practice.ipynb

Kernel: Python 3 (system-wide)

Practice & Refresh

1. Write a Python program to compute the distance between the points (x1, y1) and (x2, y2).

import math
p1 = [4, 0]
p2 = [6, 6]
distance = math.sqrt( ((p1[0]-p2[0])**2)+((p1[1]-p2[1])**2) )

print(distance)

6.324555320336759

1. Write a Python program to calculate the hypotenuse of a right angled triangle.

import math
p1 = [4, 0]
p2 = [6, 6]
distance = math.sqrt( ((p1[0]-p2[0])**2)+((p1[1]-p2[1])**2) )

print(distance)

1. Write a Python program to plot on the same figure y1=sin(x) and y2=0.5, and configure the range of y-axis to (-2,2)

import numpy as np
import matplotlib.pyplot as plt

x=np.linspace(-2*np.pi, 2*np.pi,500)
y1 = np.sin(x)

plt.plot(x,y1)


a = np.empty(500)
a.fill(0.5)
plt.plot(x,a)

[<matplotlib.lines.Line2D at 0x7f97293c2eb0>]

1. Write a Python program to create an array of 10 zeros,10 ones, 10 fives.

x1 =np.zeros(10)
print(x1)

x2 = np.ones(10)
print(x2)

x3 = np.empty(10)
x3.fill(5)
print(x3)

[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]

1. Write a Python program to create a 3x3 identity matrix, i.e. diagonal elements are 1, the rest are 0.

x = np.eye(3)
print(x)

[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]

1. Write a Python program to plot Gaussian distribution with mean=0 and standard deviation = 1.

import scipy.stats as stats
import math


mu = 0
sigma = 1
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
plt.plot(x, stats.norm.pdf(x, mu, sigma))
plt.show()