Normal distribution

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
...          linewidth=2, color='r')
>>> plt.show()

Source: numpy.org

Normal distribution

# Creating a normal distribution
import numpy as np 
mu, sigma = 0, 0.1 # mean and standard deviation
s = np.random.normal(mu, sigma, 1000)

# Visualizing the Normal Distribution
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s, 30, density=True)
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
         np.exp(- (bins - mu)**2 / (2 * sigma**2)), linewidth=2, color='r')
plt.show()

Source: numpy.org

Similar codes

0 vote326 views1 answer
How to loadh5 file in python
Python
0 vote346 views1 answer
Nearest neighbors imputation
Python
0 vote563 views1 answer
Python - How To Concatenate List of String
Python
1 vote325 views1 answer
Python program to print ASCII characters in lowercase
Python
0 vote350 views1 answer
Python3 change file permissions
Python
8 votes347 views3 answers
Python is integer string
Python
0 vote371 views1 answer
Django Signup view
Python Django
2 votes347 views2 answers
Django response headers
Python Django
2 votes310 views1 answer
Python download s3 image
Python
7 votes425 views3 answers
Np sum
Python