The two most significant data structures in Python are arrays and lists. In Python, data are stored in lists, arrays, and lists. We can iterate, slice, and index using these data structures. However, there are a few differences between them. We will discover the Array and List difference in this blog.
Python includes a wide range of data structures, including lists, tuples, sets, and dictionaries, which offer a wide range of features and functionalities. In Python, lists are the most efficient and user-friendly data structure.
However, Python doesn’t come with any built-in support for the array. To utilize the array module or the NumPy package in the Python application, we must first import the array module. And that is the main Array and List difference. Let’s take a quick introduction to both data structures before getting into the details of this subject.
Python comes with a built-in linear data structure called a list. Data is stored in a sequential order using it. Several operations, including indexing, iterating, and slicing, can be applied to a list. The list has the following characteristics.
- A square bracket surrounds the list of components, and commas are used to divide each one (,).
- Because it is changeable, we can change the list elements after they have been created.
- Since the lists are sorted, the elements are kept in a particular sequence. To gain access to the list element, we can utilise indexing.
Different data kinds of things can be stored. Strings, numbers, and objects can all be included in the same list.
Python List Examples
Below are the examples of a list.
Example – 1
list = [31, 60, 19, 12]
[31, 60, 19, 12]
Example – 2
# creating a list containing elements # belonging to different data types list1 = [1,"Yash",['a','e']] print(list1)
[1, 'Yash', ['a', 'e']]
The first element in the list above is a number, followed by a string, and then a list of characters.
Array in Python
A linear data structure that stores the data is an array. It is also encased in square brackets, ordered, and modifiable. It has storage space for common things. However, there are limitations on how the various data type values may be stored.
Either an array module or a Numpy must be imported into Python in order to interact with an array.
import array as arr or import numpy as np
The allocation of elements in contiguous memory locations makes it simple to change, add, remove, or access them. Additionally, we must provide the data type. Let’s analyze the next illustration.
Example – 1
Import array as arr array_1 = arr.array("i", [31, 60,19, 12]) print(array_1) print(type(array_1))
array('i', [31, 60, 19, 12])
Example – 2: Using Numpy array
import numpy as np array_sample = np.array(["str", 'Ankit', 'Gourav', ‘Mayank', 'Deepti']) print (array_sample) print(type(array_sample))
['numbers' 'Ankit', 'Gourav', ‘Mayank', 'Deepti'] < class 'numpy.ndarray' >
The string type has been defined, and the string value has been saved.
Array vs List
Now, we have a brief introduction and features. Here, we will discuss the Array and List difference.
|1.||The list is capable of storing values of many categories.||It is limited to values of the same type.|
|2.||The direct arithmetic operations are not supported by the list.||It is able to do mathematical operations directly.|
|3.||Before working with the array, we must import it.||We don’t need to import the lists because it is a built-in data structure.|
|4.||When storing data, lists are less compatible than arrays.||Arrays and lists work considerably better together.|
|5.||It uses a lot of memory.||It is a smaller list in terms of memory size.|
|6.||It is appropriate for storing the data item’s extended sequence.||It is appropriate for storing shorter data sequences.|
|7.||With the use of explicit looping, we can print the complete list.||Without explicitly looping, we are still able to display the complete list.|
|8.||It has the ability to nest several kinds of components.||It must either have all nested components that are the same size.|
- While an array must be imported from the array or NumPy package, a list is a built-in data structure.
- Both lists and arrays may hold ordered objects and are changeable.
- Arrays can only hold elements of the same type, whereas lists may store elements of multiple types.
- Compared to arrays, lists are more flexible since they don’t require explicit looping to print their elements.
- While we can perform arithmetic operations directly on arrays, we are unable to do so on lists.
With this, we will come to an end of this Array and List difference. We hope this article helps you to understand the python concept where we discuss the Array and List difference.