper functions.

Real-World Commenting Scenarios

Let's get practical with examples from actual projects:

Scenario 1: Algorithm Explanation

# Using Miller-Rabin primality test for cryptographic applications # Reason: Deterministic for our input range < 2^64 with selected witnesses def is_prime(n): if n < 2: return False # Witness bases for deterministic test up to 2^64 witnesses = [2, 325, 9375, 28178, ...] ...

Scenario 2: Business Logic Clarification

# IMPORTANT: Threshold changed from 0.75 to 0.65 per finance memo #2023-41 # Do NOT revert without CFO approval fraud_threshold = 0.65

Scenario 3: Workaround Documentation

# TEMPORARY: Cloud service returns UTC timestamps instead of local time # Remove when ticket CLOUD-2281 is resolved (est. Oct 2023) timestamp = utc_to_local(raw_timestamp)

Comment-Driven Development?

A trick I picked up from senior developers: sometimes write comments first. Before implementing a complex function, outline your approach in comments:

def merge_customer_data(primary, secondary): # Step 1: Validate both datasets have required fields # Step 2: Match records using email + phone fuzzy matching # Step 3: Handle conflicts: prioritize primary source # Step 4: Merge address histories chronologically ...

This helps clarify your thinking before writing code. Just remember to remove these scaffolding comments later!

Python Commenting FAQ

How do I quickly comment multiple lines in VS Code?

Select lines and press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac). This toggles # prefixes. PyCharm uses the same shortcut. Game-changer for debugging!

Can I use HTML in Python docstrings?

Technically yes, but please don't. Most documentation tools (Sphinx, MkDocs) use simpler markup like reStructuredText or Markdown. I made this mistake early on and it created rendering issues.

How detailed should function docstrings be?

Cover inputs, outputs, errors, and one usage example. I usually follow this template:

def example(param): """Brief description Longer explanation if needed Args: param (type): Description Returns: type: Description Raises: ErrorType: When does this happen Example: >>> example(input) expected_output """

Are there official standards for Python comments?

PEP 8 covers basics, but the real standard is: "Will someone understand this in 6 months?" PEP 257 covers docstrings specifically. I suggest keeping both open while learning how to comment in Python.

How do I document type hints with comments?

Use inline comments only for complex cases. Modern Python prefers type hints:

# Instead of: def process(data): # data: list of dicts ... # Do: from typing import List, Dict def process(data: List[Dict[str, float]]) -> None: ...

Special Cases Worth Mentioning

Scenario Commenting Approach My Suggestion
Test files Document edge cases being tested # Tests division by zero handling
Data science scripts Explain data transformations # Normalizing to address skew > 2.0
API code Document rate limits and endpoints # Max 100 req/min (code 429)
Concurrency Flag thread-unsafe sections # WARNING: Non-atomic operation

Last week, I found a comment that saved us from a major API regression: "# This endpoint deprecated after v3 - use /v4/search instead". Without that note, we might have wasted days on dead-end debugging trying to figure out how to comment in Python effectively for API versioning.

Comments evolve with your code. I review mine quarterly during code cleanups. Anything outdated gets removed or updated immediately - it's like dental hygiene for your codebase.

The Ultimate Checklist for Python Comments

  • Does this comment explain WHY rather than WHAT?
  • Would this help someone new debugging at 3 AM?
  • Am I documenting special requirements or constraints?
  • Does this duplicate information that could be in function/variable names?
  • Have I removed any commented-out code blocks?
  • Do docstrings follow our team's chosen standard?
  • Are multi-line comments actually necessary? (Could this be a docstring?)
  • Would future-me understand this comment?

Learning how to comment in Python isn't about rules - it's about communication. Your comments are love letters to your future self and your teammates. Write them with care, maintain them diligently, and never underestimate their power to prevent disasters.

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