Data Science & Machine Learning - 7.2 Seaborn Distribution Plots

Hi friends,

Welcome to this post on visualizing Distribution Plots under Data Science & Machine Learning. In the previous post, we discussed about the installation process of Seaborn library for Data Visualization. In this post, we'll learn about distribution plots supported by Seaborn that allows us to visualize the distribution of a dataset.

Note: All the commands discussed below are run in the Jupyter Notebook environment. See this post on Jupyter Notebook to know about it in detail. 

Seaborn Distribution Plots

So, let's first import the useful libraries. 

I have imported NumPy library as well to generate random data to fill our plots with. Now, let's start with creating various plots using Seaborn.

1. distplot : It is used to visualize the distribution of a univariate (one variable) data points. In the example below, first we generated a random NumPy array and then plot a distribution plot of the same using the Seaborn's distplot() method:





What it returns is basically a histogram of data points and the dark blue line is called the KDE (Kernel Density Estimation). We can remove the KDE from our plot by setting the kde parameter to false.



The y-axis represents the count of the data points in the range represented by x-axis. We can also change the bins parameter to get a detailed/abstract view of the data points. Generally, larger the bins value more detailed the distribution plot and smaller the bins value less detailed the distribution plot.