Define functional programming and explain its key principles.

Functional programming is a programming paradigm that revolves around the concept of treating computation as the evaluation of mathematical functions. It is based on a set of key principles that guide the design and implementation of programs, with an emphasis on immutability, pure functions, higher-order functions, function composition, recursion, referential transparency, lazy evaluation, and a strong type system.

1. Immutability: In functional programming, data is immutable, meaning it cannot be changed after it is created. Instead of modifying existing data, functional programs create new data structures. This promotes clarity, simplifies reasoning about code, and helps prevent bugs related to mutable state.
2. Pure Functions: A pure function is a function that always produces the same output for a given input and has no side effects. It operates solely on its input parameters and does not modify external state. Pure functions are deterministic, which means they provide predictable and reliable results. They are easier to test, reason about, and parallelize, making code more robust and maintainable.
3. Higher-Order Functions: Functional programming treats functions as first-class citizens, allowing them to be assigned to variables, passed as arguments to other functions, and returned as results. Higher-order functions enable code modularity and reusability, as they can abstract common patterns and behaviors.
4. Function Composition: Functional programming encourages the composition of smaller functions to create more complex functions. Functions can be combined together, forming pipelines or chains of operations. This promotes code reuse, enhances readability, and enables the construction of expressive and concise code.
5. Recursion: Functional programming favors recursion over iterative loops. Recursive functions allow for elegant and concise solutions to problems that involve working with nested or recursive data structures. They provide a natural way to express algorithms and solve problems in a declarative manner.
6. Referential Transparency: Referential transparency means that an expression can be replaced with its corresponding value without changing the program's behavior. In functional programming, pure functions and immutable data ensure referential transparency. This property facilitates reasoning about code and enables equational reasoning, making programs easier to understand and debug.
7. Lazy Evaluation: Functional programming languages often employ lazy evaluation, which means that expressions are not evaluated until their results are actually needed. This allows for more efficient and optimized execution, as only necessary computations are performed. Lazy evaluation can improve performance and enable working with infinite or large data streams.
8. Strong Type System: Functional programming languages typically feature a strong and expressive type system. Static typing and type inference help catch errors at compile time, provide better code documentation, and enhance program correctness. Types enable reasoning about program behavior and can aid in program optimization.

Functional programming promotes writing code that is concise, modular, and maintainable. It facilitates reasoning about program behavior, supports parallel and concurrent execution, and provides a foundation for building reliable and scalable software systems. By adhering to the principles of functional programming, developers can create code that is more robust, testable, and adaptable to changing requirements.