How to plot a normal distribution with matplotlib in python ?

Example of python code to plot a normal distribution with matplotlib:

import matplotlib.pyplot as plt
import scipy.stats
import numpy as np


x_min = 0.0
x_max = 16.0

mean = 8.0 
std = 2.0

x = np.linspace(x_min, x_max, 100)

y = scipy.stats.norm.pdf(x,mean,std)

plt.plot(x,y, color='coral')

plt.grid()

plt.xlim(x_min,x_max)
plt.ylim(0,0.25)

plt.title('How to plot a normal distribution in python with matplotlib',fontsize=10)

plt.xlabel('x')
plt.ylabel('Normal Distribution')

plt.savefig("normal_distribution.png")
plt.show()

Another example:

import matplotlib.pyplot as plt
import scipy.stats
import numpy as np


x_min = 0.0
x_max = 16.0

mean = 8.0 
std = 3.0

x = np.linspace(x_min, x_max, 100)

y = scipy.stats.norm.pdf(x,mean,std)

plt.plot(x,y, color='black')

#----------------------------------------------------------------------------------------#
# fill area 1

pt1 = mean + std
plt.plot([pt1 ,pt1 ],[0.0,scipy.stats.norm.pdf(pt1 ,mean, std)], color='black')

pt2 = mean - std
plt.plot([pt2 ,pt2 ],[0.0,scipy.stats.norm.pdf(pt2 ,mean, std)], color='black')

ptx = np.linspace(pt1, pt2, 10)
pty = scipy.stats.norm.pdf(ptx,mean,std)

plt.fill_between(ptx, pty, color='#0b559f', alpha='1.0')

#----------------------------------------------------------------------------------------#
# fill area 2

pt1 = mean + std
plt.plot([pt1 ,pt1 ],[0.0,scipy.stats.norm.pdf(pt1 ,mean, std)], color='black')

pt2 = mean + 2.0 * std
plt.plot([pt2 ,pt2 ],[0.0,scipy.stats.norm.pdf(pt2 ,mean, std)], color='black')

ptx = np.linspace(pt1, pt2, 10)
pty = scipy.stats.norm.pdf(ptx,mean,std)

plt.fill_between(ptx, pty, color='#2b7bba', alpha='1.0')

#----------------------------------------------------------------------------------------#
# fill area 3

pt1 = mean - std
plt.plot([pt1 ,pt1 ],[0.0,scipy.stats.norm.pdf(pt1 ,mean, std)], color='black')

pt2 = mean - 2.0 * std
plt.plot([pt2 ,pt2 ],[0.0,scipy.stats.norm.pdf(pt2 ,mean, std)], color='black')

ptx = np.linspace(pt1, pt2, 10)
pty = scipy.stats.norm.pdf(ptx,mean,std)

plt.fill_between(ptx, pty, color='#2b7bba', alpha='1.0')

#----------------------------------------------------------------------------------------#
# fill area 4

pt1 = mean + 2.0 * std
plt.plot([pt1 ,pt1 ],[0.0,scipy.stats.norm.pdf(pt1 ,mean, std)], color='black')

pt2 = mean + 3.0 * std
plt.plot([pt2 ,pt2 ],[0.0,scipy.stats.norm.pdf(pt2 ,mean, std)], color='black')

ptx = np.linspace(pt1, pt2, 10)
pty = scipy.stats.norm.pdf(ptx,mean,std)

plt.fill_between(ptx, pty, color='#539ecd', alpha='1.0')

#----------------------------------------------------------------------------------------#
# fill area 5

pt1 = mean - 2.0 * std
plt.plot([pt1 ,pt1 ],[0.0,scipy.stats.norm.pdf(pt1 ,mean, std)], color='black')

pt2 = mean - 3.0 * std
plt.plot([pt2 ,pt2 ],[0.0,scipy.stats.norm.pdf(pt2 ,mean, std)], color='black')

ptx = np.linspace(pt1, pt2, 10)
pty = scipy.stats.norm.pdf(ptx,mean,std)

plt.fill_between(ptx, pty, color='#539ecd', alpha='1.0')

#----------------------------------------------------------------------------------------#
# fill area 6

pt1 = mean + 3.0 * std
plt.plot([pt1 ,pt1 ],[0.0,scipy.stats.norm.pdf(pt1 ,mean, std)], color='black')

pt2 = mean + 10.0 *std
plt.plot([pt2 ,pt2 ],[0.0,scipy.stats.norm.pdf(pt2 ,mean, std)], color='black')

ptx = np.linspace(pt1, pt2, 10)
pty = scipy.stats.norm.pdf(ptx,mean,std)

plt.fill_between(ptx, pty, color='#89bedc', alpha='1.0')

#----------------------------------------------------------------------------------------#
# fill area 7

pt1 = mean - 3.0 * std
plt.plot([pt1 ,pt1 ],[0.0,scipy.stats.norm.pdf(pt1 ,mean, std)], color='black')

pt2 = mean - 10.0 * std
plt.plot([pt2 ,pt2 ],[0.0,scipy.stats.norm.pdf(pt2 ,mean, std)], color='black')

ptx = np.linspace(pt1, pt2, 10)
pty = scipy.stats.norm.pdf(ptx,mean,std)

plt.fill_between(ptx, pty, color='#89bedc', alpha='1.0')

#----------------------------------------------------------------------------------------#

plt.grid()

plt.xlim(x_min,x_max)
plt.ylim(0,0.25)

plt.title('How to plot a normal distribution in python with matplotlib',fontsize=10)

plt.xlabel('x')
plt.ylabel('Normal Distribution')

plt.savefig("normal_distribution_2.png")
plt.show()

References