sympy.stats.LogNormal() in python

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.

Python3 1=1

# 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 :

Python3 1=1

# 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

sympy.stats.LogNormal() in python

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