## GENERATE RANDOM NUMBERS: NUMPY BASIC EXERCISE 18

Find a solution in Numpy that generate random numbers based on standard normal distribution

In this exercise, we are going to generate random number up-to 20 based on standard normal distribution.

Note: normal(loc=0.0, scale=1.0, size=None). Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently.

### Code

```import numpy as np
rand = np.random.normal(0,20,20)
print("Generating 20 random values")
print(rand)```

### Output

```Generating 20 random values
[-41.04710687 -13.95470119 -15.01538703 -33.57800168  12.98236712
-16.12801452   6.9631557   11.87425154  25.69415256  30.71314733
7.43508363  31.18996274  18.33545056   9.82372055 -12.38501579
-21.00535824 -44.48524337  -1.5283168   10.12943489 -25.7899703 ]```

### Code Editor

``` ``` ``` import numpy as np rand = np.random.normal(0,20,20) print("Generating 20 random values") print(rand) ``` ``` ```

References:
StatTrek: Statistics Dictionary