**What is it?**

This is a continuous probability distribution for a real-valued random variable.

**Key characteristics:**

- The **mean value** or expectation is equal to 0.
- The **standard deviation** to 1.
- The shape is bell-curved.
- The distribution is symmetrical.
**Python realization:**

We will generate standard normal distribution with the size 1000 and mean and standard deviation specific to the standard normal distribution. We use the function `random.normal()` from the `numpy` library with the parameters: `loc` is the mean value and `scale` is the standard deviation.

You can play with the distribution size and see how the distribution will be modified. 

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Generate standard normal distribution with the size 1000
data = np.random.normal(loc = 0, scale = 1, size = 1000)

sns.histplot(data = data, kde = True)
plt.show()

Standard Normal Distribution (Gaussian distribution) 1/2

Standard Normal Distribution (Gaussian distribution) 1/2