Hey there! As a fellow Python developer, I know how frustrating it can be to work with messy, poorly commented code. I‘ve been there too.
Code comments are like breadcrumbs we leave behind to understand the trail of logic when revisiting old code. They help future-proof our work by enhancing maintainability.
In this comprehensive guide, I‘ll share everything I‘ve learned over the years about writing readable Python code with clear comments:
- Why comments are critical for Python and coding in general
- The ins and outs of different Python comment types
- Commenting best practices based on experience and expert advice
- My top editor recommendations for simplifying Python comments
By the end of this guide, you‘ll have the knowledge to write expert-level commented Python code that stands the test of time. Let‘s dive in!
Why Comments Are Essential in Python
Before jumping into syntax, I want to convince you of why commenting is so important for Python.
I‘ve worked on teams where commenting was seen as a "nice-to-have" or afterthought. This always came back to bite us down the road! Maintaining undocumented legacy code becomes a nightmare.
Here are four key reasons you should comment your Python code:
1. Remember Your Own Thought Process Long Term
Ever start working on old code and think "what was I thinking here?" I sure have.
Comments are like small reminders of your past thought process and design decisions. They serve as breadcrumbs to lead you back through your own logic months or years later.
Without them, you waste time reverse engineering your own work when maintaining or iterating on a codebase.
2. Explain Complex Algorithms Simply
Python is often used for complex statistical analysis, machine learning models, intense mathematics, and intricate algorithms.
These sections are hard enough to implement correctly the first time. Don‘t make your future self decipher all that complexity again! Use comments to break down the tricky parts into plain English.
I‘ve found comments particularly helpful when translating complex math and academic papers into Python. Summarize key equations and concepts in easy-to-understand language near the relevant code.
3. Help Onboard New Team Members
Unless you are a solo coder on a project, chances are you will work with other developers over time. When they onboard, comments help them rapidly understand your existing approach before making changes.
I like to leave high-level summaries explaining the overall program flow and architecture. For key methods and components, write a few sentences documenting purpose and functionality.
This ramps up new hires far faster so they can be productive teammates.
4. Create More Maintainable Code
Maintainability refers to how easy code is to update and build upon long term. It‘s a crucial quality characteristic and comments directly support it.
They make your code more modular and flexible by documenting logical sections and intentions. When requirements change in the future, you or other developers can better modify appropriately commented code.
Statistics show that typically developers spend over 50% of their time maintaining and modifying existing code rather than writing new logic from scratch. So optimize for maintainability with comments!
Python Comment Types Explained
Now that you‘re convinced of why Python code commenting is essential, let‘s explore the different types of comments available:
These are brief single line comments that sit on the same line as a code statement. They start with a
# followed by the comment text:
# This squared value will be used later for the model prediction
squared_value = 7**2
That example demonstrates good inline comment usage – it provides relevant context in a succinct way.
Inline comments work best for short explanations tailored to a single line of code. Don‘t try to write paragraphs and documentation with them.
To write longer comments spanning multiple lines, you can use triple quotes:
This is a multiline comment.
Useful when you need a paragraph
or multiple lines of explanation.
Anything between the
""" quotes won‘t be executed as code – it‘s pure documentation.
These are great for complex logic explanation, documentation on classes and functions, detailing program flow at a high level, and any other concept requiring more depth.
Docstrings are a special variant of multiline comments used to document modules, functions, classes, and methods in Python.
They are defined as the first statement in these components, like so:
def sum_numbers(a, b):
This function returns the sum of two numbers.
>>> sum_numbers(2, 5)
return a + b
Docstrings are quite powerful. They allow you to demonstrate intended usage and behavior using examples, like above. Many IDEs render them nicely as function references. And tools like Sphinx can auto-generate documentation from docstrings.
Their position right after the definition declares them as the "official" documentation for that component. Treat docstrings as the perfect place for overview, usage, arguments, return values, errors, and examples.
Commenting Out Code
Sometimes you want to temporarily remove code from execution without deleting it entirely. This is known as "commenting out" in most coding languages.
In Python, you can comment out whole blocks or specific lines easily:
# This function is no longer needed
# def old_function():
print("Hello") # This line runs normally
Commented out code shouldn‘t be left in your files permanently. But it‘s handy during development and debugging.
