Python | List Comprehension

Python | List Comprehension

Introduction: List comprehension is a powerful and simple technique in Python for creating lists in a more readable way. It provides a short syntax to generate lists by applying an expression to each item in an iterable. In this article, we’ll explore the concept of list comprehension in Python, explore its syntax, features, and demonstrate its usage through practical examples.

Understanding List Comprehension: List comprehension is a short and elegant way to create lists in Python. It consists of an expression followed by a for clause, which iterates over an iterable (such as a list, tuple, or range), and optionally one or more if clauses for filtering elements.

Syntax of List Comprehension: The basic syntax of list comprehension is as follows:

new_list = [expression for item in iterable if condition]

Here, expression is the operation to perform on each item, item is the variable representing each element in the iterable, and condition is an optional filtering condition.

Example 1: Basic List Comprehension:
Let’s start with a simple example to illustrate the usage of list comprehension:

numbers = [10, 20, 30, 40, 50]
squared_numbers = [x**2 for x in numbers]


[100, 400, 900, 1600, 2500]

In this example, list comprehension is used to square each element of the numbers list, resulting in a new list of squared numbers.

Example 2: List Comprehension with Conditional Filtering:
List comprehension can also include conditional filtering to include or exclude elements based on certain conditions. Here’s an example:

numbers = [11, 12, 13, 14, 15]
even_numbers = [x for x in numbers if x % 2 == 0]


[12, 14]

In this example, only the even numbers from the numbers list are included in the even_numbers list using a conditional filtering clause.

Example 3: Nested List Comprehension:
List comprehension can be nested to create lists of lists or perform more complex operations. Here’s an example of creating a matrix using nested list comprehension:

matrix = [[i * j for j in range(1, 4)] for i in range(1, 4)]


[[1, 2, 3], [2, 4, 6], [3, 6, 9]]

In this example, a nested list comprehension is used to generate a 3×3 matrix where each element is the product of its row and column indices. For every ith value the j for loop executed completely.

Example 4: Using List Comprehension with Strings:
List comprehension is not limited to numeric operations; it can also be used with strings. Here’s an example of converting a list of strings to uppercase:

words = ["apple", "banana", "cherry"]
uppercase_words = [word.upper() for word in words]



In this example, each word in the words list is converted to uppercase using list comprehension.

Conclusion: List comprehension is good tool in Python for creating lists with minimal code and maximum readability. Whether you’re manipulating numeric data, filtering elements, or working with strings, list comprehension offers a concise and elegant solution for generating lists in Python.

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