Learn Seaborn: A Brief Guide

In the previous tutorial, we learned why data visualization is important and how we can create plots using matplotlib. In this tutorial, we’ll learn about another data visualization library called Seaborn, which is built on top of matplotlib. But why do we need seaborn if we have matplotlib? Using seaborn you can make plots that are visually appealing and not just that seaborn is known for a range of plots that are not present in matplotlib that could be quite helpful in data analysis. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/0*DojhMEbTLTGmLx76.jpg" style="height:394px; width:700px" /> <h1>Installing Seaborn</h1> Unlike other libraries we’ve worked on until now seaborn doesn’t come pre-installed with anaconda but don’t worry we can install it using conda or pip. <pre> conda install -c anaconda seaborn #Install using conda</pre> <pre> pip install seaborn # Install using pip</pre> <h1>Importing Seaborn and Dataset</h1> Conventionally seaborn is imported as sns, why you ask? Well, I had the same question! I mean sns is not an acronym for seaborn no matter how you see it. Turns out it’s actually an insider joke. You can read more about it here. For this tutorial, we’ll be using Students Performance Dataset on Kaggle. Now that everything is cleared let’s start importing the dataset and libraries. <a href="https://naemazam.medium.com/learn-seaborn-a-brief-guide-87f5a1b5e75f">Website</a>