# How to Calculate the Probability of Normal Distribution using SciPy

In this article, we will discuss the SciPy normal distribution. We will calculate the probability of normal distribution using SciPy.

## Normal Distribution

First, let’s understand the normal distribution.

The normal distribution is a continuous probability distribution for a random variable with actual values. It is calculated using the mean and standard deviation.

The probability density function, sometimes known as the PDF, computes the likelihood of a single point in a distribution. The PDF is calculated by using this formula:

Where `μ`

is the mean and `σ`

is the standard deviation of the distribution.

## Calculate the Probability of Normal Distribution Using SciPy

To compute the probability density function of a particular distribution, we will utilize one of the `scipy.stats.norm.pdf()`

methods.

The syntax of `scipy.stats.norm.pdf()`

is given below:

```
scipy.stats.norm.pdf(data, loc, scale)
```

Where `data`

is a collection of points or values representing equally sampled data in the form of array data, `loc`

is the location of sample data (mean), and `scale`

is a sample of standard deviation.

Let’s look at how to compute the pdf of a particular distribution using the methods below:

First, import the required libraries like this:

Then create an array to get random numbers. Here, we have created an array between -10 to 10 with a difference of 0.3 and stored it in an object `x_arr.`

Let’s get the mean and standard deviation values using the given random data.

Now computing the pdf value using the `norm`

method with `loc=mean`

and `scale=std.`

Here, `5`

is an array-like object whose probability is to be calculated.

Printing the calculated value of pdf:

Here is the complete code screenshot:

This way, we can use the mean and standard deviation values to calculate the probability of normal distribution using the SciPy library.

**Shiv Yadav**

Shiv is a self-driven and passionate Machine learning Learner who is innovative in application design, development, testing, and deployment and provides program requirements into sustainable advanced technical solutions through JavaScript, Python, and other programs for continuous improvement of AI technologies.

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