Python Docstring Usage Guide (With Examples)

Python Docstring Usage Guide (With Examples)

Imagine revisiting your code after a significant period; without proper documentation, you’d be lost.

Python Docstrings are used to remedy this. They are integral for code documentation, serving as your personal notes and guide for other developers, explaining the purpose and functionality of different parts of your code.

This comprehensive guide aims to familiarize you with Python Docstrings, enabling you to write effective and meaningful ones. You’ll understand not just the syntax and structure of Python Docstrings but also their importance in code documentation.

So, gear up to dive deep into the world of Python Docstrings!

TL;DR: What are Python Docstrings?

Python Docstrings are string literals used to document your code. They describe what a module, class, function, or method does, serving as a guide for yourself and other developers. You can use a docstring with triple quotations ("""), and the syntax, """[insert Documentation Here]""". They enhance code understandability and maintainability. For a more advanced understanding and tips on writing effective Docstrings, read on!

def hello_world():
    """This function prints 'Hello, World!'"""
    print('Hello, World!')

Understanding Python Docstrings

A Python Docstring is a string literal that you write as the first statement in a module, function, class, or method definition. It serves as a concise guide to your Python code, offering a brief explanation of the code’s purpose.

The syntax and structure of a Python Docstring are simple. It starts and ends with triple quotes, either single or double. Here’s a basic example of a Python Docstring:

def hello_world():
    """This function prints 'Hello, World!'"""
    print('Hello, World!')


In this example, the Docstring """This function prints 'Hello, World!'""" explains what the hello_world function does. It’s a straightforward explanation, but in the context of complex code, such explanations can be a lifesaver.

Why should you use Docstrings in your Python code?

Docstrings improve code readability, allowing anyone who reads your code to understand its purpose without having to decipher it. They also enhance code maintainability, guiding you through the process when you need to update your code in the future. Moreover, Docstrings can be utilized to automatically generate documentation for your code using tools like Sphinx.

A little investment in writing effective Docstrings can save you a significant amount of time and headaches in the future.

Where To use DocStrings

Python Docstrings are not just limited to functions. They can also document classes and modules. For instance, you can use a Docstring at the beginning of a Python file to describe the module’s functionality. Similarly, you can use a Docstring in a class to explain its purpose and methods.

Here is an example where Docstrings are used in a python module:

""" This is a python module that has functions to calculate square and cube of a number.
    This module demonstrates the use of docstrings in a python module.

def square(num):
    """ Function to calculate square of a number.

        num (int): accepts an integer value.

        int: Square of input integer.
    return num * num

def cube(num):
    """ Function to calculate cube of a number.

        num (int): accepts an integer value.

        int: Cube of input integer.
    return num * num * num

In the above example, Docstrings are used to describe what the module does at the beginning of the file. Also, it explains what each function does, the parameters they take and what they return.

Now, let’s see an example where Docstrings are used in a class:

class MathOperations:
    """ This is a class to perform basic mathematical operations.
        The class demonstrates the usage of docstrings in a python class.

    def __init__(self, num1, num2):
        """Constructor to initialize the attributes of the class.

            num1 (int): accepts an integer value.
            num2 (int): accepts an integer value.
        self.num1 = num1
        self.num2 = num2

    def add(self):
        """ Function to calculate sum of the numbers.

            int: Sum of num1 and num2.
        return self.num1 + self.num2

    def multiply(self):
        """ Function to calculate product of the numbers.

            int: Product of num1 and num2.
        return self.num1 * self.num2

In the above example, the class and its methods are documented using Docstrings. The __init__ method’s docstring explains what the constructor does and the Docstrings for the add and multiply methods explain their usage.

Docstring Formatting

Different projects may adopt varying Docstring formats. The most prevalent formats are reStructuredText, Google, and Numpydoc. reStructuredText is the standard Python Docstring format, used by tools like Sphinx for documentation generation. Google and Numpydoc formats, on the other hand, offer enhanced readability and are favored in data science projects. Choose a format that aligns with your project and maintain consistency.

Here are some examples for each:

1. reStructuredText:

def power(base, exponent):
    Calculate the power of a number.

    :param base: The base number
    :type base: int
    :param exponent: The exponent number
    :type exponent: int
    :returns: The result of base raised to the power of exponent.
    :rtype: int
    return base ** exponent

2. Google Style:

def power(base, exponent):
    Calculate the power of a number.

        base (int): The base number
        exponent (int): The exponent number

        int: The result of base raised to the power of exponent.
    return base ** exponent

3. Numpydoc

def power(base, exponent):
    Calculate the power of a number.

    base : int
        The base number
    exponent : int
        The exponent number

        The result of base raised to the power of exponent.
    return base ** exponent

Choose one of them that suites your project and maintain that consistency across your project. These are specifically helpful when we use documentation generation tools like Sphinx, pydoc, etc.

