Document Your Python Code and Projects With ChatGPT

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November 08, 2023
Document Your Python Code and Projects With ChatGPT
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Having good documentation is a critical feature of any successful Python project. In practice, writing documentation is hard and can take a lot of time and effort, so some developers don’t like to do it. Luckily, with large language models (LLMs) and tools like ChatGPT, you can quickly document your Python code and projects.

In Python, you can document your code using docstrings and then take advantage of them to enrich the project’s external documentation. ChatGPT can be of great help in writing both docstrings and external documentation.

In this tutorial, you’ll:

  • Build different ChatGPT prompts to generate Python docstrings
  • Use different styles while generating docstrings with ChatGPT
  • Add doctest tests and usage examples to Python docstrings
  • Create external documentation, such as README files and tutorials, with ChatGPT

To get the most out of this tutorial, you should have a ChatGPT account and know the basics of interacting with this tool using prompt engineering. You should also know the basics of how to document Python code.

Benefits of Using ChatGPT for Documenting Python Code

Having high-quality, up-to-date documentation is critical for any software project. Poor documentation can cause a project to fail or go unnoticed even if the codebase is well written and the project’s main idea is innovative and useful.

Writing good documentation takes considerable time and effort. That’s why using large language models (LLMs) like ChatGPT can be a viable alternative for providing your projects and code with proper documentation.

Some of the benefits of ChatGPT for documenting Python code include the following:

  • Increased productivity: It allows automation of tasks related to code documentation and its maintenance, which saves you considerable time and effort.
  • Improved quality: It helps ensure that your documentation is accurate, up-to-date, and comprehensive.
  • Enhanced user experience: It can produce engaging and user-friendly documentation, leading to a better user experience.
  • Reduced costs: It helps reduce the costs of creating and maintaining documentation.
  • Improved compliance: It can help ensure that the documentation complies with standards and regulations, making it more consistent and professional.

With ChatGPT, you can generate cool documentation for your Python code in almost no time. In the following sections, you’ll learn the basics of using ChatGPT as an assistant for creating coherent docstrings and user-friendly external documentation for your Python projects.

Effective ChatGPT Prompts for Writing Docstrings

The primary way to document Python code is through docstrings. In Python, a docstring is typically a triple-quoted string that occupies the first line of modules, functions, classes, and methods. This string has a special meaning for Python, which stores it in an attribute called .__doc__.

Many Python tools, including code editors and IDEs, take advantage of docstrings to provide real-time help when you’re writing your code. Docstrings are also part of Python’s built-in help system, which you can access with the help() function:

Python 
>>> help(str)

Help on class str in module builtins:

class str(object)
 |  str(object='') -> str
 |  str(bytes_or_buffer[, encoding[, errors]]) -> str
 |
 |  Create a new string object from the given object. If encoding or
 |  errors is specified, then the object must expose a data buffer
 |  that will be decoded using the given encoding and error handler.
 |  Otherwise, returns the result of object.__str__() (if defined)
 |  or repr(object).
 |  encoding defaults to sys.getdefaultencoding().
 |  errors defaults to 'strict'.
 |
 |  Methods defined here:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return key in self.
...

In this example, you call help() with the str class as an argument, and you get the class’s documentation page, which includes the class’s docstring:

Python 
>>> print(str.__doc__)
str(object='') -> str
str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or
errors is specified, then the object must expose a data buffer
that will be decoded using the given encoding and error handler.
Otherwise, returns the result of object.__str__() (if defined)
or repr(object).
encoding defaults to sys.getdefaultencoding().
errors defaults to 'strict'.

In this case, you get the class’s docstring by accessing the .__doc__ attribute directly on the str class. As you can conclude, docstrings add a lot of value to your code. They’re the primary documentation that you and other Python developers will use to learn about any Python object.

You can also take advantage of your code’s docstrings when you’re building project documentation with a tool like Sphinx or MkDocs. These tools have plugins and features that allow you to extract the docstrings and make them part of your project’s external documentation, which can save you a lot of time.

Python has well-established conventions for writing good docstrings. Package, module, class, method, and function docstrings all have specific goals and should follow specific guidelines. You can find these guidelines and conventions in PEP 257.

Although PEP 257 provides a standard, you’ll actually find a healthy variety of docstring styles across the Python ecosystem. Here are a few common alternatives:

Read the full article at https://realpython.com/document-python-code-with-chatgpt/ »

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November 08, 2023 at 07:30PM
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