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

2 votes148 views1 answer
Calculate market value crsp pandas
Python
0 vote189 views1 answer
Dynamic footer in django
Python Django
0 vote146 views1 answer
@tf.function
Python
0 vote158 views1 answer
Python get all ips in a range
Python
3 votes216 views2 answers
List of dictionary values
Python
0 vote154 views1 answer
Mudopy
Python
22 votes149 views1 answer
Python isset key
Python
-1 vote150 views2 answers
Python selenium find element by text
Python
0 vote117 views1 answer
How to use tkinter in google colab
Python
0 vote164 views1 answer
Pandas join two series on index
Python