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# Types of Data Distribution In Python

Before we discuss the types of data distribution it is imperative to examine, What is data distribution?

Data distribution is a function that specifies all possible values for a variable and also quantifies the relative frequency (probability of how often they occur).

Distributions are considered any population that has a scattering of data.

How many types of distributions are there?

Based on the types of data we deal with day to day, we have two types of distribution functions.

1.Discrete Distributions: For discrete data, we have discrete distributions.

Discrete data distributions include binomial distributions, Poisson distributions, and geometric distributions.

2.Continuous Distributions: For continuous data, we have continuous distributions.

Continuous data distributions include normal distributions and the Student's t-distribution.

How do you show data distribution?/How do you check for data distribution in Python?

A histogram is the most commonly used plot type for visualizing distribution. It shows the frequency of values in data by grouping it into equal-sized intervals or classes (so-called bins). In such a way that it gives you an idea about the approximate probability distribution of your quantitative data.

Example Image of Histogram

1. Histogram Plot

A simple and commonly used plot to quickly check the distribution of a sample of data is the histogram. In the histogram, the data is divided into a pre-specified number of groups called bins. The data is then sorted into each bin and the count of the number of observations in each bin is retained.

2. Scatter plots

Scatter plots are best for showing distribution in large data.

Example Image of Scatter plots

“what is data distribution in machine learning?”

In the context of machine learning an easy explanation of data distribution is as below.

A distribution is simply a collection of data, or scores, on a variable. Usually these scores are arranged in order from smallest to largest and then they can be presented graphically.

What is the most common type of distribution?

The most commonly used distribution is the normal distribution, which is used frequently in finance, investing, science, and engineering.

Example Image of Normal Data Distribution

The normal distribution is fully characterized by its mean and standard deviation, meaning the distribution is not skewed and does exhibit kurtosis.

What is Kurtosis?

Kurtosis is a measure of the tailedness of a distribution.

Tailedness is how often outliers occur. Excess kurtosis is the tailedness of a distribution relative to a normal distribution.

Distributions with medium kurtosis (medium tails) are mesokurtic. Distributions with low kurtosis (thin tails) are platykurtic.

Example Image of Kurtosis

Kurtosis Formula

Why is distribution of data important?/What is the importance of distributions?

The importance of distributions

Sampling distributions are important for statistics because we need to collect the sample and estimate the parameters of the population distribution. Hence distribution is necessary to make inferences about the overall population.

What is the distribution of the dataset? The distribution of a data set is the shape of the graph when all possible values are plotted on a frequency graph (showing how often they occur).