Table of Contents
Characteristics of functional programming
Advantages of functional programming
Simplified Debugging
Convenience of testing
Composability
Functions are first-class objects
Function as Object
Example
Output
Passing functions as parameters
Home Backend Development Python Tutorial Functional programming in Python

Functional programming in Python

Sep 14, 2023 pm 01:49 PM
recursion Iterator lambda function

Functional programming in Python

Functional programming languages ​​are specifically designed to handle symbolic computation and list processing applications. Functional programming is based on mathematical functions. Some popular functional programming languages ​​include: Lisp, Python, Erlang, Haskell, Clojure, etc.

Characteristics of functional programming

The most significant features of functional programming are as follows:

  • Functional programming languages ​​are designed based on the concept of mathematical functions, which use conditional expressions and recursion to perform calculations.

  • Functional programming supports higher-order functions and lazy evaluation features.

  • Like OOP, functional programming languages ​​support popular concepts such as abstraction, encapsulation, inheritance, and polymorphism.

Advantages of functional programming

The following are the advantages -

Modularity - It forces you to break the problem into small pieces. Programs are more modular as a result. Writing a small function that does just one thing is easier to specify and write than writing a large function Perform complex transformations. Small functions are also easier to read and inspect mistake.

Simplified Debugging

These functions are usually small and well-defined, so debugging is simplified. When the program is not working properly, each function is an interface point where you can check that the data is correct.

Convenience of testing

Testing is easier because every function is a possible subject of unit testing. Functions do not rely on system state that needs to be copied before running the test, instead you simply synthesize the correct inputs and then check that the output is as expected.

Composability

When writing functional programs, you will write many functions with different inputs and outputs. Some of these functions will inevitably be specialized for specific applications, but others will be very useful in a variety of programs.

Functions are first-class objects

To support functional programming, a function should have the following conditions, and Python does both: take another function as an argument and return the other function to its caller.

In Python, functions are treated as first-class objects, i.e. we can store functions in variables, return functions from functions, etc.

The following are some examples of displaying functions in Python, which are very useful for understanding decorators.

Function as Object

In this example, functions are treated as objects. Here, the function demo() is assigned to the variable

Example

# Creating a function
def demo(mystr):
   return mystr.swapcase() # swapping the case

print(demo('Thisisit!'))
sample = demo
print(sample('Hello'))
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Output

tHISISIT!
hELLO
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Passing functions as parameters

Passed as a parameter in this function. The demo3() function calls the demo() and demo2() functions as parameters.

Example

def demo(text):
   return text.swapcase()

def demo2(text):
   return text.capitalize()

def demo3(func):
   res = func("This is it!") # Function passed as an argument
   print (res)

# Calling
demo3(demo)
demo3(demo2)
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Output

tHIS IS IT!
This is it!
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