2.2 Numpy Lab .ipynb

Kernel: Python 3 (ipykernel)

• Create a NumPy array with elements ranging from 0 to 9. Then, reshape it into a 3x3 matrix.

• Create a 2D array of Shape 2*4 with defined elements in row1 - M1

• Create a 2D identify matrix of shape 3*3- M2

• Create a 2D ones matrix with Shape 2*4-M3

Compute: 1. M1+M3 2. M3M1 3. 4M2

• Given two NumPy arrays A and B, perform element-wise addition, subtraction, multiplication, and division.

• Extract the middle row of a given 2D NumPy array. Then, extract the elements at odd indices from the last column.

• Compute the mean, median, and standard deviation of a given NumPy array across rows and columns.

• Concatenate two 1D NumPy arrays vertically and horizontally, respectively.

• Given a 1D NumPy array of ages, find all ages greater than 30.

• Given a 2D NumPy array representing student scores and a 1D array representing score weights, compute weighted averages for each student.

• Generate a random 3x3 NumPy array of integers between 0 and 100.

import numpy as np

arr = np.arange(9).reshape(3, 3)
print(arr)

[[0 1 2]
 [3 4 5]
 [6 7 8]]

A = np.array([1, 2, 3])
B = np.array([4, 5, 6])

addition = A + B
subtraction = A - B
multiplication = A * B
division = A / B

print("Addition:", addition)
print("Subtraction:", subtraction)
print("Multiplication:", multiplication)
print("Division:", division)

Addition: [5 7 9]
Subtraction: [-3 -3 -3]
Multiplication: [ 4 10 18]
Division: [0.25 0.4  0.5 ]

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

middle_row = arr[1]
odd_indices_last_column = arr[::2, -1]

print("Middle row:", middle_row)
print("Odd indices of last column:", odd_indices_last_column)

Middle row: [4 5 6]
Odd indices of last column: [3 9]

arr = np.array([1, 2, 3, 4, 5])

mean = np.mean(arr)
median = np.median(arr)
std_dev = np.std(arr)

print("Mean:", mean)
print("Median:", median)
print("Standard Deviation:", std_dev)

Mean: 3.0
Median: 3.0
Standard Deviation: 1.4142135623730951

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

vertical_concat = np.vstack((arr1, arr2))
horizontal_concat = np.hstack((arr1, arr2))

print("Vertical Concatenation:")
print(vertical_concat)
print("\nHorizontal Concatenation:", horizontal_concat)

Vertical Concatenation:
[[1 2 3]
 [4 5 6]]

Horizontal Concatenation: [1 2 3 4 5 6]

ages = np.array([25, 35, 40, 20, 45])

over_30 = ages[ages > 30]

print("Ages over 30:", over_30)

Ages over 30: [35 40 45]

scores = np.array([[80, 90, 70], [85, 88, 92]])
weights = np.array([0.3, 0.4, 0.3])

weighted_avg = np.dot(scores, weights)

print("Weighted Averages for each student:", weighted_avg)

Weighted Averages for each student: [81.  88.3]

random_array = np.random.randint(0, 100, size=(3, 3))

print("Random Array:")
print(random_array)

Random Array:
[[95 10 19]
 [35 91 48]
 [62 42  5]]