# sympy.stats.ChiNoncentral() in Python

With the help of `sympy.stats.ChiNoncentral()` method, we can get the continuous random variable which represents the non-central chi distribution.

Syntax : `sympy.stats.ChiNoncentral(name, k, l)`
Where, k and l is number of degree of freedom.
Return : Return the continuous random variable.

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

 `# Import sympy and ChiNoncentral ` `from` `sympy.stats ``import` `ChiNoncentral, density, E ` `from` `sympy ``import` `Symbol, simplify ` ` `  `k ``=` `Symbol(``"k"``, integer ``=` `True``) ` `l ``=` `Symbol(``"l"``, integer ``=` `True``) ` `z ``=` `Symbol(``"z"``) ` ` `  `# Using sympy.stats.ChiNoncentral() method ` `X ``=` `ChiNoncentral(``"x"``, k, l) ` `gfg ``=` `density(X)(z) ` ` `  `pprint(gfg) `

Output :

2 2
-k l z
— – — – —
k 2 2 2 /k \
l*z *(l*z) *e *besseli|- – 1, l*z|
\2 /

Example #2 :

 `# Import sympy and ChiNoncentral ` `from` `sympy.stats ``import` `ChiNoncentral, density, E ` `from` `sympy ``import` `Symbol, simplify ` ` `  `k ``=` `5` `l ``=` `6` `z ``=` `0.2` ` `  `# Using sympy.stats.ChiNoncentral() method ` `X ``=` `ChiNoncentral(``"x"``, k, l) ` `gfg ``=` `density(X)(z) ` ` `  `print``(gfg) `

Output :

1.81702770690497e-11*besseli(3/2, 1.2)

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