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Lists vs Arrays

While writing code for any purpose, you'll often encounter the need to store and manipulate collections of data. Two common ways to do this are by using lists and arrays. In this tutorial, we'll learn about the difference between lists and arrays. We'll also understand when to use a list and when to use an array.

Lists:

  • Dynamic Size: Lists in Python are dynamic, means we can change their size by adding or removing elements as needed.

  • Heterogeneous: Lists can hold elements of different data types, such as integers, strings, and objects, in the same list.

  • Operations: Lists offer a wide range of built-in operations and methods for manipulation.

  • Memory: Lists generally consume more memory as compared to array.

  • Iteration: Lists are easily iterable using loops or list comprehensions.

Example:

my_list = [1, 2, 3, "hello", 5.0]

 

Arrays:

  • Dynamic Size: In other languages Arrays have a fixed size determined at the time of creation. But in Python we use modules like - Array and Numpy to create Dynamic arrays.

  • Homogeneous: Arrays typically store elements of the same data type, which leads to efficient memory allocation.

  • Operations: Arrays may have fewer built-in methods for manipulation compared to lists.

  • Memory: Arrays generally consume less memory because they have a fixed size.

  • Iteration: Arrays can be iterated in the same way as lists.

  • Efficiency: Arrays are generally faster for element access and mathematical operations on elements.

Example:

import array
my_array = array.array('i', [1, 2, 3, 4, 5])

 

When to Use Lists:

  • Use lists when you need a dynamic data structure that can grow or shrink.

  • Lists are suitable when we want to store elements of different types in a single collection.

  • Lists are more versatile and convenient for most general-purpose programming tasks.

 

When to Use Arrays:

  • Use arrays when you need a fixed-size data structure with a specific data type.

  • Arrays are efficient for mathematical operations or when we need to work with large datasets.

  • When memory efficiency is critical, arrays are a better choice.