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 vote332 views1 answer
Ascending discending
Python
0 vote315 views1 answer
Global /tmp/pip-req-build-civioau0/opencv/modules/videoio/src/cap_v4l.cpp (587) autosetup_capture_mode_v4l2 VIDEOIO(V4L2:/dev/video0): device is busy
Python
1 vote310 views1 answer
Stdout.write python
Python
2 votes339 views2 answers
Python split string capital letters
Python
8 votes328 views1 answer
Convert column string to int pandas
Python
0 vote318 views2 answers
How to increment a variable in python]
Python
1 vote301 views1 answer
Return json with flask
Python
0 vote325 views1 answer
Renpy hide textbox
Python
0 vote330 views1 answer
Dfs and bfs inn python
Python
0 vote361 views1 answer
Python remove articles from string regex
Python