How does functional programming deal with state and mutable data?

Functional programming deals with state and mutable data by avoiding or minimising their use, instead favouring immutable data and pure functions.

In functional programming, the concept of state is often avoided or minimised. This is because state introduces complexity and can lead to unpredictable behaviour in a program. Instead, functional programming favours the use of pure functions, which are functions that always produce the same output given the same input and do not have any side effects. This makes the behaviour of a program more predictable and easier to reason about.

Mutable data is also avoided in functional programming. Instead, data is often treated as immutable, meaning it cannot be changed once it has been created. If a change is needed, a new version of the data is created, rather than modifying the existing data. This approach can help to prevent bugs that can occur when data is unexpectedly changed, and it also makes it easier to reason about how data flows through a program.

However, completely avoiding state and mutable data is not always practical or efficient. For example, in some cases, using mutable data can lead to more efficient algorithms. In these cases, functional programming languages often provide ways to manage state and mutable data in a controlled manner. For example, they may provide special types of data structures that encapsulate mutable data, or they may provide mechanisms for managing state in a controlled way, such as monads in Haskell.

In summary, functional programming deals with state and mutable data by avoiding or minimising their use, instead favouring the use of pure functions and immutable data. However, when state or mutable data is necessary, functional programming languages provide ways to manage them in a controlled and predictable manner. This approach helps to reduce complexity and make programs easier to understand and reason about.