Python Tutorial : Introduction to Seaborn

Key Takeaways

hello welcome to this introductory course on Seabourn my name is Erin case and I'll be your instructor so what is Seaborn Seaborn is a powerful Python library for creating data visualizations it was developed in order to make it easy to create the most common types of plots the plot shown here can be created with just a few lines of Seabourn code this is a picture of a typical data analysis workflow data visualization is often a huge component of both the data exploration phase and the communication of results so Seabourn will be very useful there there are several tools that can be used for data visualization but Seaboard offers several advantages first Seaborn's main purpose is to make data visualization easy it was built to automatically handle a lot of complexity behind the scenes second Seabourn works extremely well with pandas data structures pandas is a Python library that is widely used for data analysis finally it's built on top of matplotlib which is another Python visualization library matplotlib is extremely flexible Seabourn allows you to take advantage of this flexibility when you need it while avoiding the complexity that Matt plot lifts flexibility can introduce to get started we'll need to import the Seabourn library the line import Seabourn as SNS will import Seabourn as the conventionally used alias SMS why SNS the Seabourn library was apparently named after a character named samuel norman Seaborn from the television show The West Wing thus the standard alias is the characters initials SNS we also need to import matplotlib which is the library that seaboard is built on top of we do this by typing import matplotlib PI plot as P LT p LT is the alias that most people use to refer to matplotlib so we'll do that here as well let's now dive into an example to illustrate how easily you can create visualizations using Seaborn here we have data for ten people consisting of lists of their heights and inches and their weights and pounds do taller people tend to weigh more you can visualize this using a type of plot known as a scatter plot which you'll learn more about later in the course use SNS dot scatter plot to call the scatter plot function from the Seabourn library then specify what to put on the x-axis and y-axis finally call the plot show function from matplotlib to show the scatter plot this plot shows us that taller people tend to have a higher weight how many of our observations of heights and weights came from males versus females you can use another type of plot the count plot to investigate this cow plots take in a categorical list and return bars that represent the number of less entries per category use the count plot function and provide the list of every person's gender this count plot shows that out of the ten observations we had in our height and weight scatterplot 6 were male and 4 or female now those were a couple of simple examples throughout this course you'll learn to make more complex visualizations such as those pictured here more importantly you'll learn when to use each type of visualization in order to most effectively extract and communicate insights using data I'm excited to dive into Seabourn with you throughout this course for now let's practice what you