 Introduction to Python Normal Distribution

Python Normal Distribution – Before moving ahead, let’s know a bit of Python Visualize Distributions with Seaborn

It is called the Gaussian Distribution and known one of the most important distributions.

It is knowing best to work with probability distribution such as IQ Scores, Heartbeat etc.

To get a Normal Data Distribution, use the random.normal() method.

It contains three parameters.

`loc – It specifies the mean`
`scale - It specifies the Standard Deviation`
`size – It specifies the shape of the array in terms of Row and Column.`

Apart from above all, there is an additional parameter –

`hist – It is Histogram (full form) that put a group of numbers together between the range.`

Example – Creating a random normal distribution array of size 3×2.

```from numpy import random

x = random.normal(size=(3, 2))

print(x)```

As a result, it returned shape of 3×2 array of random float numbers.

Example – Creating a random normal distribution of size 3×2 with mean at 1 and standard deviation of 2.

```from numpy import random

x = random.normal(loc=8, scale=12, size=(3, 2))

print(x)```
```Output -

[[24.58910238  7.91364785]
[-5.28282301 10.04603531]
[20.49968082  7.87460784]]```

As a result, it returned shape of 3×2 array of random float numbers.

Visualization of Normal Distribution

Example – Generating an Array with different random numbers.

```from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.normal(size=5000))

plt.show()```

As can be seen, it returned a graph plotted with different points on different location.

Example – Generating an Array with different random numbers.

```from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.normal(size=5000), hist=False)

plt.show()```

As shown above, it returned a graph plotted with different points on different location.

Note: Normal Distribution is also known as the Bell Curve because it is shaped like Bell curve.

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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