Mastering 2D List Comprehension in Python: A Comprehensive Guide

Introduction

Python is a powerful and flexible programming language that is widely used across industries. The language is popular because of its simple syntax, ease of use, and wide range of libraries. One of the essential features of Python is a list comprehension. In this article, we will focus on 2D list comprehension, its syntax, and how to master it.

What is 2D List Comprehension?

A list is one of the fundamental data types in Python. List comprehension provides an easy and concise way to create lists. It is a way of creating a new list by performing operations on existing lists. A 2D list is simply a list of lists. 2D list comprehension allows us to create a new 2D list by iterating over an existing 2D list.

Syntax of 2D List Comprehension

The syntax of 2D list comprehension is similar to that of a one-dimensional list comprehension, but with an additional level of iteration. The general syntax is as follows:

new_list = [[expression for item2 in inner_list] for item1 in outer_list]

The above syntax creates a new 2D list with an expression that iterates over the inner and outer lists.

Example of 2D List Comprehension

Let’s consider an example of a 2D list. Suppose we have a 2D list that stores the scores of students in different subjects.

student_scores = [[80, 75, 85, 90], [70, 60, 50, 80], [90, 80, 70, 85]]

We can use 2D list comprehension to create a new list that stores the average score of each student. The example is as follows:

average_scores = [sum(student) / len(student) for student in student_scores]

The above expression creates a new list that stores the average score of each student. We first iterate over the outer list ‘student_scores’ and then calculate the sum and the length of each list.

Case Studies

Let us now understand how 2D list comprehension can be used for practical applications through case studies.

Case Study 1: Matrix Multiplication

Let us consider a case where we need to perform matrix multiplication of two matrices. We can perform the matrix multiplication using 2D list comprehension as follows:

matrix1 = [[2, 1, 4], [3, 1, 5], [2, 2, 1]]

matrix2 = [[3, 1, 2], [2, 1, 3], [1, 4, 2]]

result_matrix = [[sum(a * b for a, b in zip(rows, cols)) for cols in zip(*matrix2)] for rows in matrix1]

The above code first takes two matrices as input and creates a new matrix that stores the result of the matrix multiplication.

Case Study 2: Finding Prime Numbers

We can use 2D list comprehension to find prime numbers in a given range. The example is as follows:

num_list = [[i, j] for i in range(2, 8) for j in range(2, i) if i % j == 0]

The above expression creates a 2D list that stores all the prime numbers in the given range.

Conclusion

In this article, we discussed 2D list comprehension and its syntax. We also saw some examples and case studies of how 2D list comprehension can be used for practical applications. It is essential to master 2D list comprehension as it can help us to write code more efficiently and concisely. Hopefully, this guide has provided enough information to get started with 2D list comprehension in Python.

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