Lambda function in Python

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Lambda function in Python are a unique feature that allows for the creation of anonymous functions, which can be called just like any other function.

These functions are often used in situations where a small, one-time use function is needed, and using a full-fledged function with a name and return statement would be overkill.

They are commonly referred to as “lambda functions” due to their origin in lambda calculus, which is a mathematical theory of functions. In this article, we will dive into the world of Python’s lambda functions, exploring their syntax, uses, and benefits.

How to Create or Declare a Lambda Function in Python?

A lambda function in Python is declared using the lambda keyword, followed by a list of arguments separated by commas, and a colon :

The expression to the right of the colon represents the function’s return value.

Example of Python Lambda function:

sum = lambda a, b: a + b
print(sum(3, 4)) 
# Output: 7

Here, lambda a, b: a + b declares a lambda function that takes two arguments a and b, and returns the sum of a and b. The function is assigned to the variable sum, which can then be called like any other function.

We can also use lambda functions directly in other functions, like map() or filter(), without assigning them to a variable.

For example of lambda function with map method in Python:

numbers = [1, 2, 3, 4, 5]
squared_numbers = list(map(lambda x: x**2, numbers))
# Output: [1, 4, 9, 16, 25]

Here, the map() function takes a lambda function lambda x: x**2 and a list of numbers as arguments and applies the lambda function to each element of the list. The resulting list of squared numbers is then assigned to the variable squared_numbers.

Types of Lambda Functions in Python

There are two types of lambda functions in Python:

Single Expression Lambda Functions

These are the most common types of lambda functions and consist of a single expression that is evaluated and returned when the function is called.

Example of single expression lambda function:

square = lambda x: x**2
# Output: 16

Multi-Line Lambda Functions

While single-expression lambda functions are sufficient for most use cases, sometimes a more complex function is needed.

In such cases, we can use multi-line lambda functions.

These functions are defined using the same syntax as single-expression functions but with the expression replaced by a block of code.

Example of multiline lambda function in Python:

adder = lambda x, y: 
    result = x + y
    return result
print(adder(2, 3)) 
# Output: 5

Note: While multi-line lambda functions can contain multiple statements, they cannot contain more than one expression. This means that they cannot contain loops, conditional statements, or other complex control structures. However, they can call other functions, including other lambda functions, to accomplish more complex tasks.

Advantages of using Lambda Functions in Python

There are several advantages to using lambda functions in Python:

  1. Conciseness: Lambda functions are concise and allow for simple, single-line functions to be written without having to create a full-fledged named function. This can make our code more readable and easier to maintain, especially for small, one-time use functions.
  2. Flexibility: Lambda functions can be passed as arguments to other functions, making them extremely flexible and useful for functional programming constructs such as map(), filter(), and reduce().
  3. Clarity: Lambda functions can help improve the clarity of our code by making it easier to see what a particular piece of code is doing, especially when used in combination with functional programming constructs.
  4. Improved Readability: By keeping our code short and to the point, lambda functions can improve the readability of the code, making it easier for others to understand and maintain.
  5. Anonymous Functionality: As anonymous functions, lambda functions can be used in situations where a named function is not necessary or appropriate, such as in functional programming constructs or when passing functions as arguments to other functions.
  6. Improved Performance: Since lambda functions are small, they have less overhead and can be faster than traditional named functions.

Note: Lambda functions offer a powerful tool for writing concise, flexible, and readable code, and can greatly simplify many tasks in Python programming. However, it is important to use them appropriately, as using them excessively can lead to code that is difficult to understand and maintain.


In conclusion, lambda functions are a valuable tool in the Python programmer’s toolkit. They offer a way to write simple, anonymous functions that are concise and easy to read, making them ideal for functional programming constructs and other situations where a named function is not necessary.

However, excessive use can lead to code that is difficult to understand and maintain. With their concise syntax, flexible functionality, and improved readability, they offer a powerful way to simplify many tasks in our code and improve the overall quality of our projects.

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