Python Lambda Functions

Last Updated : 28 Mar, 2026

Lambda functions are small anonymous functions, meaning they do not have a defined name. These are small, short-lived functions used to pass simple logic to another function.

  • Contain only one expression.
  • Result of that expression is returned automatically (no return keyword needed).

In this example, a lambda function is defined to convert a string to its upper case using upper().

Python 
a = 'GeeksforGeeks'
upper = lambda x: x.upper()  
print(upper(a))

Output
GEEKSFORGEEKS

Explanation:

  • 'a' store the string 'GeeksforGeeks'.
  • upper is a lambda function that takes an argument x and returns x.upper().
  • upper(a) applies the lambda to a, converting it to uppercase.

Syntax

In Python, lambda functions are created using the lambda keyword. Below is the syntax:

lambda
Python Lambda Expression
  • Function name (a): Stores the lambda function so it can be reused later.
  • Lambda keyword (lambda): Defines an anonymous (inline) function in Python.
  • Argument (x): The input value passed to the lambda function.
  • Expression (x**2): The operation performed on the argument and returned as the result.

Use Cases of Lambda Functions

1. Using with Condition Checking

A lambda function can use conditional expressions (if-else) to return different results based on a condition.

Python 
check = lambda x: "Positive" if x > 0 else "Negative" if x < 0 else "Zero"
print(check(5))   
print(check(-3))  
print(check(0))

Output
Positive
Negative
Zero

Explanation:

  • The lambda function takes x as input.
  • It uses nested if-else statements to return "Positive," "Negative," or "Zero."

Below example checks divisibility by 2 and returns "Even" or "Odd" accordingly.

Python 
check = lambda x: "Even" if x % 2 == 0 else "Odd"
print(check(4))  
print(check(7))

Output
Even
Odd

Explanation:

  • The lambda checks if a number is divisible by 2 (x % 2 == 0).
  • Returns "Even" for true and "Odd" otherwise.

2. Using with List Comprehension

Lambda functions can be combined with list comprehensions to apply the same operation to multiple values in a compact way.

Python 
func = [lambda arg=x: arg * 10 for x in range(1, 5)]
for i in func:
    print(i())

Output
10
20
30
40

Explanation:

  • lambda function multiplies each element by 10.
  • list comprehension iterates through li and applies the lambda to each element.

3. Using for Returning Multiple Results

Although a lambda can contain only one expression, it can still return multiple results by combining them into a tuple.

Python 
calc = lambda x, y: (x + y, x * y)
res = calc(3, 4)
print(res)

Output
(7, 12)

Explanation: lambda function performs both addition and multiplication and returns a tuple with both results.

4. Using with filter()

filter() function uses a lambda expression to select elements from a list that satisfy a given condition, such as keeping only even numbers.

Python 
c = [1, 2, 3, 4, 5, 6]
even = filter(lambda x: x % 2 == 0, c)
print(list(even))

Output
[2, 4, 6]

Explanation:

  • The lambda function checks if a number is even (x % 2 == 0).
  • filter() applies this condition to each element in nums.

5. Using with map()

map() function applies a lambda expression to each element of a list and returns a new list with the transformed values.

Python 
a = [1, 2, 3, 4]
double = map(lambda x: x * 2, a)
print(list(double))

Output
[2, 4, 6, 8]

Explanation:

  • The lambda function doubles each number.
  • map() iterates through a and applies the transformation.

6. Using with reduce()

reduce() function repeatedly applies a lambda expression to elements of a list to combine them into a single result.

Python 
from functools import reduce
a = [1, 2, 3, 4]
mul = reduce(lambda x, y: x * y, a)
print(mul)

Output
24

Explanation:

  • The lambda multiplies two numbers at a time.
  • reduce() applies this operation across the list.

To know the difference between def keyword and Lambda function, refer to this article, def vs lambda.

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