Python list comprehension is a powerful and concise way to create new lists by iterating over an existing collection and applying a condition or transformation to each element. In this Python list comprehension tutorial, we will discuss how to use this technique to write more efficient and readable code. If you are new to Python, we recommend checking out our Python Web Scraping Tips.
List comprehensions are not only more elegant but also faster than traditional for loops in many cases. They allow you to write complex operations in just one line of code. By the end of this tutorial, you’ll have a solid understanding of how list comprehensions work and how to use them effectively in your Python programs.
Basic Syntax of List Comprehension
The basic syntax of a list comprehension in Python is as follows:
[expression for item in iterable if condition]
This can be read as: “Create a new list by applying an
expression to each
item in the given
iterable if the
condition is True.” The
condition part is optional, and you can use it to filter the items based on specific criteria. Let’s dive into some examples to better understand how list comprehensions work in Python.
Examples of Python List Comprehension
Let’s start with a simple example. Suppose we have a list of numbers, and we want to create a new list containing the squares of these numbers. Using a traditional for loop, we would write the following code:
numbers = [1, 2, 3, 4, 5] squares =  for number in numbers: squares.append(number ** 2) print(squares) # Output: [1, 4, 9, 16, 25]
Now, let’s achieve the same result using list comprehension:
numbers = [1, 2, 3, 4, 5] squares = [number ** 2 for number in numbers] print(squares) # Output: [1, 4, 9, 16, 25]
As you can see, the list comprehension version is much shorter and more readable. You can find more examples and use cases in our Python List Comprehension Examples post.
Using Conditions in List Comprehension
One of the most powerful features of list comprehension is the ability to include conditions. For example, let’s say we want to create a list of even numbers from an existing list of integers. We can use a conditional statement to achieve this:
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9] even_numbers = [number for number in numbers if number % 2 == 0] print(even_numbers) # Output: [2, 4, 6, 8]
To learn more about using conditions in Python, check out our Python Conditional Statements tutorial.
Nested List Comprehensions
List comprehensions can also be nested to handle more complex operations. For example, let’s say we have a matrix (list of lists) and we want to flatten it into a single list. We can use nested list comprehensions to achieve this:
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] flattened = [number for row in matrix for number in row] print(flattened) # Output: [1, 2, 3, 4, 5, 6, 7, 8, 9]
For more advanced examples and use cases, check out our Nested List Comprehension in Python tutorial.
In this Python list comprehension tutorial, we explored the power and elegance of list comprehensions in Python. We discussed their basic syntax, how to use conditions, and even how to handle nested comprehensions. List comprehensions are an excellent tool for writing efficient, concise, and readable code, so it’s essential to master this technique as a Python developer.
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