Dependency Injection

Python is a multi-paradigm language, but developers often treat it like Java, forcing every piece of logic into a class structure. This obsession with Object-Oriented Programming frequently leads to "spaghetti code"—not because the code is messy, but because the abstractions are unnecessary. Writing clean Python requires knowing when to use a class and, more importantly, when to walk away from one. Functions Masquerading as Classes A common anti-pattern involves creating a class that contains only an `__init__` and a single method. If your class doesn't store state that multiple methods need to access, or if you never intend to create multiple instances with different data, you have written a function with extra steps. This adds boilerplate and forces the user to instantiate an object just to perform one action. In Python, functions are first-class citizens. If you just need to load data or process a single input, a standalone function is more readable, easier to test, and significantly faster to implement. The Module vs. The Utility Class Developers coming from static languages often create classes filled entirely with `@staticmethod` decorators. These "utility classes" act as namespaces for related functions, like string manipulation or math helpers. However, Python already has a built-in namespace tool: the module. Instead of a `StringUtils` class, create a `string_utils.py` file. This allows you to import exactly what you need without the overhead of a class structure that will never be instantiated. It respects the Pythonic philosophy of simplicity and prevents misleading users into thinking there is internal state to manage. Flattening Inheritance with Composition Deep inheritance hierarchies are brittle. When a sub-class depends heavily on a super-class, a minor change at the top of the tree can break the entire branch. Many developers use inheritance to describe roles—like `Manager` inheriting from `Employee`—but this creates a rigid structure. A better approach is **composition**. By giving an `Employee` a `Role` attribute (perhaps using a StringEnum), you decouple the identity from the behavior. This makes the code easier to extend; adding a new role doesn't require a new class, just a new enum value or instance. Embracing Abstractions and Protocols Hard-coding dependencies inside a method makes testing a nightmare. If a `process_order` function creates its own `EmailService` internally, you can't test the order logic without actually sending an email. The solution lies in Dependency Injection and abstractions. By using Python's `Protocol` from the `typing` module, you can define a contract. As long as an object has a `send_email` method, the order processor doesn't care if it's a real SMTP client or a mock object for testing. This decoupling is the hallmark of professional software design. The Balance of Encapsulation Encapsulation protects the internal state of an object, ensuring that data remains consistent. If you have a `BankAccount`, you shouldn't let external code modify the `balance` directly; you should use `withdraw` and `deposit` methods that include validation logic. However, don't over-engineer simple data containers. If a class is just a name and an age, adding Getters and Setters is just noise. For these cases, Data Classes provide a clean, concise way to represent data without the boilerplate, allowing direct attribute access while maintaining the benefits of a structured object. Clean Python isn't about using every feature the language offers. It is about choosing the simplest tool that solves the problem. Whether that's a function, a module, or a well-designed class, the goal remains the same: maintainability and clarity.