The two most famous paradigms people use for Python are OOP and FP. Both represent an altogether different concept with usage scenarios. Understanding between two paradigms can be divergent to help developers decide which should be used in a project.
OOP refers to objects that are part of classes. In addition, it focuses on encapsulation, inheritance, and polymorphism:
Encapsulation: It hides the implementation details of an object, showing access to only some parts of it and facilitates controlled interaction through public methods, but internal states are kept private.
Inheritance: This allows new classes to inherit properties and methods from other classes that promote reusing code.
Polymorphism: This enables objects of different classes to be treated as objects of a common superclass.
OOP is the most efficient for modelling real-world entities and, thus best suited to applications such as inventory systems or user management, where data and behaviour are tightly coupled. For instance, the class Car can encapsulate such properties as colour, model, and methods drive() and stop().
On the other hand, FP focuses on function evaluation and not on operations related to state manipulation. It follows the following characteristics:
Pure Functions: these functions are determinist; if used with the same input they would always return the same output and do not produce any side effects thus tests are not complex.
Immutability: data cannot be changed after it is produced; that is to say it does not contain any reference toward its changing state, which reduces bugs of shared state.
Higher-Order Functions: A function can take other functions as parameters or can return them from it. This is creating some tight abstractions.
FP comes in handy when there is a requirement for high parallelization of pipelines or data transformation pipelines. For instance, functional composition allows processing lists without changing source data or keeping code more maintainable.
OOP: All data is an object; every object maintains its state and is operated by some method.
FP: Because the data flows from one function to another, it's not tied into any state. This abstraction makes the code much more understandable and side effect-free.
OOP: Concerned with the question of how one ought to structure one's code in terms of objects representing domain entities.
FP: Interested in what ought to be done to data by application of functions.
OOP: Can rapidly blossom into a nightmare of complex hierarchies if not kept in check. Does provide clearly defined avenues for adding functionality based on inherited code, though.
FP: It prefers flat code structures that are easier to test as chances for side effects happening are lesser. However, managing the states among multiple functions is havoc.
OOP might have a minor advantage in situations where instances need to be created or updated very frequently because the states happen to be mutable.
FP will shine through when high concurrency is required since it emphasizes immutability and statelessness.
This application models complex systems with interrelated entities.
This application would benefit from a well-defined structure, encapsulation, and inheritance.
A part of a team that's already familiar with OOP concepts.
This has huge datasets that you need to process in parallel.
This application is distinguished by such high abstraction levels and functions reuse
The minimum side effects to help debug and test.
Python supports both paradigms where Python programmers can use the strengths of one paradigm where it is applicable. For example, you could define classes as in OOP but then use functional techniques like map and filter to process data:
The choice between OOP and FP in Python depends on the project's needs. OOP works well for structured systems, while FP promotes cleaner code with immutability and pure functions. Understanding both paradigms helps you make better decisions. Often, the best approach is a hybrid that combines the strengths of both.