12-02, 10:00–11:30 (UTC), Workshop/Tutorial II
Want to create beautiful and complex visualisations of your data with concise code? Look no further than Seaborn, Python’s fantastic plotting library which builds on the hugely popular Matplotlib package. This hands-on tutorial will provide you with all the necessary tools to communicate your data insights with Seaborn.
Over the last few decades, a plethora of Python packages have been developed to tackle a range of data visualisation problems. This tutorial will provide a hands-on introduction to Seaborn, a fantastic open-source plotting library that builds on the Matplotlib package. Seaborn allows complex data visualisations to be created simply and easily, whilst also improving on the default look and feel of Matplotlib figures.
Topics covered:
- Overview of Seaborn’s data visualisation functions (kernel density estimation, bivariate distributions, regression models, etc)
- Creating multi-plot grids with Seaborn
- Customising Seaborn plots (time permitting)
By the end of this session, you will be able to:
- use a range of techniques for communicating your data insights
- create beautiful graphics with concise code
- compose complex visualisations as well as standard displays such as scatter plots, histograms, boxplots and more.
No previous experience in Seaborn is necessary for this tutorial. However, basic familiarity with Pandas DataFrames and plotting with Matplotlib would be useful.
This tutorial will be run using an online environment with all the dependencies and libraries pre-installed.
The tutorial materials and slides are available at https://github.com/jumpingrivers/2022-pydata-global-seaborn.
To enable a prompt start, please follow the link to the welcome page (found on the first slide) and enter your email address and the master password (also on the first slide). This will generate a personal username and password for you to access the online training environment. Don't worry if you lose your username/password. Re-submitting your email will generate the same login details each time.
No previous knowledge expected
Myles holds a PhD in Astrophysics and works as a Data Scientist at Jumping Rivers. With nine years of experience in Python programming, he enjoys applying his knowledge to a wide variety of projects ranging from astronomy to sport science. He is also deeply passionate about sharing his expertise with others, and has taught courses spanning data visualisation and machine learning with Python.
Parisa is a Data Scientist at Jumping Rivers. She enjoys using Python to visualise and extract information from data. As a trainer, she loves sharing her knowledge, and has experience delivering courses on a variety of topics, from visualisation to machine learning. Her enthusiasm for Python and data science was developed during her PhD in Particle Physics with the CDT for Data Intensive Science at Durham University.