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 vote149 views1 answer
Python inline web server
Python
1 vote154 views1 answer
Get all classes from css file using python
Python
15 votes127 views2 answers
Python make api request
Python
0 vote137 views1 answer
Install aws sdk ubuntu 20.04 command line
Python
11 votes132 views2 answers
Discord.py presence
Python
3 votes132 views2 answers
Map dataframe
Python
0 vote138 views1 answer
Default orange and blue matplotlib
Python
25 votes148 views4 answers
Pandas count the number of unique values in a column
Python
0 vote133 views1 answer
Eastvale roblox python
Python
3 votes153 views2 answers
Np argmin top n
Python