How to Calculate the Probability of Normal Distribution using SciPy

1. Normal Distribution
2. 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.