Week 7 Practice Notebooks - Duplicate

# import the library numpy as np # import the library matplotlib.pyplt as plt import numpy as np import matplotlib.pyplot as plt

# set a seed for your calculations so that they are reproducible np.random.seed(12345)

# generate 50 random integers from 1 to 50 using random.randint() and make a # frequency histogram x= np.random.randint(1,50,50) print (x) plt.hist(x)

# generate 10000 random integers from 1 to 50 using random.randint() and make a # frequency histogram # compare with previous histogram x= np.random.randint(1,50,1000) plt.hist(x)

# generate 1000 floating point numbers uniformly distributed from 1 to 100 and make a # frequency histogram x = np.random.uniform(1000) y = 1 + 99*x print (y) plt.hist((x,y))

# generate 1000 floating point numbers normally distributed about a mean of 50 # with a standard deviation of 5 and make a frequency histogram x= np.random.normal(1000) x = 50 + (5)*x plt.hist(x)

# generate 1000 floating point numbers normally distributed about a mean of 50 # with a standard deviation of 5 and make a density histogram; compare with frequency # histogram x= np.random.normal(1000) x = 50 + (5)*x plt.hist(x, density = True)

# import random and set seed import random random.seed(12345)

# Simulate drawing a single ball; each ball has a number from from 1 to 50 ball = random.randint(1,50) print (ball)

# Simulate drawing 100 balls and keep track of the number of balls # that have a number less than 25 ball = random.randint(1,50) n = 100 count = 0 for i in range (0,n) : tracking = ball = random.randint(1,50) if ( i < 25): count = count + 1 print (count)

# Calculate discrete probability that you will draw a ball with a number <25 using 10,000 # simulations ball = random.randint(1,50) n = 1000 sum_count = 0 for i in range (0,n) : tracking = ball = random.randint(1,50) if ( i < 25): sum_count = count + 1 print (sum_count) sum_count = [0 for i in range (0,10000)] dis_prob = [(num)/n for num in sum_count] print (dis_prob)