Python Arrays: Everything You Need to Know
Published: 2023-05-29 11:30:39



Python has grown significantly during the past few years and gained a lot of popularity among programming languages today. It is extensively utilized in a wide range of commercial fields, including programming, web development, machine learning, and data science. It's not unexpected that Python has eclipsed Java as the most popular programming language given its broad usage. You will discover one of the most crucial Python data structures in this article: Python arrays. 
What are Arrays in Python?
Arrays are a fundamental data structure that is used extensively throughout the majority of programming languages. They are multi-item storage containers in the Python programming language. They are a collection of ordered items whose values are all of the same data type. The fact that Python arrays can only carry a sequence of multiple objects of the same type is the most crucial thing to keep in mind when using them.
What Distinguishes Python Lists From Python Arrays?
One of the most prevalent data structures in Python and a fundamental building block of the language are lists. Arrays and lists both function similarly. Lists are an ordered collection of elements, just like arrays. They can also change in size over the course of the program because they are changeable and not fixed in size. They are particularly flexible to work with because items may be added and removed. Lists and arrays, however, are not the same thing.
Items from lists can be of different data kinds. As a result, a list can have multiple instances of any Python data type, including strings, floating point numbers, integers, and more. With arrays, that is not the case.
As was indicated in the section above, arrays only store objects that belong to the same data type. Integers, floating point numbers, and other desired Python data types can all be found in arrays.
When to Use Python Arrays
Python does not support arrays, although it does support lists. Because they are not a default data structure, arrays must be imported using the array module before they can be used.
When working with homogeneous data, arrays of the array module are helpful because they are lightweight wrappers over C arrays. In comparison to lists, they are also more space- and memory-efficient due to their smaller size.
By importing the NumPy library, you can utilize NumPy arrays to execute mathematical operations. In addition, lists behave similarly to Python arrays but are more versatile, so you should only use them when absolutely necessary. Joining Python training in Kochi will help you to learn and utilize Python and its array much more easily. 
Why Use an Array in Python?
Multiple values can be kept in an array at once. It allows you to hold several values in a single variable and aids in code optimization Arrays speed up work. The type codes that can be executed with the various data types when defining Python arrays are listed in the table below:
TYPECODE
C TYPE
PYTHON TYPE
SIZE
'b'
signed char
int
1
'B'
unsigned char
int
1
'u'
wchar_t
Unicode character
2
'h'
signed short
int
2
'H'
unsigned short
int
2
'i'
signed int
int
2
'I'
unsigned int
int
2
'l'
signed long
int
4
'L'
unsigned long
int
4
'q'
signed long long
int
8
'Q'
unsigned long long
int
8
'f'
float
float
4
'd'
double
float
8

Creating an Array in Python
Importing an array module into a Python program produces an array.
Syntax: from array import *
arrayName = array(typecode, [ Initializers ])
Example:
Typecodes are alphabetical symbols used to specify the kind of value the array will store. Typical type codes include:


Typecode
Value
b
Represents signed integer of size 1 byte
B
Represents unsigned integer of size 1 byte
c
Represents character of size 1 byte
i
Represents signed integer of size 2 bytes
I
Represents unsigned integer of size 2 bytes
f
Represents floating-point of size 4 bytes
d
Represents floating-point of size 8 bytes

Accessing an Array's Elements
You must enter the index number to access an array entry. Indexing begins at zero, not one. As a result, the index number is always one less than the array's length.
Example:

Basic Array Operations
Insertion Operation
The insert() function can be used to insert data into an array. The element is inserted using this function at the appropriate index.
Syntax: insert(index, value)
Example:

Deletion Operation
With the use of pop() or remove(), array elements can be deleted.
Example:

You specify the index of the element you want to remove when using the pop() method.
Example:

Array Concatenation
The + symbol can be used to merge two arrays.
Example:

Looping Through an Array
Using a loop, we can retrieve every element in an array.
Example:

Reversing the Elements
The reverse() method can be used to reverse the order of the items.
Example:

Count() Method
To count the occurrences of a specific element, use the count() method.
Example:

Python Arrays vs. Lists
We can specify the data type that will be stored in an array in Python, which is the main distinction between lists and arrays. There are no such limitations on lists.
**Conclusion**
Any programming language needs arrays as a fundamental data structure. 
Python employs arrays to quickly and efficiently store groups of related data. All aspiring data scientists and analysts should become proficient with Python. If you want to include this highly sought-after skill on your resume, enroll in [Python course in Kochi](https://www.irohub.com/python-training-kochi) today!