Python Program on NumPy Array Creation

In this article, we will explore a Python program that demonstrates various methods of the array module. The array module provides an efficient way to store and manipulate arrays in Python. The program covers essential methods like pop(), tolist(), append(), index(), remove(), count(), reverse(), and insert(). Understanding these methods is crucial for effective array manipulation in Python.

Prerequisites

• Basic Python Knowledge (Variables, Data Types, Syntax)
• Familiarity with the NumPy Library

Topic Explanation

In this program, an array of integers is created using the array module as a foundation for demonstrating its diverse functionalities. The demonstration covers methods like pop(), which removes the array’s last element, and tolist(), converting the array into a list. It also includes append(), which adds new elements to the array, enhancing its utility.

The exploration extends to methods like index(), which identifies the position of an element, and remove(), which deletes an element’s first appearance in the array. For quantifying the frequency of elements, the count() method is employed. Additionally, the program showcases reverse(), for inverting the array’s order, and insert(), allowing for the placement of elements at specific locations, thus broadening the array’s adaptability.

Code:

# Importing the NumPy library with an alias 'np' for ease of use
import numpy as np

# Constructing an integer array 'myar' with a customized set of values
myar = np.array([5, 15, 25, 35, 10, 20, 30, 40], int)

#Outputting the mean value of all elements in the array
print("Average:", myar.mean())

# Generating an array 'myar' with 4 values linearly spaced between 1 and 4
myar = np.linspace(1, 4, 4)

# Creating a sequence 'myar' with values from 5 to 15 in ascending order
myar = np.arange(5, 16)

# Outputting the created ascending sequence
print("Ascending sequence:", myar)

# Forming an array 'myar' of 5 elements, all set to 2 and of float type
myar = np.full(5, 2.0)

# Displaying the array with all elements set to 2
print("Array of twos:", myar)

# Constructing an array 'myar' of 5 elements, all set to 3 and of float type
myar = np.full(5, 3.0)

# Showing the array with all elements set to 3
print("Array of threes:", myar)