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.
Table of contents
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!
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: 4In 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.
About Our Python Mini-Degree
For categorical learning, we highly recommend our Python Mini-Degree program. This is an extensive education plan designed to take you from beginner to professional by teaching you various aspects of Python programming. Notably, Python is a highly sought-after language in the job market, especially in data science and web development. So, learning it can unlock countless career opportunities.
The Python Mini-Degree offered by us comes packed with an array of hands-on courses that cover everything from coding basics, algorithms, object-oriented programming to game and app development. The idea behind these courses is to let you learn Python through doing. You get to create games, develop algorithms and build real-world apps.
We ensure that our Python courses are always updated to align with the industry trends. Upon the completion of these courses, you will not only have a deeper understanding of Python but also a robust portfolio which will be essential in your professional pursuits.
Python for the Intermediate Learners
If you’ve already mastered the basics and are raring to step up your Python game, do explore our wide range of intermediate-level Python courses available here.
At Zenva, we are determined to support you in your learning journey. Whether you are a beginner or further along, our diverse courses cater to all levels, and will help you master coding, game creation and a lot more. Here’s to your learning journey with us at Zenva!
Conclusion
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!
Ready to explore more? Jump into the world of Python programming with us at Zenva! From bite-sized lessons to comprehensive Mini-Degrees, we have something tailored for everyone’s learning style. Embark on your journey with Zenva’s Python Mini-degree today and become a true Python expert.
Did you come across any errors in this tutorial? Please let us know by completing this form and we’ll look into it!
FINAL DAYS: Unlock coding courses in Unity, Godot, Unreal, Python and more.
