The Library Author's Dilemma: Managing Python Dependencies

Building a Python library often means standing on the shoulders of giants – leveraging the fantastic work already done in other packages. Need to make HTTP requests? Use requests or httpx. Need numerical computing? numpy is your friend. Adding dependencies can save you immense amounts of time and effort.

But here’s the catch: every dependency you add is a choice with consequences. For library authors, managing dependencies isn’t just about making your life easier; it’s about being a good citizen in the Python ecosystem and not making your users’ lives harder. It’s a careful balancing act.

To Add or Not to Add: The Dependency Cost

Before typing pip install some-cool-package and adding it to your install_requires, pause and consider the true cost of adding a dependency:

• Increased Complexity: Your library now depends on another moving part, which might have its own dependencies (transitive dependencies), increasing the overall surface area for potential issues.
• Potential Conflicts: Your chosen dependency (or its specific version) might clash with dependencies required by your user’s application or other libraries they use. This is a primary cause of the infamous “dependency hell.”
• Larger Footprint: More dependencies mean more code for your users to download and install.
• Maintenance Burden: You inherit the responsibility of tracking updates, security vulnerabilities, and potential deprecations or breaking changes in your dependencies.
• License Compatibility: You need to ensure the dependency’s license is compatible with your library’s license and intended use.

Ask yourself:

• Does this dependency save significantly more effort than the cost of managing it?
• Is the functionality core to my library, or could it be optional?
• Is the dependency stable, well-maintained, and widely trusted?

Sometimes, avoiding a dependency (even if it means writing a bit more code yourself, especially for simple tasks) can be the better long-term choice for a library meant for wide distribution.

Choosing Dependencies Wisely

If you decide to add a dependency, choose carefully:

• Stability & Maintenance: Prefer libraries with a stable release history, active maintenance, and good community support. An abandoned dependency is a future problem.
• Scope: Does it do one thing well, or is it a massive framework where you only need a tiny part?
• Popularity (sometimes): Widely used libraries are often better tested and more likely to be compatible with users’ existing environments.
• License: Double-check its license!
• Security: Does the project have a good track record regarding security vulnerabilities?

Version Constraints: The Tightrope Walk

This is where library authors really feel the pressure. How do you specify the version of requests your library needs?

• Pinning Exact Versions (requests == 2.28.1): Looks safe, right? You know your library works with exactly this version. BUT, this is generally bad practice for libraries. If User A’s application needs requests == 2.27.0 and your library demands 2.28.1, they can’t use both! You’ve created a conflict.
• Minimum Versions (requests >= 2.25.0): Better. You specify the oldest version you know works. This allows users to potentially use newer versions required by other parts of their stack. However, if requests releases 3.0.0 with breaking changes, this specifier won’t prevent pip from installing it, potentially breaking your library for the user.
• Upper Bounds (requests < 3.0.0): Often used with a minimum version (>= 2.25.0, < 3.0.0). This prevents installation of known future breaking versions. The downside? You must actively maintain this. When requests 3.0.0 is released and you confirm your library works with it (or update your library to work), you need to release a new version of your library with an updated upper bound (< 4.0.0). Forgetting to do this blocks users from legitimate updates.
• Compatible Releases (requests ~= 2.25): This is often the sweet spot for libraries. It translates to >= 2.25.0, == 2.25.* (or similar logic depending on the exact specifier). It allows users to get PATCH updates (bug fixes like 2.25.1) from the dependency but prevents MINOR (2.26.0) or MAJOR (3.0.0) updates that are more likely to contain new features or breaking changes. This balances stability with receiving important fixes.

Key Principle: Libraries should generally specify the loosest dependency versions that are known to work. Applications, which control the final environment, can (and often should) pin versions much more tightly.

The Tangled Web: Transitive Dependencies

Remember, your dependencies have dependencies. You might only add requests, but requests itself depends on charset-normalizer, idna, urllib3, and certifi. You inherit this entire tree.

• Use tools like pipdeptree to visualize your full dependency tree.
• Conflicts are more likely in deeper or wider trees.

Keeping it Optional: extras_require

What if your library can use pandas for extra features, but doesn’t require it for core functionality? Use optional dependencies (defined in the project.optional-dependencies table in pyproject.toml, previously extras_require in setup.py/setup.cfg).

# pyproject.toml
[project]
name = "my-library"
version = "1.2.0"
dependencies = [
    "requests ~= 2.25",
]

[project.optional-dependencies]
pandas = ["pandas >= 1.4.0"]
plotting = [
    "matplotlib >= 3.5",
    "seaborn ~= 0.11"
]
all = [
    "my-library[pandas]",
    "my-library[plotting]"
]

Users who want the extra features install your library like this: pip install my-library[pandas,plotting] or pip install my-library[all]. This keeps your core library lightweight.

Staying Vigilant: Maintenance & Security

Dependency management isn’t a one-time task.

• Regularly Review: Check for updates to your dependencies.
• Use Automation: Tools like GitHub’s Dependabot can automatically create pull requests to update dependencies.
• Security Scanning: Use tools like pip-audit or safety in your CI pipeline to check for known vulnerabilities in your dependencies.
• Test Updates: Use tox to test your library against different versions of its dependencies (e.g., minimum and latest compatible versions) to catch integration issues early.

Managing dependencies thoughtfully is a hallmark of a well-maintained Python library. By carefully considering whether to add a dependency, choosing stable packages, specifying sensible version constraints (usually ~=), and utilizing optional dependencies, you can create a library that is both powerful and plays well with others in the rich Python ecosystem.