09-24, 14:00–15:30 (Europe/London), Room B
Ever wish you could create beautiful, publication-worthy graphics in a few lines of code? Welcome to Seaborn, Python’s visualisation library which builds on the Matplotlib package. This tutorial will provide a hands-on tour of Seaborn’s statistical functions, from regression plots to multipanel facet grids and more!
20 years on from its first stable release, Matplotlib remains the pillar of static data visualisations in the Python ecosystem. And it’s not hard to see why: it offers a vast array of plot types and flexible customisation options. However, one caveat is that detailed statistical figures, including multi-panel grids and regression plots, can often require many lines of finely-tuned code. Seaborn mitigates this by providing wrapper functions which allow complex data visualisations to be produced in just a few lines.
This hands-on tutorial will cover:
- Seaborn’s statistical functions (kernel density estimation, regression models, etc)
- customisation of Seaborn figures with Matplotlib
By the end, you will be able to:
- use a range of techniques for communicating your data insights
- leverage the best aspects of Seaborn and Matplotlib to create beautiful graphics with concise code
- create detailed facet and pair grid plots
Some basic familiarity with Python programming and data structures including Pandas DataFrames would be helpful. The tutorial will be run using a virtual environment with all dependencies pre-installed.
Myles holds a PhD in Astrophysics and works as a Data Scientist at Jumping Rivers. With ten years of experience in Python programming, he enjoys applying his knowledge to a wide variety of projects ranging from public health to sport science. He is also deeply passionate about sharing his expertise with others, and has written and taught courses spanning data visualisation and machine learning with Python.
Parisa is a data scientist and trainer at Jumping Rivers. She enjoys using Python to visualise and extract information from data. She loves sharing her knowledge and has experience delivering courses on a variety of topics, from data visualisation to machine learning. Her enthusiasm for Python and data science developed during her PhD in Particle Physics at Durham University.