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