Visualisation


If you have data and need to visualise it then this module is for you. Follow Sam Ball and his elegant video series, with accompanying Jupyter Notebooks, as he shows you how to create beautiful plots using Python. Following a deeper look at matplotlib we quickly discover that this is not all there is to plotting with Python. A peek at the amazing Seaborn plotting module is followed by instruction on how to incorporate Pandas and make plots interactive with the Bokeh module. You will be dazzled by this impressive showcase of how Python can be used to create beautiful and interactive plots of real data. Once you've completed this module your plots will be so breathtakingly beautiful you will have to exhibit them in galleries and museums.

Module Coordinator

Sam Ball




A Deeper Look Into Matplotlib



Matplotlib is the "grandfather" data visualisation tool for data in Python - it offers unparalleled control over graphs and diagrams for data, and lets us annotate and customise figures to our heart's content. Matplotlib is built upon for other important modules we'll use later, such as Seaborn, which is more used for statistical visualisation.



Statistical Plots in Seaborn



Seaborn is a module that builds off matplotlib's functionality to let us create great looking graphs easily, usually within one function call. In a data science context, it's used to make very quick and rough judgements by eye, before going on to test them using statistical methods. By the end of this guide you should have a strong grip on how to use seaborn, and be comfortable with representing data of different types using seaborn.



Pandas In Built Vis



Although Pandas is mainly used for creating and manipulating it's DataFrame object, it has some inbuilt data visualisation options that can be helpful to know when we want to create very quick graphs using default templates Pandas provides. Since these work through matplotlib, we can edit stylesheets and color options to make very professional looking graphs very quickly straight from the Pandas ecosystem. By the end of this guide you should be able to use Pandas' built in plots as a shortcut for creating quick plots when you don't need to use other libraries.



Interactive Plots With Bokeh



If you've ever wondered how blogs and news websites create interactive data visualisations, chances are they either use d3.js (written in javascript), or Bokeh (written in Python). If you're looking to spend some time to make your visualisations stand out from the rest, Bokeh is a great way to do it. By the end of this guide you should have some idea how to create interactive visualisations and be able to start your journey to make world class data visualisations.