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Python Documentation Best Practices

Python is a fantastic programming language for both inexperienced and seasoned programmers. Check out these 10 best practises for Python documentation if we're just starting started.

Any software development method must have documentation. It can be used to create code samples and tutorials, and it aids developers and users in understanding the code.

The high reputation of Python's documentation is well-deserved. The Python community works very hard to create user-friendly documentation that is of the highest calibre.

To ensure that their Python documentation is as good as it can be, developers should still adhere to a few best practises. Ten of those recommended practises will be covered in this tutorial.

1. Describe our code.

The code is a priceless resource. It's the fruit of our labour and embodies our ingenuity and thoughts. When we write documentation for our code, we're not just preserving our work for future reference but also recording our ideas and thoughts. This documentation can be quite helpful if you need to revisit our code later or if we need to explain it to someone else.

It is also simpler to maintain and enhance our code if it is documented. It's simpler to identify mistakes and areas for improvement when our documentation is precise and straightforward. Additionally, documentation helps speed up the detection and correction of issues.

And finally, writing up our code helps improve our programming skills. You are compelled to think about our code differently when we take the time to document it. We must consider how to communicate our code to someone else. This might assist we in fixing issues in our code.

2. Adopt a unified style

The code must appear to have been developed by a single person when several people are contributing to a project. This is so that the code is simpler to read and comprehend. It also makes finding and fixing bugs simpler. Additionally, automatic documentation generation is simpler with a consistent style. The Sphinx programme, for instance, can produce documentation from Python docstrings. The generated documentation will be simpler to read if the docstrings are prepared in a standardised manner.

Although there are many other ways to generate Python docstrings, the Google Python Style Guide is the most widely used model. This manual offers suggestions on how to produce short and clear documentation as well as on how to format docstrings.

3. Create docstrings for all public classes, methods, functions, and modules.

When trying to comprehend our code, people will first glance at our docstrings. They should explain what our code does and how to use it in a clear, succinct manner.

People will struggle to comprehend our code and might give up if we don't include docstrings for our public modules, functions, classes, and methods.

Documenting our own code with docstrings will help we recall what it does in the future. It's simple to forget what we were doing while creating code a few days or weeks later.

However, if our docstring is well-written, we can easily remind ourselves of what our code does.

Finally, creating docstrings is a useful approach to become familiar with the norms of Python documentation. Our code will be simpler to read and comprehend for others if we adhere to the conventions.

4. Incorporate illustrations in the writing

People frequently want to see an example of how to use a new module or function when they are trying to learn how to do so. They gain a better understanding of how to use the module or function in their own code as well as what it accomplishes.

People will have to look elsewhere (like in the source code) if we don't provide examples in our documentation, which can be time-consuming and frustrating. In addition, if users are unable to locate any examples, they might decide not to use our module or function at all.

As a result, it's crucial to include at least one or two examples in our Python documentation, preferably along with a description of what each example does. This can greatly improve user comprehension of our module or function and increase the likelihood that they'll utilise it.

5. Maintain a changelog.

A changelog is a file that contains a carefully curated list of significant changes for each version of a project, organised chronologically. This would apply to any substantial or noteworthy modifications made to Python projects, such as new features, bug fixes, breaking changes, and other changes.

Developers (and other stakeholders) can better grasp what has changed between versions thanks to changelogs, which also help explain why those changes were done. In order to retain backwards compatibility and to choose when and how to upgrade to a new version, it is essential to have this understanding. Maintaining a changelog additionally makes it simpler for individuals to contribute to a project because they understand what must be done and why right away.

A changelog can be formatted in a variety of ways, but the most crucial factor is to make sure that it is simple to read and comprehend. The simplest method to accomplish this is to stick to a standard structure and write succinct, direct entries.

6. Describe limitations

If you're documenting a Python project, it's possible that someone may wish to use our code in a way that isn't what we intended at some time. And if they do, they'll encounter difficulties.

People can save a huge amount of time and aggravation if we make our code's limits clear up front. Additionally, we can prevent hearing bad things about our code.

So how can we describe the constraints imposed by our code?

Identifying these restrictions is the first step. Although it can be challenging, it's crucial to be as truthful and impartial as we can be.

The next step would be to document the limits once we've determined what they are. Include the following:

A thorough explanation of the restriction, its justification, and a method for getting around it

By following these instructions, we can make sure our we Python documentation is clear and helpful and that it establishes reasonable expectations for code users.

7. Don't document internal information

We only produce documentation that is useful to those that are familiar with our code when we document internal information. New users will not benefit from this, and it may even be confusing.

Names of classes, functions, and variables are examples of internal information. There is no need to provide any of these details in the documentation because those who are previously familiar with our code will already be aware of them.

Internal information should only ever be documented if there is something about it is unusual or likely to be misconstrued. We could want to list the keys that are present in a dictionary that is returned by a function called "get user" that delivers user information, for instance. Even so, we should only take this action if it is indeed essential.

8. Create appealing documentation with Sphinx.

Georg Brandl developed Sphinx, a tool that makes it simple to produce intelligent and stunning documentation. It is available under the BSD licence. It was initially developed for the Python manuals, and many other projects have since adopted it.

The markup language used by Sphinx is reStructuredText, and many of its features were created especially for Python documentation. It may, for instance, build API documentation automatically from Python source code and enables the addition of docstrings form Python modules.

A lot of Sphinx's capabilities also make it simple to produce attractive documentation. It can produce PDF pages directly from reStructuredText sources, for instance, and it enables the usage of LaTeX for mathematical formulas.

9. Store our records online

People can find and browse our documentation much more easily when it is hosted online. People can even star even fork our repository if our documentation is stored on a well-known platform like GitHub, which increases the likelihood that others will find it.

Furthermore, by hosting our documentation online, we can benefit from functions like search engine optimization (SEO). We may make it considerably simpler for users to find our documentation when they search for topics related to what we have described by optimising it for the proper keywords.

Finally, having our documentation online makes it simpler to maintain its current state. Instead of having to update numerous versions of our documentation strewn over the internet, it is much easier to make updates and additions when our documentation is in one central area.

10. Ensure that all of our documentation is current.

Our documentation will evolve along with our codebase. Our documentation will quickly become old and erroneous if we don't keep it up with our code. When trying to use our code, this might cause confusion and aggravation in both novice and seasoned developers.

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