Programming Exercises : August 19, 2025

Bubble Sort

def bubbleSort(lst):
    for i in range(len(lst)):
        for j in range(i, len(lst)):
            if lst[i] > lst[j]:
                lst[i], lst[j] = lst[j], lst[i]

    return lst

lst = [5, 2, 9, 1, 5, 6]
sorted_lst = bubbleSort(lst)
print("Sorted List :", sorted_lst)

Numpy Basics : Arrays

# Importing Numpy And Making It Available As 'np'
# This Is A Good Practice To Follow And Maintain Consistency In Code
# Anything Can Be Put In Place Of 'np' But 'np' Is The Standard Alias For Numpy

import numpy as np

myList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print("Original List :", myList)
print("Type of myList :", type(myList))

# Converting List To Numpy Array

myArray = np.array(myList)
print("Converted Numpy Array :", myArray)
print("Type of myArray :", type(myArray))

# Parameters Of Numpy Array
print("Shape Of myArray :", myArray.shape)  # Shape Of The Array

print("Length Of myArray :", len(myArray))  # Length Of The Array
print("Data Type Of myArray :", myArray.dtype)  # Data Type Of The Array Elements

print("Size Of myArray :", myArray.size)  # Total Number Of Elements In The Array
print("N Dimensions Of myArray :", myArray.ndim)  # Number Of Dimensions Of The Array

print(
    "Item Size Of myArray :", myArray.itemsize
)  # Size In Bytes Of Each Element In The Array

print("Memory Layout Of myArray :", myArray.flags)  # Memory Layout Information

Numpy Operations And Matrices

import numpy as np

# Reshaping A Numpy Array
myArray = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
print("Original Array:", myArray)

reshapedArray = myArray.reshape(2, 5)
print("Reshaped Array (2 Rows, 5 Columns):", reshapedArray)

# Operations On Numpy Arrays
print("Sum of myArray:", np.sum(myArray))  # Sum Of All Elements
print("Mean of myArray:", np.mean(myArray))  # Mean Of All Elements
print(
    "Standard Deviation of myArray:", np.std(myArray)
)  # Standard Deviation Of All Elements

# Mathematical Operations
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])

print("Element-wise Addition:", array1 + array2)  # Element-wise Addition
print("Element-wise Subtraction:", array1 - array2)  # Element-wise Subtraction
print("Element-wise Multiplication:", array1 * array2)  # Element-wise Multiplication
print("Element-wise Division:", array1 / array2)  # Element-wise Division


print("Dot Product of array1 and array2:", np.dot(array1, array2))  # Dot Product
print("Cross Product of array1 and array2:", np.cross(array1, array2))  # Cross Product

# Coloumn Wise - Row Wise Operations
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print("Original Matrix:\n", matrix)

print("Sum Of Each Column:", np.sum(matrix, axis=0))  # Sum Of Each Column
print("Sum Of Each Row:", np.sum(matrix, axis=1))  # Sum Of Each Row

# Creating Special Matrices
identity_matrix = np.identity(3)  # Identity Matrix - 3 Rows, 3 Columns
print("Identity Matrix:\n", identity_matrix)

zero_matrix = np.zeros((2, 3))  # Zero Matrix - 2 Rows, 3 Columns
print("Zero Matrix:\n", zero_matrix)