Documenting Your Library's API: Best Practices

Every time I start using a new Python library, I find myself immediately looking for two things: code examples and the API reference. While examples show you how to use a library, it’s the API reference that becomes your constant companion - the place you return to again and again to check exactly how that one function works or what parameters that one class accepts.

In this post, I’ll share what I’ve learned about creating API documentation that your users will actually want to read (and can find what they need in). We’ll be using Sphinx with its powerful autodoc extension, which turns your docstrings into a beautiful, searchable reference.

The Art of API Organization

Think of your API documentation like a well-organized library (the book kind, not the code kind). Just as a library has sections, categories, and a catalog system, your API reference needs a clear structure that helps users find what they’re looking for.

Here’s how I organize my API documentation:

1. Create a dedicated API section in your docs (usually api.rst or an api/ directory)
2. Start with an index page that gives an overview
3. Break down the documentation by module or functionality

Here’s what a good API index page (api/index.rst) might look like:

API Reference
============

Welcome to the complete API reference for YourLibrary. Here you'll find detailed 
documentation for every public module, class, and function.

.. toctree::
   :maxdepth: 2
   :caption: Library Components:

   core
   utils
   plotting
   data_handling

Making autodoc Work for You

The real magic happens when you combine Sphinx’s autodoc extension with well-written docstrings. Here’s how I document different components:

For Entire Modules

.. automodule:: yourlibrary.core
   :members:
   :show-inheritance:
   :member-order: bysource  # Keep the order from your source code

For Individual Classes

.. autoclass:: yourlibrary.core.DataProcessor
   :members:
   :inherited-members:  # Show what it inherits
   :special-members: __init__  # Usually want to show the constructor

For Standalone Functions

.. autofunction:: yourlibrary.utils.process_data

Pro tip: Use the :members: option judiciously. While it’s tempting to document everything, sometimes less is more. You probably don’t need to expose every internal helper method.

The Secret to Great API Documentation

After years of writing (and more importantly, reading) API documentation, I’ve found these practices make the biggest difference:

1. Focus on the Public Interface

Your API documentation should be like a good user manual - it tells people how to use your library, not how it works internally. Document what users need to know, not what you needed to know to write it.

2. Use Type Hints (But Don’t Rely on Them Completely)

Modern Python’s type hints are great:

def process_data(data: pd.DataFrame, columns: list[str], inplace: bool = False) -> pd.DataFrame:
    """Process specific columns in a DataFrame.
    
    Args:
        data: The DataFrame to process
        columns: List of column names to process
        inplace: Whether to modify the DataFrame in place
    
    Returns:
        The processed DataFrame
    
    Raises:
        ValueError: If any column in `columns` doesn't exist in `data`
    """
    # ... implementation ...

Sphinx can use these type hints to enhance your documentation, but don’t skip writing clear parameter descriptions - types alone don’t tell the whole story.

3. Cross-Reference Everything

Make it easy for users to navigate your docs. I use these roles constantly:

See :class:`~yourlibrary.core.DataProcessor` for the main processing class.
The :func:`~yourlibrary.utils.validate_input` function handles validation.
Check out the :doc:`/tutorials/getting_started` guide for examples.

The ~ prefix is a neat trick - it shows just the class name (DataProcessor) instead of the full path (yourlibrary.core.DataProcessor) in the link text.

4. Group Related Items

For complex modules or classes, I like to group related functionality:

class DataProcessor:
    """Process and transform data.
    
    Input/Output Methods
    ------------------
    - load_data: Load data from a file
    - save_data: Save processed data
    
    Processing Methods
    ----------------
    - clean: Remove invalid entries
    - transform: Apply transformations
    """

Handling the Tricky Stuff

Some Python features need special attention in documentation:

Properties

class Dataset:
    @property
    def shape(self) -> tuple[int, int]:
        """The dimensions of the dataset (rows, columns).
        
        Returns:
            A tuple of (n_rows, n_cols)
        """
        return self._data.shape

Decorated Functions

Always use functools.wraps to preserve docstrings:

from functools import wraps

def validate_input(func):
    @wraps(func)  # This preserves the original function's docstring
    def wrapper(*args, **kwargs):
        # ... validation logic ...
        return func(*args, **kwargs)
    return wrapper

Making Sure It All Works

Here’s my checklist for reviewing API documentation:

1. Build the docs (make html) and actually read them
2. Try to find specific functions/classes as if you were a user
3. Click all the cross-references to make sure they work
4. Show the docs to someone who hasn’t seen your code before

The Real Test

Want to know if your API documentation is good enough? Watch someone try to use your library without asking you questions. If they can find what they need in your docs, understand how to use it, and actually get it working - congratulations, you’ve done it right.

Remember: Good API documentation isn’t just a reference - it’s a guide that helps users understand your library’s capabilities and how to use them effectively. Take the time to do it right, and your users will thank you (usually by not needing to open issues asking how things work).