What is python automatic documentation? python automatic documentation is a method of generating information about functions and classes automatically. But how can we do that? And what are the advantages of doing that? In this article, we will look into those questions and more.
Automatic Documentation is a tool to generate documentation from docstrings in Python modules or classes, inspired by the Javadoc package for Java.[1] It is implemented as a standard module that can be launched from the command line. The resulting output is normally written to HTML files, but can also produce LaTeX output, depending on compiler options.
Python documentation is not only for programmers. We understand that documentation can be seen as about as exciting as watching grass grow, but it is essential if you want your code to be useful and widely adopted. This is where a good understanding of Python automatic documentation will really come in handy.
This package can automatically generate documentation for your python project. Under the hood, it uses pydoc to convert the docstrings of all functions, classes, methods etc into HTML files. You can easily take those generated files and improve on them or host them somewhere on the internet.
What is Software Documentation?
Software documentation is an online document that explains everything about a software product, from what it is and how it is built to how it can be put to maximum use. It uses written text along with images and how-to videos to demonstrate how to execute steps and perform tasks. With all software information meticulously arranged in categories and subcategories, the document makes it easy for users to navigate through articles and get what they need instantly.
A software document serves both end-users and developers. While it walks end-users through the basics of the software, it helps developers update their software programs regularly.
Writing Docstrings
Depending on the complexity of the function, method, or class being written, a one-line docstring may be perfectly appropriate. These are generally used for really obvious cases, such as:
def add(a, b):
"""Add two numbers and return the result."""
return a + b
The docstring should describe the function in a way that is easy to understand. For simple cases like trivial functions and classes, simply embedding the function’s signature (i.e. add(a, b) -> result) in the docstring is unnecessary. This is because with Python’s inspect module, it is already quite easy to find this information if needed, and it is also readily available by reading the source code.
In larger or more complex projects however, it is often a good idea to give more information about a function, what it does, any exceptions it may raise, what it returns, or relevant details about the parameters.
For more detailed documentation of code a popular style used, is the one used by the NumPy project, often called NumPy style docstrings. While it can take up more lines than the previous example, it allows the developer to include a lot more information about a method, function, or class.
def random_number_generator(arg1, arg2):
"""
Summary line.
Extended description of function.
Parameters
----------
arg1 : int
Description of arg1
arg2 : str
Description of arg2
Returns
-------
int
Description of return value
"""
return 42
The sphinx.ext.napoleon plugin allows Sphinx to parse this style of docstrings, making it easy to incorporate NumPy style docstrings into your project.
At the end of the day, it doesn’t really matter what style is used for writing docstrings; their purpose is to serve as documentation for anyone who may need to read or make changes to your code. As long as it is correct, understandable, and gets the relevant points across then it has done the job it was designed to do.
Why Documenting Your Code Is So Important
Hopefully, if you’re reading this tutorial, you already know the importance of documenting your code. But if not, then let me quote something Guido mentioned to me at a recent PyCon:
“Code is more often read than written.”
— Guido van Rossum
When you write code, you write it for two primary audiences: your users and your developers (including yourself). Both audiences are equally important. If you’re like me, you’ve probably opened up old codebases and wondered to yourself, “What in the world was I thinking?” If you’re having a problem reading your own code, imagine what your users or other developers are experiencing when they’re trying to use or contribute to your code.
Conversely, I’m sure you’ve run into a situation where you wanted to do something in Python and found what looks like a great library that can get the job done. However, when you start using the library, you look for examples, write-ups, or even official documentation on how to do something specific and can’t immediately find the solution.
After searching, you come to realize that the documentation is lacking or even worse, missing entirely. This is a frustrating feeling that deters you from using the library, no matter how great or efficient the code is. Daniele Procida summarized this situation best:
In this guide, you’ll learn from the ground up how to properly document your Python code from the smallest of scripts to the largest of Python projects to help prevent your users from ever feeling too frustrated to use or contribute to your project.
Commenting vs Documenting Code
Before we can go into how to document your Python code, we need to distinguish documenting from commenting.
In general, commenting is describing your code to/for developers. The intended main audience is the maintainers and developers of the Python code. In conjunction with well-written code, comments help to guide the reader to better understand your code and its purpose and design:
“Code tells you how; Comments tell you why.”
— Jeff Atwood (aka Coding Horror)
Documenting code is describing its use and functionality to your users. While it may be helpful in the development process, the main intended audience is the users. The following section describes how and when to comment your code.
