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# sympy.stats.Normal() in python

• Last Updated : 05 Jun, 2020

With the help of `sympy.stats.Normal()` method, we can get the continuous random variable which represents the normal distribution. Syntax : `sympy.stats.Normal(name, mean, std)`
Where, mean and std are real number.
Return : Return the continuous random variable.

Example #1 :
In this example we can see that by using `sympy.stats.Normal()` method, we are able to get the continuous random variable representing normal distribution by using this method.

 `# Import sympy and Normal``from` `sympy.stats ``import` `Normal, density``from` `sympy ``import` `Symbol, pprint`` ` `z ``=` `Symbol(``"z"``)``mean ``=` `Symbol(``"mean"``, positive ``=` `True``)``std ``=` `Symbol(``"std"``, positive ``=` `True``)`` ` `# Using sympy.stats.Normal() method``X ``=` `Normal(``"x"``, mean, std)``gfg ``=` `density(X)(z)`` ` `pprint(gfg)`

Output :

2
-(-mean + z)
————–
2
___ 2*std
\/ 2 *e
———————
____
2*\/ pi *std

Example #2 :

 `# Import sympy and Normal``from` `sympy.stats ``import` `Normal, density``from` `sympy ``import` `Symbol, pprint`` ` `z ``=` `2``mean ``=` `1.8``std ``=` `4`` ` `# Using sympy.stats.Normal() method``X ``=` `Normal(``"x"``, mean, std)``gfg ``=` `density(X)(z)`` ` `pprint(gfg)`

Output :

0.124843847615573*\/ 2
———————–
____
\/ pi

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