Now you know the full gamut of Python comment types. Time to level up your skills with some best practices!
Python Commenting Best Practices
Writing effective comments takes skill and discipline. Let‘s explore what Python code commenting done right looks like:
Provide WHY Over WHAT
Average comments simply restate what each line of code does procedurally. These low-value comments clutter up code without providing insight.
Great comments focus on explaining why something is implemented a certain way. Share the developer‘s mindset and intent.
# Set x to 0
x = 0
# Initialize x to 0 as the default initial state
x = 0
Position Comments Before Code
Always position comments above the code they describe for better context:
# Initialize variables for model training
epochs = 100
learning_rate = 0.001
Don‘t force readers to hunt around or read after the fact. Establish context up front.
Delete Outdated Comments
Don‘t let old or irrelevant comments linger – delete them! If you significantly refactor code, update the related comments.
Stale, misleading comments are worse than none at all. Keep them in sync.
Break Up Giant Functions/Files
Sometimes overly long functions and files lack comments because it‘s daunting to document them.
This is often a code smell indicating you should break up large units into smaller, modular components with focused individual purposes. Each can then be documented more easily.
Follow Language Conventions
Every language has its own commenting conventions and best practices. Learn and follow them!
For Python, PEP 257 covers docstring standards. Many Python linting tools will flag docstring issues.
Adopt Consistent Commenting Style
However you decide to comment, do it consistently across all your Python files.
Settle on standard spacing, capitalization, punctuation, and other formatting rules for comments. Consistency leads to better code comprehension.
Comment During Code Reviews
Code reviews are a great time for high-level documentation. Have teammates review a new feature or component and suggest areas needing clarification through comments.
Different perspectives identify the logic most warranting further explanation through comments.
Top Python IDEs for Commenting
The right editor or IDE can significantly improve your Python commenting workflow. Let‘s check out some of the best choices:
Microsoft‘s free Visual Studio Code is my favorite for Python development:
- Extensions like Better Comments provide handy comment shortcuts and templates
- Multi-cursor support makes quick work of commenting out multiple lines
- Hovers display docstring references and notes without clicking
- Linting alerts help enforce docstring standards from PEP 257
With its robust extension ecosystem and focus on productivity, VS Code is hard to beat for commented Python code.
JetBrains PyCharm IDE is tailored specifically for Python:
- Smart comment/uncomment functions understand Python syntax
- Docstring generation with templates speeds up writing
- Dedicated keyboard shortcuts like Ctrl+/ to toggle commenting
- Easy navigation between comments in a file
- PEP 8 compliance checks on docstrings
As a Python-centric IDE, PyCharm packs in many amenities for commenting.
Jupyter Notebook excels at documentation thanks to Markdown support:
- Separate Markdown cells allow fluid documentation
- Toggle code cell commenting with a simple #
- Mix code, output, and comments linearly
- Embed images/tables next to code they explain
- Wide ecosystem of extensions like autocomment code
Notebooks are great for illustrating concepts incrementally with liberally interspersed comments.
Spyder is another scientific Python IDE with solid commenting abilities:
- Quickly comment or uncomment selected lines
- Context-aware comment/uncomment actions
- Tooltips display docstring text without clicking
- Find comments and strings instantly with search
- PEP8 linting for docstring style and standards
With Spyder, it‘s simple to follow best practices for commented Python code.
Putting It All Together
Whew, that was a lot to digest! Let‘s recap the key points:
Well-written comments explain why code is written a certain way. They document the logic and mindset.
Python has inline comments, multiline comments, docstrings, and commenting out code.
Position comments before relevant code. Follow language conventions like PEP 257.
Delete outdated comments! Keep docs in sync with changes.
Break up huge functions and files to make commentary easier.
Adopt consistent commenting rules across all your Python files.
Use the right editor/IDE to accelerate documenting code.
No one enjoys writing documentation. But great code comments will supercharge your productivity and save future headaches!
The next time you write a tricky Python program, imagine yourself or a new team member revisiting it months later. Write comments suited for them!
Leaving "breadcrumbs" with targeted, insightful comments will pay dividends in maintainability and comprehension of your Python codebase for years to come.