Guidelines for Designing Python Libraries
Creating a Python library is relatively simple, but designing it correctly from the beginning can make a big difference for maintainability, usability, and long-term stability. In this article we will review several good practices to follow when designing a library intended to be reused by other developers.
Define a Clear Project Structure
A well-organized project structure helps separate your library code from configuration files, tests, and documentation. A commonly recommended layout is the src layout:
my-library/
├── src/
│ └── mylibrary/
│ ├── __init__.py
│ └── greeter.py
├── tests/
├── README.md
├── LICENSE
└── pyproject.toml
This structure prevents Python from accidentally importing modules from the project root instead of the installed package.
Project Metadata and Dependency Management
Modern Python libraries define their metadata using pyproject.toml. Example:
[project]
name = "mylibrary"
version = "0.1.0"
description = "Example Python library"
readme = "README.md"
requires-python = ">=3.10"
authors = [
{ name = "Your Name", email = "you@email.com" }
]This file also helps to manage your dependencies. The following file specifies a single dependency. Every dependency you add is a dependency your user must also install.
[project]
name = "my_awesome_library"
version = "0.1.0"
dependencies = [
"requests >= 2.25.1",
]Follow PEP 8 and Naming Conventions
Consistency is the most important factor in library design. Most Python developers expect your code to follow PEP 8, the official style guide for Python code.
Modules and Packages: Use short, lowercase names (e.g., requests, pandas).
Classes: Use PascalCase (e.g., DataProcessor).).
Functions and Variables: Use snake_case (e.g., calculate_mean).).
Constants: Use all capital letters (e.g., MAX_RETRIES).
Minimalist Public API
A well-designed library should expose only what is necessary. This reduces the "cognitive load" for the user and allows you to change internal implementation details without breaking the user's code. You can explicitly declare the public API in __init__.py. For instance, the following file will expose only the functions greet and sum.
from .greeter import greet
from .operations import sum
__all__ = ["greet", "sum"]
From a consumer perspective, the following import will be affected, and it will only add the exposed functions.
from mylibrary import *
This would not prevent a consumer from including other functions like:
from mylibrary.operations import sub
Use Type Hinting (PEP 484)
Since Python 3.5, type hints have become a standard for professional libraries. They help users understand what data types a function expects and what it returns, and they enable better IDE autocompletion. The following example shows a function with type hints:
from typing import List, Optional
def filter_data(items: List[int], threshold: int) -> List[int]:
"""Filters a list of integers based on a threshold."""
return [item for item in items if item > threshold]
The Mypy library can help with static type checking, ensuring that you're using variables and functions in your code correctly.
For linting and formatting you can use Ruff
Proper Error Handling
Avoid raising generic exceptions like Exception or RuntimeError. Instead, define custom exception classes for your library. This allows users to catch specific errors related to your tool.
class LibraryError(Exception):
"""Base class for exceptions in this library."""
pass
class ConnectionError(LibraryError):
"""Raised when the library cannot connect to the service."""
pass
def connect_to_service():
if not service_available:
raise ConnectionError("The service is offline.")Write Tests
Libraries should always include tests to guarantee stability across versions. A typical structure looks like:
tests/
└── test_greeter.pyExample test:
from mylibrary import greet
def test_greet():
assert greet("Alice") == "Hello Alice!"Frameworks like pytest are widely used for this purpose.
Documentation and Docstrings
A library is only as good as its documentation. Every public module, class, and function should have a docstring. The most common format is the Google Style or NumPy Style.
def multiply(a: float, b: float) -> float:
"""
Multiplies two numbers and returns the result.
Args:
a (float): The first multiplier.
b (float): The second multiplier.
Returns:
float: The product of a and b.
"""
return a * bVersioning
Follow Semantic Versioning (SemVer). This helps users understand the impact of updating your library:
Major version (1.0.0): For incompatible API changes.
Minor version (0.1.0): For adding functionality in a backwards-compatible manner.
Patch version (0.0.1): For backwards-compatible bug fixes.