Writing Effective Python Docstrings

Having grasped the fundamentals of Python Docstrings and their significance, let’s delve into the best practices for crafting them. Here are some key pointers to bear in mind:

  1. Clarity and Conciseness: Your Docstrings should lucidly delineate what the code accomplishes. Steer clear of unnecessary technical jargon and strive for simplicity.
  2. Comprehensive Information: For functions and methods, elucidate what the parameters represent, what the function accomplishes, and what it returns. For classes, describe their purpose and their methods.
  3. Consistent Style: Python supports multiple Docstring formats like reStructuredText, Google, and Numpydoc. Pick one that aligns with your project requirements and adhere to it.

Let’s examine an instance of a well-crafted Docstring:

def add_numbers(a, b):
    """Add two numbers together.

        a (int): The first number.
        b (int): The second number.

        int: The sum of the two numbers.
    return a + b

In this example, the Docstring succinctly explains the add_numbers function’s purpose, its parameters, and its return value. While the function is simple, the Docstring enhances its comprehensibility.

Advanced Python Docstrings

Python Docstrings, while primarily a documentation tool, also play a pivotal role in several advanced aspects of Python, including generating documentation, testing, and providing type hints. Let’s delve into these advanced applications of Python Docstrings.

Automatic Documentation with Sphinx

Docstrings prove instrumental in generating documentation with tools like Sphinx. Sphinx, a tool designed for creating intelligent and aesthetically pleasing documentation, leverages the Docstrings in your code for this purpose.

All you need to do is ensure your Docstrings are comprehensive, and Sphinx handles the rest, facilitating easy maintenance of up-to-date documentation for your project.

However, here are the steps you can take to create documentation using Sphinx:

  1. First, Install Sphinx via pip:
pip install Sphinx
  1. Navigate to the directory where your project is present. Then run the following Sphinx command to create a source directory for your documentation files:

After following the interactive setup, this command will create a file, which is used to configure Sphinx, and an index.rst file, which is used as the root document and master to contain the table of contents tree.

  1. After the setup, you could leverage sphinx-apidoc package to generate reStructuredText files from your Python scripts like so:
sphinx-apidoc -o source/ ../your-python-scripts-directory

This command tells Sphinx to auto-generate documentation files from your python script files and put them in the source directory.

  1. Last step is to run the following Sphinx command to build the HTML documentation from your reStructuredText files:
make html

All of these steps together will generate beautifully formatted HTML documentations for your code. Giving it a try and see for yourself!

For more information, you can visit the Sphinx official documentation here.

Doctest Module

Secondly, Docstrings play a role in testing your code using the doctest module. Doctest searches your Docstrings for interactive Python sessions and runs these sessions to verify their functionality.

This allows you to write tests for your code directly in your Docstrings, bolstering the assurance that your code performs as intended.

def add(a, b):
    """Return the sum of a and b.

    >>> add(1, 2)
    >>> add(-1, 1)
    return a + b

In this example, the Docstring includes two doctests, which test the add function with different inputs.

Docstrings for Type Hints

Next, you can leverage Docstrings to provide type hints. Type hints, a relatively new feature in Python, allow you to specify the expected type of function arguments and return values. Including these type hints in your Docstrings further enhances the self-explanatory nature of your code.

Let’s explore an example of regular Python type hints and their counterpart in Docstrings.

Here is an example of using Python type hints:

def power(base: int, exponent: int) -> int:
    Calculate the power of a number.

        base: The base number.
        exponent: The exponent number.

        The power of the base number raised to the exponent.
    return base ** exponent

In this example, base: int and exponent: int are type hints for the function’s arguments, indicating that both base and exponent should be integers. -> int is a type hint for the return value, specifying that this function returns an integer.

Now, here’s the same function but with types specified in the Docstring instead:

def power(base, exponent):
    Calculate the power of a number.

        base (int): The base number.
        exponent (int): The exponent number.

        int: The power of the base number raised to the exponent.
    return base ** exponent

In this Docstring, the types of arguments and return value are mentioned directly within the description. This makes the function easier to understand.

Both of these methods have their own merits:

  1. Python Type Hints

– They are more syntactically obvious, as they sit right next to variable names in function signatures.
– They can be used by static type checkers, linters, and IDE features to detect errors before execution.

  1. Docstring Type Hints

– They are included in the automatically generated documentation whereas Python type hints are not usually included.
– If your project or team uses a standard that includes types in docstrings (like Google, Numpydoc etc.), including types in docstrings can make your code more consistent with the rest of the project.