Basics of Commenting Code
Comments are created in Python using the pound sign (#) and should be brief statements no longer than a few sentences. Here’s a simple example:
def hello_world():
# A simple comment preceding a simple print statement
print("Hello World")
According to PEP 8, comments should have a maximum length of 72 characters. This is true even if your project changes the max line length to be greater than the recommended 80 characters. If a comment is going to be greater than the comment char limit, using multiple lines for the comment is appropriate:
def hello_long_world():
# A very long statement that just goes on and on and on and on and
# never ends until after it's reached the 80 char limit
print("Hellooooooooooooooooooooooooooooooooooooooooooooooooooooooo World")
Commenting your code serves multiple purposes, including:
- Planning and Reviewing: When you are developing new portions of your code, it may be appropriate to first use comments as a way of planning or outlining that section of code. Remember to remove these comments once the actual coding has been implemented and reviewed/tested:
# First step # Second step # Third step - Code Description: Comments can be used to explain the intent of specific sections of code:
# Attempt a connection based on previous settings. If unsuccessful, # prompt user for new settings. - Algorithmic Description: When algorithms are used, especially complicated ones, it can be useful to explain how the algorithm works or how it’s implemented within your code. It may also be appropriate to describe why a specific algorithm was selected over another.
# Using quick sort for performance gains - Tagging: The use of tagging can be used to label specific sections of code where known issues or areas of improvement are located. Some examples are:
BUG,FIXME, andTODO.# TODO: Add condition for when val is None
Comments to your code should be kept brief and focused. Avoid using long comments when possible. Additionally, you should use the following four essential rules as suggested by Jeff Atwood:
- Keep comments as close to the code being described as possible. Comments that aren’t near their describing code are frustrating to the reader and easily missed when updates are made.
- Don’t use complex formatting (such as tables or ASCII figures). Complex formatting leads to distracting content and can be difficult to maintain over time.
- Don’t include redundant information. Assume the reader of the code has a basic understanding of programming principles and language syntax.
- Design your code to comment itself. The easiest way to understand code is by reading it. When you design your code using clear, easy-to-understand concepts, the reader will be able to quickly conceptualize your intent.
Remember that comments are designed for the reader, including yourself, to help guide them in understanding the purpose and design of the software.
Commenting Code via Type Hinting (Python 3.5+)
Type hinting was added to Python 3.5 and is an additional form to help the readers of your code. In fact, it takes Jeff’s fourth suggestion from above to the next level. It allows the developer to design and explain portions of their code without commenting. Here’s a quick example:
def hello_name(name: str) -> str:
return(f"Hello {name}")
From examining the type hinting, you can immediately tell that the function expects the input name to be of a type str, or string. You can also tell that the expected output of the function will be of a type str, or string, as well. While type hinting helps reduce comments, take into consideration that doing so may also make extra work when you are creating or updating your project documentation.
Python Documentation Tools
ProProfs Knowledge Base
ProProfs Knowledge Base is a simple and versatile tool that helps you create software documentation of top-notch quality for your end-users. Packed with bespoke features, the tool simplifies software documentation and assists you at all stages of content creation, right from authoring to publishing.
If you are worried about where to start, fret not. The tool offers ready-to-use templates that give you a good starting point to build content. You can write and style the entire software documentation just the way you want with the wide array of formatting features ProProfs Knowledge Base provides.
In addition to this, features like single sourcing, revision history, import, and export, further improve the process of creating and managing online documentation.
Rating: 4.6/5
Pricing: Starts at $30/author/month
Whatfix
Whatfix is carefully-designed to help you provide digitally-driven and contextually-relevant experiences to your end-users. With Whatfix, you can deliver in-app support to product users and take them through step-by-step instructions right inside your software product. It allows you to pack your software documentation in a small widget that points users towards relevant articles.
With such interactive guidance and contextual walkthroughs in real-time, right when users are exploring your software, Whatfix promises an exceptional experience. What’s even more interesting about this software is the automated personalization and conditional workflows that take the user experience to the next level.
Rating: 4.6/5
Pricing: Available on Request
Zoho Docs
Zoho’s documentation software is fully customizable and highly secure. It supports multi-branding, domain mapping, a powerful authoring system, and forums and communities. All these features and many more are designed to help you create, manage, and maintain your software documentation with ease.
What sets Zoho’s documentation software apart from others is that it allows your software users to engage in discussions, give and get suggestions, and share ideas on built-in forums and communities. This makes the ultimate user experience much better as they get the best of both the worlds – self-service as well as direct interaction with fellow users.
Rating: 4.5/5
Pricing: Starts at $10.75/agent/month
Process Street
Process Street is another tool for software documentation that deserves your attention. It comes with a simple interface that lets you build structured software documentation for your end-users. With intuitive keyboard shortcuts and drag and drop functionality, Process Street makes document authoring easy for everyone in your development team. You can also add visual and audio elements to your document and turn it into a media-rich information resource.
In addition to this, it allows developers to work in collaboration and take full accountability for the assigned software development tasks.
Rating: 4.6/5
Pricing: Starts at $25/user/month
Conclusion
Imagine that you’ve worked on some Python package for a while. It was quite easy to create this package, because it’s just a part of your bigger project. Then one day, your colleague came up with an idea to create a small application based on your code and release it as a separate product. And then another one came with the same suggestion. And after that, yet another person needed some significant changes in your code to stop doing redundant work.
Automatically generate your API documentation from your Python modules. This is a good starting point for building your own full-featured documentation, especially for small or medium-sized projects.