Improving Readability with Comments
The key to writing good comments is to explain "why" rather than just "what was done" to improve the readability of the code. 1. Comments should explain logical reasons, such as considerations behind value selection or processing; 2. Use paragraph comments for complex logic to summarize the overall idea of functions or algorithms; 3. Maintain comments regularly to ensure consistency with the code, avoid misleading, and delete outdated content if necessary; 4. Check comments synchronously when reviewing the code, and record public logic through documents to reduce the burden of code comments.

No matter how well the code is written, if no one can understand it, it will be useless. Writing comments is not an unnecessary move, but allowing others (including their future self) to understand your ideas faster. Especially in projects where multiple people collaborate or long-term maintenance, annotations are the most direct way to improve readability.

Comments should explain "why", not just "what did"
What do many people do when writing comments habitually repeating code, such as:
# Set variable x to 5 x = 5
This kind of comment is actually useless. What is really useful is to explain the logic behind this code, such as why you choose this value, or why you deal with it in this way.

For example:
# Use 5 as the default value, because the minimum input is 5 x = 5
Those who read this way will know that this is not written casually, but has a specific reason. Don't just say what you did, but make it clear why you did it.

Add paragraph comments to complex logic
Some functions or algorithms have a lot of logic, and it is easy to be confused when looking at the code directly. At this time, you can write a short explanation at the beginning to explain the overall idea clearly.
For example, a piece of code that deals with data cleaning:
# Data cleaning steps:
# 1. Remove outliers (values exceeding 3 times the standard deviation)
# 2. Use forward padding for missing values# 3. Convert categorical variables to one-hot encoding def clean_data(df):
...In this way, someone can know the general process of this code by scanning it, and there is no need to guess it one by one. Especially for those who just took over, this structured annotation is very friendly.
Notes should also be maintained regularly, so don't let them become misleading
Many people no longer care about comments after writing the code. As a result, the code has been changed for several rounds, and the comments are still the same. This situation is worse than not writing comments because it can mislead others.
It is recommended to update the comments when modifying the key logic, even if it is simply adjusting the wording. If you find that a comment is no longer in line with the code, don't hesitate. It is better to delete it than to leave it misleading.
Additionally, consider the following practices to maintain the quality of the annotation:
- When reviewing PR, check whether relevant comments need to be updated
- Record public logic in documents or wikis, avoid using code comments to explain complex logic
- Delete obviously outdated and meaningless comments, such as
# TODO: 这个地方需要优化but has not been changed
Basically that's it. Notes are not written as much as you want, but rather they must be written accurately and clearly. If used well, it is the instruction manual of the code; if used poorly, it becomes noise.
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