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.
Learn data science course from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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’]]
upGrad’s Exclusive Data Science Webinar for you –
Explore our Popular Data Science Courses
- 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 = ‘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])
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.
|Preferred for storing a small amount of data.||
Preferred for storing a large amount of data
|Can not be used for mathematical operations directly.||
Array elements can be easily manipulated using advanced mathematical operations.
|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.
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 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.
Arrays are suitable for storing large amounts of data, as the memory consumption of arrays is more efficient than lists.
Read our popular Data Science Articles
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
A 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.
Top Data Science Skills to Learn
|SL. No||Top Data Science Skills to Learn|
|1||Data Analysis Programs||Inferential Statistics Programs|
|2||Hypothesis Testing Programs||Logistic Regression Programs|
|3||Linear Regression Programs||Linear Algebra for Analysis Programs|
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.