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

4 votes271 views2 answers
How to make a square shape in python
Python
6 votes266 views2 answers
Change tkinter window name
Python
48 votes268 views6 answers
Uppercase python
Python
1 vote288 views1 answer
How to remove microseconds from datetime in python
Python
0 vote262 views1 answer
Dataframe to ftp
Python
0 vote289 views1 answer
How to make a password generating program in python
Python
0 vote290 views1 answer
Binary table dataframe
Python
0 vote290 views1 answer
Specificity formula python
Python
0 vote290 views1 answer
Enumerate in range python
Python
0 vote289 views1 answer
Python how to count number of true
Python