# random.gauss() function in Python

`random` module is used to generate random numbers in Python. Not actually random, rather this is used to generate pseudo-random numbers. That implies that these randomly generated numbers can be determined.

## random.gauss()

`gauss() ` is an inbuilt method of the `random` module. It is used to return a random floating point number with gaussian distribution.
Syntax : random.gauss(mu, sigma) Parameters : mu : mean sigma : standard deviation Returns : a random gaussian distribution floating number
Example 1:
 `# import the random module ``import` `random `` ` `# determining the values of the parameters ``mu ``=` `100``sigma ``=` `50`` ` `# using the gauss() method ``print``(random.gauss(mu, sigma)) `

Output :
`127.80261974806497`
Example 2: We can generate the number multiple times and plot a graph to observe the gaussian distribution.
 `# import the required libraries  ``import` `random  ``import` `matplotlib.pyplot as plt  ``   ` `# store the random numbers in a   ``# list  ``nums ``=` `[]  ``mu ``=` `100``sigma ``=` `50``   ` `for` `i ``in` `range``(``100``):  ``    ``temp ``=` `random.gauss(mu, sigma) ``    ``nums.append(temp)  ``       ` `# plotting a graph  ``plt.plot(nums)  ``plt.show() `

Output : Example 3: We can create a histogram to observe the density of the gaussian distribution.
 `# import the required libraries  ``import` `random  ``import` `matplotlib.pyplot as plt  ``   ` `# store the random numbers in a list  ``nums ``=` `[]  ``mu ``=` `100``sigma ``=` `50``   ` `for` `i ``in` `range``(``10000``):  ``    ``temp ``=` `random.gauss(mu, sigma)  ``    ``nums.append(temp)  ``       ` `# plotting a graph  ``plt.hist(nums, bins ``=` `200``)  ``plt.show() `

Output :

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