# How to generate normally distributed data with NumPy using Python

• 1 minGenerating data can be useful in many ways. It is often used when you want to

- Do a
**Monte-Carlo****simulation***(E.g. to test out whether a model is working or not)* - Create a
**statistical model**that utilizes random noise. (E.g. random walk) - Create
**synthetic data**. (E.g. based on an**existing distribution**) - etc...

We call those processes **"Data Generative Processes" **or **DGA.**

When data is generated according to what we observe in the real world.

Enough talking, let's code

## The formula

## The code

Let's generate data using NumPy's normally distributed data generator numpy.random.normal().

To this function, we have to pass three arguments.

- The mean of our distribution: mu
- A standard deviation: std
- How many random numbers do we want: n

```
import numpy as np
# We set our three arguments
mu = 0
std = 1
n = 50
# We generate a list of n randomly distributed observation
randomly_generated_data = np.random.normal(mu, std, n)
```

Here you are! You now know how to generate normally distributed data with NumPy using Python.

# More on statistics

If you liked what you read and want to know more about how to apply **Statistics in Python** and avoid a few headaches... check out the other articles I wrote by clicking just here: