Python Lambda Tutorial – Complete Guide

Welcome Python enthusiasts! If you are in the process of learning or mastering Python, you are likely to have come across a helpful Python feature called lambda, also known as anonymous functions. They are small, quickly-defined functions that are simple yet incredibly useful. In this article, we’re going to unravel how they work and why you should learn to use them – we’ll ensure it’s accessible, engaging and worthwhile for all levels of coders.

What is Python Lambda?

Python lambda is a small anonymous function. It’s anonymous because it doesn’t have a name like regular Python functions defined using the def keyword. It’s expressed with the keyword lambda, hence its name.

What is Python Lambda for?

Python lambda becomes handy when you need to declare a simple one-time-use function that you don’t want to give a name; think of it as a throw-away function. These functions are often used in conjunction with map(), reduce(), and filter() functions, or any functionality that has a short lifespan in your code.

Why Should I Learn Python Lambda?

Python lambda functions can help you write cleaner, more efficient code, particularly for small tasks. It’s a key component of functional programming in Python and is often used in areas such as data analysis, GUI programming and more. Plus, the more tools you can master in Python, the stronger your coding skills become. Let’s dive in and make you lambda ready!

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How to Use Python Lambda

Creating a lambda function in Python is straightforward. The structure of a lambda function is: lambda arguments: expression.

Here’s an example of a simple lambda function:

double = lambda x: x * 2
print(double(5))  # Output: 10

In the example above, the lambda function multiplies its argument by 2.

Lambda functions really shine when used in conjunction with Python’s built-in functions, like map(), reduce(), and filter(). Let’s explore how we can use lambda functions with these built-ins.

Using Lambda with Python’s map() function

The map() function applies a specified function to each item in an iterable. Let’s use lambda with map():

my_list = [1, 2, 3, 4]
squared = list(map(lambda x: x**2, my_list))
print(squared)  # Output: [1, 4, 9, 16]

As you can see, the lambda function is being used to square each item in the list, and the map() function applies this lambda function to each item.

Using Lambda with Python’s filter() function

The filter() function constructs an iterator from elements of an iterable for which a function returns true. Let’s explore this with an example:

my_list = [1, 2, 3, 4]
evens = list(filter(lambda x: x%2 == 0, my_list))
print(evens)  # Output: [2, 4]

In this example, we use the lambda function to filter only even numbers.

Using Lambda with Python’s reduce() function

reduce() is a function that applies a function of two arguments cumulatively on a list of objects in succession from left to right to reduce the list to a single output. Here we take an example:

from functools import reduce

my_list = [1, 2, 3, 4]
product = reduce((lambda x, y: x * y), my_list)
print(product)  # Output: 24

Here, the lambda function takes two arguments and multiplies them. The reduce() function applies this lambda to the list cumulatively, yielding the product of all elements in the list.

More Complex Lambda Functions

Although lambda functions are often used for simple, short tasks, they are capable of handling more complexity. For example, they can take multiple arguments:

add = lambda x, y: x + y
print(add(5, 3))  # Output: 8

In this example, the lambda function takes two arguments and returns their sum.

Conditional Statements with Lambda Functions

You can include conditional statements within lambda functions. Let’s look at an example where we want to differentiate between odd and even numbers:

check_num = lambda x: "even" if x % 2 == 0 else "odd"
print(check_num(7))  # Output: odd

Remember, while lambda functions can handle more complexity, keeping them as simple as possible results in more understandable and maintainable code.

Nested Lambda Functions

Yes, lambda functions can also be nested inside other lambda functions. Let’s see an example:

nested_lambda = lambda x: (lambda y: x + y)
add = nested_lambda(2)
print(add(3))  # Output: 5

In this example, a lambda function is nested inside another lambda function. The result is a function that takes one parameter, adds it to the parameter of the outer function, and returns the result.

Accessing Lambda Functions from a Dictionary

As lambda functions can be treated like any other object in Python, we can store them in data structures like dictionaries. Let’s check out an example:

my_dict = {
  'add': lambda x, y: x + y,
  'subtract': lambda x, y: x - y,

print(my_dict['add'](7, 3))  # Output: 10
print(my_dict['subtract'](7, 3))  # Output: 4

In this example, we created a dictionary that stores two lambda functions. Each function can be accessed using the respective dictionary key.

We hope these examples have helped illustrate the versatility and utility of Python’s lambda functions. Remember, practice is essential in mastering these, as with any coding concept. Happy coding!

Where to Go Next with Python?

Now that you’ve been introduced to Python’s lambda functions and understand how to use them, you may be wondering what to dive into next. At Zenva, we invite you to continue honing your Python skills with our vast collection of Python courses, which are perfect for both beginners looking to strengthen their foundational knowledge and intermediate learners wishing to delve into more complex topics.

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Adding Python’s lambda functions to your coding toolkit is a step towards cleaner, concise, and efficient code. Now that you know what lambda functions are, how to use them, and why they’re useful, you’re ready to take your Python skills to a new level. With continued learning and practice, we are confident that you can become a Pythonista!

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