Python Tutorial: Introduction to Seaborn

Key Takeaways

welcome to this introduction of Seaborg my name is Chris Moffat and I will be your instructor for this course I have been using Python for over 10 years and I'm currently the creator of the popular blog practical business Python I am excited to show you how to effectively use the Seabourn library for creating insightful visualizations the Python visualization landscape is complex and it can be challenging to find the right tool for the right job before we discuss Seabourn in detail it is helpful to understand where it stands in this landscape this illustration is from Jake Vander plazas PyCon 2017 presentation on the visualization landscape in Python and highlights the complex ecosystem the key point is that matplotlib is a foundational library used by many visualization tools including Seabourn matplotlib is a robust library that can support building many types of visualization Seabourn uses it to construct statistical visualizations when working with Seabourn is helpful to understand some of the underlying matplotlib constructs this brief example shows how to plot a column in a panda's data frame as a histogram this specific example includes information about the alcohol content of several different types of Portuguese wines if you do not understand this code example you may want to review some introductory Python courses the rest of this course will assume you understand basic Python and pandas usage pandas is one of the most important Python libraries for manipulating and analyzing data in addition to providing powerful data manipulation tools panda supports basic data plotting functions the actual API is consistent with other panelist functions so it is a very useful tool the plotting is carried out by matplotlib so the resulting output looks very similar to the pure matplotlib output this functionality is very useful when you need to quickly look at data that is already in a data frame Seabourn integrates with the rest of the python data science landscape by averaging matplotlib and integrating with pandas in this example a plot similar to a histogram can be created using Seabourn's to dist plot function the resulting output looks like a histogram but actually is a Gaussian kernel density estimate or KDE in the next slide we will compare this output to the pandas generated histogram this relatively simple example is illustrative of how to use Seabourn the code is simple but can be used for powerful data analysis in addition to the analysis it makes reasonable assumptions about colors and other visual elements to make visualizations that look more pleasing than the standard matplotlib plots additionally Seabourn performs statistical analysis on the data to generate the KDE now it's your turn to try out Seaborn