Arrays vs. Lists in Python

Key takeaways:

  • Lists are built-in Python data structures that are flexible, capable of holding elements of various data types, and easy to use.

  • Arrays, typically from the numpy library, require all elements to be of the same data type but are optimized for performance, especially in mathematical operations.

  • Arrays outperform lists regarding memory usage and speed when dealing with large datasets or numerical computations.

In Python, both lists and arrays serve as data structures for storing collections of items, but they are optimized for different use cases. Lists are versatile, easy to use, and support elements of various types, making them ideal for general-purpose programming. Arrays, on the other hand, offer memory efficiency and faster execution times for numerical operations but require all elements to be the same type.

When working with Python, it’s crucial to understand the difference between two important data structures:

While both store data in indexed formats and allow for iteration, they serve different purposes and come with distinct features. Python offers a built-in data structure called a list that provides flexibility, allowing the storage of mixed data types. However, when performance and efficiency are key, Python also supports arrays, which are more memory-efficient but require uniform data types. Arrays are typically imported from external libraries like numpy.

What is a list in Python?

A list in Python is a built-in data type used to store multiple items in a single variable.

Python list
Python list

Lists can contain elements of different (heterogeneous) data types, such as strings, integers, and booleans.

# Creating a list with mixed data types
my_list = ["Jon", 318, 'C', False]
print(my_list)

Characteristics of lists:

  • Heterogeneous: Lists can store elements of different data types.

  • Dynamic: Lists can grow or shrink in size as elements are added or removed.

  • Flexible: Lists are built into Python’s syntax, so no additional imports are needed.

What is an array in Python?

An array in Python is more restricted than a list, as all elements must be of the same data type (homogeneous).

Python array
Python array

Arrays are often used for mathematical computations and performance-critical applications. Arrays are not built-in and need to be imported from libraries like numpy.

from numpy import array
my_array = array([11, 3, 5, 2, 4])
print(my_array)

Characteristics of arrays:

  • Homogeneous: All elements in an array must be of the same data type.

  • Efficient: Arrays are optimized for arithmetic operations and use less memory than lists.

  • External dependency: Arrays are not built-in; you must import them from libraries like numpy.

Differences between list and array in Python

While lists and arrays may look similar at first glance, they differ in several ways, particularly regarding flexibility, memory efficiency, and use cases.

# List example
my_list = [6, 8, 14, 2]
for i in range(len(my_list)):
    my_list[i] = my_list[i] / 2
print("List:", my_list)
# Array example
from numpy import array
my_array = array([6, 8, 14, 2])
print("Array:", my_array / 2)

In a list example, we initialize my_list with integer values [6, 8, 14, 2]. This list is then iterated over using a for loop that ranges from 0 to the length of the list (4 in this case). During each iteration, the current element of the list is divided by 2 and updated in place. As a result, the list is modified to contain the halved values of its original elements. Finally, the modified list is printed.

In an array example, we first import the array function from the NumPy library. We then create a NumPy array my_array initialized with the same integer values [6, 8, 14, 2]. Unlike lists, NumPy arrays support vectorized operations, allowing us to perform element-wise calculations more efficiently. Here, my_array / 2 divides each array element by 2 using NumPy’s internal optimizations. The result is printed.

Let’s see the key differences:

Lists

Arrays

Data type

Can store different data types (heterogeneous)

Only supports a single data type (homogeneous)

Built-in

Yes, no need to import external libraries

No, requires importing from libraries like NumPy

Memory usage

Takes more memory due to flexibility

More memory-efficient as it stores elements of the same type

Mathematical operations

Cannot directly perform vectorized arithmetic operations

Optimized for arithmetic operations

Resizing

Dynamic: Can grow and shrink in size

Static size: Resizing requires copying to a new array

Operations differences in lists and arrays

Lists and arrays differ in their operational capabilities. Arrays are highly optimized for mathematical computations, while lists offer more flexibility but lack performance in certain scenarios.

# Working with a list
my_list = [10, 20, 30]
# You need a loop for element-wise operations
for i in range(len(my_list)):
    my_list[i] += 5
print("Updated List:", my_list)
# Working with an array
from numpy import array
my_array = array([10, 20, 30])
# Direct arithmetic operation on the entire array
print("Updated Array:", my_array + 5)

In the first example, we initialize my_list with the values [10, 20, 30]. To add 5 to each list element, we use a for loop that iterates over the range of the list’s length. During each iteration, the loop updates the current element by adding 5. This operation is performed in place, meaning each list element is individually modified. After the loop completes, the updated list is printed, resulting in [15, 25, 35]. This approach shows how lists require explicit loops for element-wise operations, making them less efficient for bulk arithmetic operations.

In the second example, we use NumPy’s array function to create an array my_array with the same values [10, 20, 30]. NumPy arrays support vectorized operations, which means we can perform arithmetic operations on the entire array directly without an explicit loop. By executing my_array + 5, each array element is incremented by 5 in a single, efficient operation. The result [15, 25, 35] shows how NumPy arrays facilitate concise and performant element-wise operations compared to traditional lists.

Is a Python list just an array?

No, a Python list is not the same as an array. Lists are more versatile because they can hold mixed data types and are part of the Python core. Arrays, conversely, are specialized data structures for numerical data and are not natively included in Python’s core libraries.

What is the advantage of using an array over the list?

Arrays offer better performance and memory efficiency than lists, especially when handling large datasets or performing numerical computations. For example, arrays are significantly faster in operations involving vectorization (applying operations element-wise without loops). Also, arrays have serious applications in machine learning’s computational-intensive algorithms.

from numpy import array
# Create an array
my_array = array([1, 2, 3, 4, 5])
# Perform element-wise operation without using a loop
new_array = my_array * 10
print(new_array)

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Frequently asked questions

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Why use arrays over lists in Python?

Arrays are preferred over lists in Python when performance and efficiency are crucial, especially for numerical operations. Arrays, particularly those from the numpy library, are optimized for fast mathematical computations and use less memory than lists. They also support vectorized operations, which allow you to perform calculations on entire arrays without explicit loops, leading to more concise and faster code.

What is the difference between an array and a list of lists?

  • Array: An array, such as one created with the numpy library, is a single-dimensional or multi-dimensional data structure with a uniform data type. It is designed for efficient numerical computations and supports operations on its entire content.

  • List of Lists: A list of lists is a nested Python list where each element is itself a list. It can hold elements of different data types and does not provide the same performance benefits as arrays. Operations on a list of lists are generally slower and less memory-efficient compared to arrays. 

# List of lists
list_of_lists = [[1, 2, 3], [4, 5, 6]]

# NumPy array
import numpy as np
array = np.array([[1, 2, 3], [4, 5, 6]])

Which is faster, a NumPy array or a list?

NumPy arrays are significantly faster than Python lists for numerical operations. This is because NumPy arrays are implemented in C and optimized for performance. They support vectorized operations, which allow you to perform calculations on the entire array at once, rather than using loops. Python lists, on the other hand, are slower for such operations because they are implemented in pure Python and are more flexible but less efficient for numerical tasks.

Are arrays mutable in Python?

Yes, arrays in Python are mutable, meaning you can change their contents after creation. This applies to both lists and NumPy arrays. For example, you can modify individual elements of a NumPy array or a Python list directly. 

# NumPy array
import numpy as np
array = np.array([1, 2, 3])
array[0] = 10
print(array)  # Output: [10  2  3]

# Python list
py_list = [1, 2, 3]
py_list[0] = 10
print(py_list)  # Output: [10, 2, 3]

For more on mutability in Python, look at our Answer What are mutable and immutable objects in Python3?

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