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Why are Lambda Operators Anonymus?

Sulochana Kamshetty

3 years ago

Introduction
Anonymous Functions
What is Lambda Function All About…
Why Use Lambda Functions?

Introduction

Welcome to the new content from insideaiml, to get the idea what exactly lambda operators are and why are they anonymous…

Lambda operators are more incisive syntax than any other functions provided in python programming language

Lambda functions are mostly used for higher order functions, provided one or more arguments and return one or more functions.

Important note:- Lambda functions are restricted to single expression.

By this end of the article we will be covering all the below points for better understanding with few examples

How to write lambda functions

Which functions in the Python standard library leverage lambdas

When to use or avoid Python lambda functions

Let us even cover what is Anonymous functions is all about:

Anonymous Functions

These are few functions which are interchangeably used for lambda functions

Anonymous functions

Lambda functions

Lambda expressions

Lambda abstractions

Lambda form

Function literals

What is Lambda Function All About…

This function is been created with lambda keyword, considering two arguments for creating lambda functions

To create a lambda function first write keyword lambda followed by one of more arguments separated by comma (,), followed by colon a (:), followed by a single line expression.

X= 1

Y = 2

>>> lambda x, y: x + y

Output:-

Lets understand few examples with different functions…

Add 20 to
argument a, and return the result:

x = lambda a : a + 20

print(x(7))

output:-

Explanation:- This above function above defines a lambda expression that takes two arguments and returns their sum. The expression has intake the sum of two different parameters and added them for the output

Lets understand how to multiply the two different arguments using lambda function..

x = lambda a, b : a * b
print(x(4,6))

output:-

Explanation :- since the code is executed taking two argument, using lambda function for multiplying these inputs, to get the output of multiplication of two arguments.

Example:- 3

Lets take 3 arguments and perform with lambda functions like , x,y,z

x
= lambda x,y,z
:  x+y+z
print(x(5, 6, 2))

output:-

Exaplination:- let us make this clear understanding about this above example 3… i.e we have provided three arguments those are x,y,z and commanded to sum up all these values to get the proper output..

Example:- 4

Lets understand the additional function about Immediately Invoked Function Expression which is (IIFE, pronounce “iffy”) with the coding part as well with example

Lets create a lambda function in which am going to work with creating variable simultaneously working with any function we perform with i.e

(lambda x, y: x + y)(2, 3)

Output:-

Explaination:- in python until we assign anything to the variable the values are not stored but with the help of lambda function I have stored the value as well as made the addition of these provided values with output..

Example5:- assigning the function, directly to the variable provided lambda function and getting the output with function we are looking for..

h = lambda x: x*x*x 

print(h(7))

output :

Explanation:- the power of 7 is 343, we got this with the help of lambda function in single code of line.

Lambda functions can be used along with built-in functions like filter(), map() and reduce()

Why Use Lambda Functions?

The specialty of lambda function is used since its anonymous function inside another function.

Lets understand this with the help of def function used in python

def myfunc(n):
  return lambda a : a * n

A lambda function can take any number of arguments, but can only have one expression.

r = lambda x, y: x * y

r(12, 3)   # call
  the lambda function

Expected Output:

hope this is clear about lambda operators and why they are anonymous in nature..

For more such blogs/courses on data science, machine learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.

Thanks for reading…Happy Learning…

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