Python Arrays

In Python, arrays are a data structure that can hold multiple values at once. Unlike lists, arrays are specialized for numerical data and can only store elements of the same data type. Arrays are useful when you need to work with a collection of items, especially when numerical operations are involved. Python provides the array module to work with arrays.
Python arrays are a great way to store and manipulate homogeneous data efficiently. While lists are more versatile, arrays can be faster for numerical operations due to their fixed data type. Understanding arrays will help you optimize your Python programs when dealing with large amounts of numerical data.

Definition and Characteristics

• Homogeneous: All elements in an array must be of the same data type.
• Indexed: Arrays are indexed, starting from zero.
• Mutable: Elements in an array can be modified after creation.
• Fixed Size: The size of an array is defined when it's created, unlike Python lists which are dynamic.

Creating an Array

To create an array in Python, you need to import the array module:

# Importing the array module
import array as arr
# Creating an array of integers
numbers = arr.array('i',[1, 2, 3, 4, 5]) #'i' indicates array of integers
print(numbers)
                

Accessing Elements in an Array

You can access elements in an array using their index:

# Accessing array elements
import array as arr
numbers = arr.array('i',[1, 2, 3, 4, 5])
print("First element:", numbers[0])
print("Last element:", numbers[-1])
                

Modifying Elements in an Array

Arrays are mutable, so you can change the value of an element:

# Modifying an element
import array as arr
numbers = arr.array('i',[1, 2, 3, 4, 5])
numbers[2] = 10
print(numbers)
                

Common Array Methods

• append(x): Adds an element x to the end of the array.
• extend(iterable): Adds elements from an iterable to the end of the array.
• insert(i, x): Inserts an element x at position i.
• remove(x): Removes the first occurrence of x.
• pop([i]): Removes and returns the element at position i.

Example: Using Array Methods

# Using array methods
import array as arr
numbers = arr.array('i',[1, 2, 10, 4, 5])
numbers.append(6)
numbers.insert(1, 15)
numbers.remove(10)
print(numbers)
                

Looping Through an Array

You can loop through an array using a for loop:

# Looping through an array
import array as arr
numbers = arr.array('i',[1, 15, 2, 4, 5, 6])
for num in numbers:
    print(num, end=' ')
                

• Advantages:
  • More memory-efficient for numerical data compared to lists.
  • Enforces type uniformity.
• Disadvantages:
  • Limited to basic operations.

Using Numpy Arrays

For more advanced numerical computations, the numpy library offers the ndarray type, which is highly optimized for performance.

• Advantages:
  • Highly efficient for large-scale numerical operations.
  • Supports advanced operations like matrix multiplication, broadcasting, etc.
• Disadvantages:
  • Requires installing NumPy.
  • Slightly more complex to use than lists.

Let's see how the array is created by using numpy library.

import numpy as np
np_array = np.array([1, 2, 3, 4, 5])
print(np_array)  

Matrix Operations Using Numpy

import numpy as np
matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])
print("Matrix 1:")
print(matrix1) 
print("Matrix 2:")
print(matrix2)

import numpy as np
matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])
result = matrix1 + matrix2
print("Addition:")
print(result) 

import numpy as np    
matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])
result = matrix1 - matrix2
print("Subtraction:")
print(result) 

import numpy as np
matrix1 = np.array([[1, 2], [3, 4]])
transpose = matrix1.T
print("Transpose of Matrix 1:")
print(transpose)

import numpy as np
matrix1 = np.array([[1, 2], [3, 4]])
inverse = np.linalg.inv(matrix1)
print("Inverse of Matrix 1:")
print(inverse)