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sympy.stats.PowerFunction() in Python
• Last Updated : 08 Jun, 2020

With the help of `sympy.stats.PowerFunction()` method, we can get the continuous random variable which represents the Power Function distribution.

Syntax : `sympy.stats.PowerFunction(name, alpha, a, b)`
Where, a, b and alpha are real number.

Return : Return the continuous random variable.

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

 `# Import sympy and PowerFunction ` `from` `sympy.stats ``import` `PowerFunction, density ` `from` `sympy ``import` `Symbol, pprint ` ` `  `z ``=` `Symbol(``"z"``) ` `alpha ``=` `Symbol(``"alpha"``, positive ``=` `True``) ` `a ``=` `Symbol(``"a"``, positive ``=` `True``) ` `b ``=` `Symbol(``"b"``, positive ``=` `True``) ` ` `  `# Using sympy.stats.PowerFunction() method ` `X ``=` `PowerFunction(``"x"``, alpha, a, b) ` `gfg ``=` `density(X)(z) ` ` `  `print``(gfg) `

Output :

(-2*a + 2*z)/(-a + b)**2

Example #2 :

 `# Import sympy and PowerFunction ` `from` `sympy.stats ``import` `PowerFunction, density, variance ` `from` `sympy ``import` `Symbol, pprint ` ` `  `z ``=` `Symbol(``"z"``) ` `alpha ``=` `2` `a ``=` `0` `b ``=` `1` ` `  `# Using sympy.stats.PowerFunction() method ` `X ``=` `PowerFunction(``"x"``, alpha, a, b) ` `gfg ``=` `density(X)(z) ` ` `  `pprint(variance(gfg)) `

Output :

1/18

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