import numpy as np
import matplotlib.pyplot as plt

# DataQuest example code


# The norm module has a pdf function (pdf stands for probability density function)
from scipy.stats import norm

# The arange function generates a numpy vector
# The vector below will start at -1, and go up to, but not including 1
# It will proceed in "steps" of .01.  So the first element will be -1, the second -.99, the third -.98, all the way up to .99.
points = np.arange(-1, 1, 0.01)

# The norm.pdf function will take the points vector and convert it into a probability vector
# Each element in the vector will correspond to the normal distribution (earlier elements and later element smaller, peak in the center)
# The distribution will be centered on 0, and will have a standard devation of .3
probabilities = norm.pdf(points, 0, 0.3)

# Plot the points values on the x-axis and the corresponding probabilities on the y-axis
# See the bell curve?
plt.plot(points, probabilities)
plt.show()


# My Code
points = np.arange(-10,10,0.1)
probabilities = norm.pdf(points, 0, 2)
plt.plot(points, probabilities)
plt.show()