Python Documentation: how to create and maintain It for your projects

Documentation is one of the key pillars for maintaining and scaling any software project. In the Python ecosystem, following good documentation practices is essential to ensure that code is reusable, understandable, and well-structured.

In this article, we’ll explore how to create effective Python documentation for your projects, with a special focus on RPA and Python automation. Let’s dive in!

Why is Python documentation important?

Clear documentation reduces the learning curve of a project, simplifies team collaboration, and prevents unnecessary rework.

In the context of Robotic Process Automation (RPA) — where workflows heavily rely on scripts and integrations — well-written documentation can mean the difference between a smooth operation and a chain of hard-to-debug errors.

Types of documentation in Python

Python offers several ways to document code, with the most common being:

1. Docstrings (Embedded Documentation)

Docstrings are strings placed directly within your code to describe what a function, method, class, or module does. They appear right after the definition and are enclosed in triple quotes (""").

Here’s a basic structure of a function using docstrings:

def add_numbers(a: int, b: int) -> int:
"""
This function adds two integers and returns the result.

Args:
a (int): First number to add.
b (int): Second number to add.

Returns:
int: The sum of a and b.
"""
return a + b

You can view this documentation at runtime by accessing the special __doc__ attribute:

print(add_numbers.__doc__)

(function) def function_name(
argument_1: int,
argument_2: int
) -> int

Here we define the main purpose of this function.

Args

argument_1 : data type
Purpose of the first argument.

argument_2 : data type
Purpose of the second argument.

Returns

int
What the function returns.

function_name(1, 2)

Or in the code:

print(function_name.__doc__)

Here we define the main purpose of this function.

Args:
argument_1 (data type): Purpose of the first argument.
argument_2 (data type): Purpose of the second argument.

Returns:
int: What the function returns.

2. Code Comments

Comments are lines of text added to the code with the purpose of explaining or documenting what a specific part of the code does. They are not executed by Python and serve solely to make the code clearer for whoever is writing or reading it.

To write a comment, simply add the # symbol before the text you want to comment.

Everything that comes after # on the same line will be ignored by Python.

See the example from Real Python:

# This is a comment
print("This will run.") # This won't run

In the example above:

• The first line is just a comment and will be completely ignored by Python.

• The second line executes the print command, and the comment on the same line is only an explanation (it does not interfere with execution).

3. Markdown Documentation

To create more readable and well-organized documentation, you can use .md (Markdown) files. Markdown allows you to structure the text using simple syntax.

Here are some commonly used Markdown symbols:

For text hierarchy:

# Main Title 
## Subtitle 1 
### Subtitle 2

For ordered lists:

1. First item 
2. Second item 
3. Third item

For unordered lists:

- Item 
- Item 
- Item

To add hyperlinks:

[Clickable text](link_address)

Using this structure makes your documentation much more fluid and organized.

When creating Markdown documentation, be mindful of the conversion engine used to render MD into HTML (the most common format online). While most syntax is supported, some systems might require slightly different notation for specific features.

How to create documentation for RPA Projects with Python?

Python documentation for Robotic Process Automation (RPA) should include the following:

• Installation instructions: how to set up the environment and required RPA libraries;

• Usage examples: code snippets showing how the automation works;

• Common errors and solutions: documentation of typical problems and how to resolve them;

• API guide: detailed explanation of classes and functions used in the project.

All set on Python Documentation?

Creating effective documentation for your Python projects isn’t just a best practice — it’s essential.

In RPA, a well-documented project ensures workflows run smoothly and can be maintained or scaled easily. Take this opportunity to learn more about BotCity, one of the leading platforms in the RPA market, and discover how it can support your automation initiatives.