# sympy.stats.LogNormal() in python

With the help of `sympy.stats.LogNormal()` method, we can get the continuous random variable which represents the Log-Normal distribution.

Syntax : `sympy.stats.LogNormal(name, mean, std)`
Where, mean and standard deviation are real number.
Return : Return the continuous random variable.

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

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

Output :

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

Example #2 :

 `# Import sympy and LogNormal ` `from` `sympy.stats ``import` `LogNormal, density ` `from` `sympy ``import` `Symbol, pprint ` ` `  `z ``=` `2.1` `mean ``=` `7.6` `std ``=` `4` ` `  `# Using sympy.stats.LogNormal() method ` `X ``=` `LogNormal(``"x"``, mean, std) ` `gfg ``=` `density(X)(z) ` ` `  `pprint(gfg) `

Output :

0.0136890249307238*\/ 2
————————
____
\/ pi

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