Python Lambda Function with Examples

A Python lambda operator or lambda function is a small anonymous function, an anonymous meaning function without a name. The Lambda functions are mainly used in combination with the functions filter(), map(), and reduce(). This function can take any number of arguments, but can only have one expression and they can be used wherever function objects are required in Python.

1. Syntax of Lambda Function

Following is the syntax of the lambda function.


# Syntax of lambda
lambda arguments: expression

Here,

lambda – is a python keyword that is used to create a lambda function.
arguments – arguments to the lambda function.
expression – expression to execute by using the arguments.

2. Python Lambda with Single Arguments

Python lambda function can take a single argument, executes the expression, and returns a result. In the below example, the lambda function is assigned to the variable square, you can call this function like any other function. The square function takes a single argument x, and returns the square of x.


# Lambda example with single argument
square = lambda x: x * x
print(square(4))

# Output:
# 16

3. Python Lambda with Two Arguments

Similarly, you can also create a lambda function with multiple arguments, let’s take another example with two arguments, add them and return the result.


# Lambda example with two arguments
add = lambda x, y : x + y
print(add(10, 20))  

#Output: 
#30

4. Using if-else Statement

You can also use an if-else statement in the lambda function. Here, is an example


# Lambda function using if-else
min = lambda a, b : a if(a < b) else b
print(min(10, 20))

# Output:
# 10

5. Lambda with map() Function

The map() function in Python is used to apply the transformation to an iterable object like a list, tuple, or set e.t.c, this applies the given transformation function to each item of an iterable (e.g., list, tuple, set). You can combine this with a lambda function. Here, is an example of using map() with lambda.

The map() is very useful when you want to apply a transformation to a large number of items without creating a new list, especially when you are working with big data.


# Lambda with map()
numbers = [2, 4, 5, 6, 3]
squared_result = list(map(lambda x: x**2, numbers))
print(squared_result) 

# Output: 
# [4, 16, 25, 36, 9]

In this example, the lambda function takes one argument x and returns its square. The map() function applies this lambda function to each item of the numbers list, and the result is a new iterator containing the squared values of each number. The list() function is used to convert the iterator to a list, so that we can print the results.

6. Lambda with Filter() Function

Let’s use the Python built-in function filter() with lambda to filter a list of values based on a given function.

In the below example, we are filtering out the even numbers from the list and converting the result into a list using the list() function. Here, the lambda function takes one argument x and returns True if x is even, False otherwise.


# Lambda with filter()
numbers = [10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers) 

# Output: 
# [10,12, 14, 16, 18, 20]

7. Lambda with Reduce Function

Finally, let’s use the lambda with reduce() function. The reduce() function applies a given function to the items of an iterable (e.g., list, tuple, set) in a cumulative way, and returns a single value. Here is an example of using lambda with reduce() function.


# Lambda with reduce()
from functools import reduce
numbers = [1, 2, 3, 4, 5]
product = reduce(lambda x, y: x+y, numbers)
print(product) 

# Output: 
# 15

This example adds all values in the list and returns a single value. In this example, the lambda function takes two arguments x and y and returns the sum of both. The reduce() function applies this lambda function cumulatively to the items of the numbers list, so first it will apply 1+2=3, then 3+3=6, then 6+4=10 and so on. The final result is a single value, the sum of all the numbers in the list.

8. Conclusion

In this article, you have learned what is lambda function and how to create an anonymous function and evaluate an expression. Also, we used the lambda function with python built-in function like map(), reduce(), and filter().