Learn about functional programming in Python, pure functions, map(), filter(), zip(), reduce() concepts

Need for Functional Programming(FP)

• The usage of Functional Programming provides us with separation of concern where we can separate data and logic separately. Hence, the code becomes clear and easy to understand to a developer.
• Functional Programming follows the DRY (Do not Repeat Yourself) principle.
• Code which follows Functional Programming practice is memory-efficient.
• The codebase which implements Functional Programming will also be easy to extend and maintain.

Pure functions

• One of the important concepts in functional programming is the usage of pure functions.
• A function is said to be a Pure function if:

1. Given the same input, the function will always return the same output.

2. The function must not produce any side effects.

• Side effects are things that a function does that affect the outside world, that is they change the state of the program.
• Changing the data in a variable, printing output can be considered as some examples of side effects of a function.

Consider the following simple example:

The function square will always return only the square of a given number and will not change anything in the outside world. This type of functions are also called declarative functions

Note:

• But, technically it’s not possible to use pure functions everywhere as we may need to change the state of the code.
• Although, it’s a good practice to use pure functions as many places as possible.
• And the fact here is that it highly probable to face bugs and errors occur in non-pure functions rather than in pure functions.
• Python provides us with some useful pure functions which are built-in python.

Pure Functions in python:

1. map()
2. filter()
3. zip()
4. reduce()

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