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

1 vote241 views1 answer
Show documentation or information about a function/ method in jupyter notebook
Python
0 vote256 views1 answer
Python datetime move forward one day
Python
12 votes280 views5 answers
Python async await
Python
-1 vote241 views1 answer
Armstrong number function
Python
16 votes243 views1 answer
Python bitwise operators methods
Python
0 vote242 views1 answer
Pip unknown command import
Python
0 vote267 views1 answer
Rebinding a list stored in a Flask Session
Python
1 vote232 views1 answer
Mysql Unread result found
Python
5 votes231 views1 answer
Python pyautogui how to change the screenshot location
Python
105 votes290 views6 answers
Python json to dict
Python