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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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