Using both methods can lead to redundancy and requires updating in two places if types change. However, it can be beneficial when you want the advantages of both methods. In conclusion, the choice to use type hints, Docstring type hints or a combination of both depends on the specific needs of your project. Consideration should be given to the context and functionality of your code, consequently choosing the method that best aligns to your project codebase, team or personal preferences.

Best Practices for Python Coding

While Python Docstrings are a fundamental tool for code documentation, they form just one aspect of writing clean, readable Python code. There are several other best practices you should adhere to, ensuring your Python code is professional, maintainable, and easy to understand.

Best PracticeDescription
Clean CodeCode should be easy to understand, debug, and modify.
Naming ConventionsFollow Python’s established naming conventions for variables, functions, classes, and constants.
CommentsUse comments to explain how a piece of code operates.
Code FormattingFollow PEP 8 style guide for consistency and readability.

Let’s delve into some of these best practices.

Clean Code

The foremost rule of writing professional Python code is maintaining cleanliness. Clean code is easy to understand, debug, and modify. It’s code that speaks for itself. To write clean code, opt for meaningful variable names, use whitespace effectively, and steer clear of complex nested structures. Remember, the objective is to make your code as readable as a book.

Naming Conventions

Python has established a set of naming conventions that you should adhere to. For instance, variable and function names should be lowercase, with words separated by underscores (e.g., my_variable). Class names should follow CamelCase (e.g., MyClass). And constants should be in all uppercase, with words separated by underscores (e.g., MY_CONSTANT). Adhering to these conventions lends consistency and readability to your code.


While Docstrings document the purpose of modules, classes, functions, and methods, comments elucidate how a piece of code operates. You should accompany any non-obvious code with comments. However, remember that while good code explicates what it does, comments should explicate how it does it.

Code Formatting

Python offers a style guide named PEP 8, which you should follow to maintain consistency and readability in your code. It covers topics such as indentation, line length, whitespace, and much more. Additionally, tools like pylint and flake8 can check your code for PEP 8 compliance.

While Python Docstrings form an integral part of code documentation, they are merely one piece of the puzzle. By also adhering to these Python coding best practices, you can ensure your code is not just well-documented but also clean, consistent, and professional.

Tools for Better Code Documentation

Crafting effective Python Docstrings and maintaining clean code can be a daunting task, especially in extensive codebases. Thankfully, a myriad of tools and Integrated Development Environments (IDEs) are available to aid you in this endeavor.

Let’s explore some of these tools and their role in enhancing Docstrings and code documentation.


PyCharm, a widely-used Python IDE, offers several features to facilitate better Docstring writing. It provides quick fixes for adding missing Docstrings and even boasts a dedicated tool window that displays the Docstring for the symbol at the caret. PyCharm supports various Docstring formats, including reStructuredText, Google, and Numpydoc.


Sphinx, as previously mentioned, is a tool that generates intelligent and aesthetically pleasing documentation from your Python Docstrings. It supports the reStructuredText format and can generate documentation in several formats, including HTML, LaTeX (for printable PDF versions), ePub, and more.


Doxygen, another versatile documentation generator, can be used with Python and several other programming languages. Like Sphinx, Doxygen supports the reStructuredText format and can generate documentation in various formats, such as HTML and LaTeX.

Other Tools

In addition to these tools, linters and formatters play a crucial role in maintaining clean code. Linters like pylint and flake8 can scrutinize your code for errors, bugs, stylistic issues, and suspicious constructs. They can also examine your Docstrings for issues like syntax errors, missing parameters, and formatting errors.

Formatters like Black and YAPF can automatically format your Python code to enhance its readability and ensure PEP 8 compliance. They manage aspects like indentation, line length, whitespace, and much more.

By utilizing these tools and IDEs, you can make the process of writing Docstrings and code documentation more efficient and enjoyable.

Further Resources for Python Strings

If you’re interested in learning more ways to handle strings in Python, here are a few resources that you might find helpful:

Wrapping Up: Python Docstrings

Through the course of this comprehensive guide, we’ve uncovered the immense power of Python Docstrings as a tool for code documentation, helping your code remain understandable and maintainable. They are a necessity for any professional Python developer.

We’ve went from understanding the basics of Python Docstrings to writing effective ones, and further exploring their advanced uses. We’ve witnessed how Python Docstrings can enhance code readability, bolster code maintainability, and even play a pivotal role in testing and type hinting.

We talked about coding and documentation best practices, and we’ve seen how tools and IDEs like PyCharm, Sphinx, and Doxygen can aid us in crafting superior Docstrings and maintaining immaculate code.

In conclusion, Python Docstrings form an integral part of the Python coding ecosystem. They’re the secret ingredient that makes your Python code stand out. So, keep refining your Docstring writing skills. Your future self and fellow developers will thank you. Happy coding!