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Python Array vs. List: Differences & Use-Cases

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3rd Nov, 2021
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Python Array vs. List: Differences & Use-Cases

Python has gained the upper hand over other languages in Data Science over the past years, and its data structures play a vital role. Python has a large set of data structures, among which array and list are the most popular that we are going to discuss today. 

Arrays and Lists are two prominent data structures in Python that are similar in many aspects. They both can be used to store data and enable us to iterate over them, slice them, and even access their elements utilizing the indexing method. Then what is the difference between list and array in Python? Well, this is what we are going to discuss in this article. 

Arrays in Python

An array is a contiguous data structure that holds homogeneous elements, i.e., the elements that belong to the same data type. 

The following are the major characteristics exhibited by arrays in Python:

  • The contiguous nature of the array allows the data to be stored in adjacent memory locations. This makes it easier to perform operations on array elements.
  • An array in Python can be declared in two ways: 
    • Using the array module 

import array # importing the ‘array’ module

myArray = array.array(‘i’, [10, 20, 30]) # array declaration

# created array: [10, 20, 30]

Note: In the above declaration, it is necessary to specify the format code. Here, ‘i’ is a format code that stands for integer.

  • Using the NumPy module

import numpy # importing the ‘numpy’ module

myArray = numpy.array([10, 20, 30]) # array declaration

# created array: [10, 20, 30]

  • Array elements are ordered. Every element has an associated integer index. For example, in arr[10, 20, 30], ‘10’, ‘20’, and ‘30’ are stored at indices 0, 1, and 2 respectively in the memory.

Note: The array indexing in Python starts from 0. 

  • An array can only contain values of the same type i.e. homogeneous elements. For example,

arr[1, 2, 3]

arr[‘a’, ‘b’, ‘c’]

An array in Python is generally used to store a list of similar items. One real-life use-case of a display can be to store the stock prices of a particular stock for a range of days. The closing price of the stock remains intact for a specific stock and day. This means that storing such details in an immutable data structure such as an array makes much more sense.

In fact, NumPy arrays are generally used to store data from large datasets in data science and machine learning. Each NumPy array corresponds to a feature in a dataset.

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Lists in Python 

A list is one of the four in-built containers or data structures supported in Python. One of the major advantages of using lists is that a single list can hold values of multiple data types. 

The following are the major characteristics exhibited by lists in Python:

  • Lists are more flexible in terms of data storage. They can contain heterogeneous data i.e., elements of different types. For example, 

[1, ‘hello’, [‘x’, ‘y’]]

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  • Lists are also ordered, and the elements stored in a list can be accessed using their indices. Negative indices can be used to access an element from the end of the list. For example.,

 myList = [20, 40, ‘hello’, ‘world’]

# printing the second last element 



  • Lists can be easily mutated after the initialization of the list. To modify any value, access it using the index of the element.

cars = [‘Ford’, ‘Tesla’, ‘Jaguar’]

cars[2] = ‘BMW’

  • Multi-dimensional lists can also be implemented in Python using the concept of nested lists. These multi-dimensional lists can be used as multi-dimensional arrays in Python.

myArr = [[1, 2], [3, 4]] 

# created 2-d array:

# |1, 2|

# |3, 4|

A real-life use-case of a multi-dimensional heterogeneous list in Python can be to store a set of product details such as product type, category, cost price, selling price, etc. Each list in such a multi-dimensional list represents a product. Since the lists are mutable, it becomes easier to change the product details whenever we want. 

If you are an aspiring Python developer, covering the basic differences between array and list in Python is a key aspect. So, without further ado, let’s jump straight onto a tabular description on Python Array vs. List.

Know the Difference Between Array and List in Python 

Python Array vs List: Who’s the winner?




Lists need not be declared since they are inbuilt in Python.

list = [10, 20, 30]

You need to import an array module or NumPy library in order to declare an array.


my_arr_1 = array.array(‘i’, [10, 20, 30])

Data Type

A single list can contain values that belong to different data types.

myList = [40, ‘hi there’, ‘m’]

All the elements of an array should be of the same data type.

myArr = arr.array(i, [1, 0, 9])


Python list is resizeable as, during list initialization, Python initializes some extra elements.Arrays have a constant size that can not be altered.



Consumes larger space and memory for the addition or removal of elements.

Stores data in a more compact manner. 

Data Storage

Preferred for storing a small amount of data.

Preferred for storing a large amount of data

Mathematical Operations

Can not be used for mathematical operations directly.

Array elements can be easily manipulated using advanced mathematical operations. 

Display Data

Elements of a list can be displayed without loop

my_List = [1,“Dennis”,[‘a’,‘b’]]


A loop must be required for the elements of an array to be displayed.

import array  

my_Arr = array.array(‘i’, [1, 2, 3])  

for i in my_Arr:


Python Array vs List: Who’s the winner?

If you are reading this section, then it means you are now quite familiarized with the difference between list and array in Python. However, you should also be aware of when to use Array or List in your program. 

This section discusses the various circumstances where you have to choose the most suitable data structure among these two.

Type of elements 

If the type of data is not predetermined, there is a collection of data belonging to multiple types. For instance, to store the record of students having entities such as name(string),  ID(integer), and marks(float), a list is a preferred choice.

If the data to be stored belongs to the same data type, then an array or a list can be preferred here. The choice will then depend on other parameters such as the size of the data, operations to be performed, and usage.

Memory Consumption

Memory consumption in lists is more as some additional space is allocated during the initialization of the list. If the data collection is relatively smaller, then a list is an efficient choice here.

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Arrays are suitable for storing large amounts of data, as the memory consumption of arrays is more efficient than lists.

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Supported Operations

If your data does not require any arithmetic operations, then a list can be a better choice, as it supports better in-built functions for data manipulation.

On the other hand, arrays should be used when mathematical operations need to be performed. The NumPy module supports many advanced mathematical operations, including trigonometry and logarithmic operations.

Module to be imported 

list can be declared without importing any module or library. It can be defined just like a usual variable since it is an in-built data structure in Python. 

However, the array is not one of the default containers of Python. There are two most popular modules- array and NumPy. Each module comes with some predefined functions to manipulate and manage the data stored in the array.

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This article covered all the topics required to give you an insightful understanding of the difference between array and list in Python. We also discussed the various use cases where a list or an array is suitable.

If you want to start your journey in Data Science, then enroll in upGrad’s Data Science course today. Check out the extensive collection of courses on upGrad to kick start your career.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.

Frequently Asked Questions (FAQs)

1When to use an array over a list in Python?

Python array is preferred over a list in various scenarios:

1. Since the array in Python is more compact and consumes less memory than a list, it is preferred to use an array when a large amount of data needs to be stored.
2. It is unnecessary to use a list to store the data when all elements are of the same data type and hence an array will be more efficient here.
3. The data stored in an array can be easily manipulated mathematically whereas this is quite inconvenient with a list.

2Which is faster between array and list in Python?

An array is faster than a list in python since all the elements stored in an array are homogeneous i.e., they have the same data type whereas a list contains heterogeneous elements.

Moreover, Python arrays are implemented in C which makes it a lot faster than lists that are built-in in Python itself.

3When is a list more suitable for storing data in Python?

Python list has a great significance in data storage and can be used in multiple cases:

1. When you have various elements of different data types, you can store them in a list and can access these elements by simply referring to their indices.
2. A list can also be resized. Hence, a list is useful when you are not certain about the number of elements.
3. Lists are highly preferable when a small amount of data is required to be stored since the built-in functions of the list are quite convenient for data manipulation.

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