Avoiding Over-Commenting

Have you ever opened a Python file only to find more comments than actual code? It's like trying to hear a conversation in a noisy room—too much clutter makes it hard to focus on what's important. While comments are essential for explaining complex logic, over-commenting can actually make your code harder to read and maintain. Today, let’s talk about how to strike the right balance and keep your code clean, expressive, and appropriately documented.

Why Less Can Be More

Comments are meant to clarify, not repeat. If your code is self-explanatory, you don’t need a comment for every line. In fact, verbose comments can become outdated quickly. When you update the code but forget to update the comment, you create confusion and misinformation.

Consider this example:

# This function adds two numbers
def add(a, b):
    return a + b

The comment here is entirely redundant. The function name add and the operation a + b are clear enough. Now imagine if every function and variable in your codebase had such obvious comments—it would be like labeling every item in your kitchen "this is a spoon" or "this is a bowl." It adds noise without value.

Instead, focus on writing code that speaks for itself. Use descriptive names for variables and functions. Let the code tell the story.

def calculate_total_price(unit_price, quantity):
    return unit_price * quantity

No comment needed here—the function name and parameters make the purpose obvious.

When Comments Are Actually Helpful

Of course, there are times when comments are not just helpful but necessary. The key is to use them where they provide real insight.

Use comments to explain the "why," not the "what." If you’re doing something unconventional or there’s a non-obvious reason behind a decision, that’s worth commenting.

For example:

def process_data(data):
    # Using insertion sort for small datasets due to lower constant factors
    if len(data) <= 10:
        return insertion_sort(data)
    else:
        return merge_sort(data)

Here, the comment explains why we’re choosing one algorithm over another in a specific case—information that isn’t obvious from the code itself.

Another good use case is when you’re dealing with workarounds or known issues:

def fetch_user_data(user_id):
    # Temporary fix: API returns 500 if user_id is negative; remove when backend is patched
    if user_id < 0:
        return None
    response = api_call(user_id)
    return response.json()

This comment clarifies that the check is a temporary measure and why it exists.

Also, comments are useful for marking sections of code or providing high-level overviews, especially in longer scripts or modules. But keep these concise and avoid stating the obvious.

Signs You’re Over-Commenting

How can you tell if you’ve crossed the line into over-commenting? Here are a few red flags:

• You’re writing a comment for almost every line of code.
• Your comments simply repeat what the code is doing.
• You find yourself commenting on things like variable declarations or basic arithmetic.
• Comments are longer than the code they describe.
• You’re using comments to explain bad code instead of improving the code itself.

If any of these sound familiar, it might be time to refactor your code and reduce the commentary.

Remember: good code should be like a good book—it should draw the reader in and tell a clear story without needing footnotes on every page.

Strategies to Avoid Over-Commenting

So how do you write code that needs fewer comments? Here are some practical tips:

• Write self-documenting code: Use meaningful names for variables, functions, and classes. Make your code read like plain English where possible.
• Break down complex functions: If a function is so long or complicated that it requires many comments, consider splitting it into smaller, well-named functions.
• Use docstrings for public APIs: For functions and classes that others will use, a docstring is appropriate to explain usage, parameters, and return values.
• Comment only the non-obvious: Assume the reader knows Python. You don’t need to explain language features—only your unique logic.

Let’s look at an example of refactoring over-commented code:

Before:

def calc(a, b, c):
    # Check if a is greater than b
    if a > b:
        # If true, return a plus c
        return a + c
    else:
        # Else return b plus c
        return b + c

After:

def get_max_plus_value(x, y, increment):
    if x > y:
        return x + increment
    else:
        return y + increment

By using clearer names, we’ve eliminated the need for those low-value comments.

Tools and Best Practices

Modern tools can help you maintain a healthy commenting style. Linters like pylint or flake8 can warn about commented-out code or missing docstrings where they’re expected. Use them to keep yourself in check.

Also, adopt a team style guide for comments. Consistency helps everyone understand when and how to comment.

Remember: the goal is not to eliminate comments entirely, but to make them meaningful. Well-placed comments can save hours of debugging and confusion. But like any powerful tool, they should be used wisely.

In summary, treat comments as seasoning—a little enhances the dish, too much ruins it. Write clear code first, and add comments only where they add real value. Your future self (and your teammates) will thank you.

Now, go make your code shine—with or without the comments!