Are Lambda Functions Faster Python

Python Programming

Are lambda functions faster in Python? This is a question that has been debated among Python developers for quite some time. As a Python enthusiast, I have always been curious about the performance differences between lambda functions and regular named functions. In this article, I will share my insights and findings on this topic.

The Basics of Lambda Functions

First, let’s start with a brief introduction to lambda functions. Lambda functions, also known as anonymous functions, are small, one-line functions that do not have a name. They are typically used in situations where a small function is needed for a specific task, and it’s not worth defining a separate named function.

Here’s an example of a lambda function:

square = lambda x: x**2

The above lambda function calculates the square of a number. It takes a parameter “x” and returns the square of that number. When we call the lambda function with the argument 5, it returns 25.

Performance Comparison

Now, let’s dive into the performance aspect of lambda functions. To compare the performance of lambda functions against regular named functions, I conducted a series of tests using the timeit module in Python. I created two functions, one using a lambda function and the other using a regular named function, and measured the execution time for each.

Here’s the code for the lambda function:

import timeit

lambda_func = lambda x: x**2

def time_lambda_func():
for i in range(1000):

print(timeit.timeit(time_lambda_func, number=1000))

And here’s the code for the regular named function:

def named_func(x):
return x**2

def time_named_func():
for i in range(1000):

print(timeit.timeit(time_named_func, number=1000))

After running the tests multiple times, I found that the execution time for both the lambda function and the named function were almost identical. This indicates that there is no significant performance difference between the two.

Considerations and Use Cases

While lambda functions may not have a noticeable performance advantage over regular named functions, they can still be useful in certain situations. One of the main advantages of lambda functions is their conciseness. They allow you to define a function inline, without the need for a separate function definition.

Lambda functions are often used in functional programming paradigms, where functions are treated as first-class citizens. They can be passed as arguments to other functions or used in higher-order functions such as map, filter, and reduce.

However, it’s important to note that lambda functions can quickly become unreadable if they become too complex or lengthy. In such cases, it’s recommended to use a regular named function for better code readability.


In conclusion, the performance of lambda functions in Python is comparable to regular named functions. While they may not offer a significant speed advantage, lambda functions can still be valuable in certain scenarios, especially in functional programming paradigms. They provide a concise way to define small, one-line functions without the need for a separate function declaration.

So, next time you find yourself in a situation where a small function is needed for a specific task, consider using a lambda function. Just remember to keep them simple and readable for the sake of maintainability.