Beyond the Basics: Data Visualization in Python
- Wicklow Hall 2B
- Start (Dublin time):
- Start (your time):
- 180 minutes
The human brain excels at finding patterns in visual representations, which is why data visualizations are essential to any analysis. Done right, they bridge the gap between those analyzing the data and those consuming the analysis. However, learning to create impactful, aesthetically-pleasing visualizations can often be challenging. This session will equip you with the skills to make customized visualizations for your data using Python.
TutorialPyData: Software Packages & Jupyter
Section 1: Getting Started With Matplotlib
We will begin by familiarizing ourselves with Matplotlib. Moving beyond the default options, we will explore how to customize various aspects of our visualizations. By the end of this section, you will be able to generate plots using the Matplotlib API directly, as well as customize the plots that libraries like pandas and Seaborn create for you.
Section 2: Moving Beyond Static Visualizations
Static visualizations are limited in how much information they can show. To move beyond these limitations, we can create animated and/or interactive visualizations. Animations make it possible for our visualizations to tell a story through movement of the plot components (e.g., bars, points, lines). Interactivity makes it possible to explore the data visually by hiding and displaying information based on user interest. In this section, we will focus on creating animated visualizations using Matplotlib before moving on to create interactive visualizations in the next section.
Section 3: Building Interactive Visualizations for Data Exploration
When exploring our data, interactive visualizations can provide the most value. Without having to create multiple iterations of the same plot, we can use mouse actions (e.g., click, hover, zoom, etc.) to explore different aspects and subsets of the data. In this section, we will learn how to use a few of the libraries in the HoloViz ecosystem to create interactive visualizations for exploring our data utilizing the Bokeh backend.
This workshop is for anyone with basic knowledge of Python and an interest in learning how to take their data visualization skills to the next level. We will be working with Jupyter Notebooks, so attendees should familiarize themselves with the interface (i.e., know how to run/edit a cell) beforehand. In addition, a basic understanding of pandas will be beneficial, but is not required; reviewing the first section of this pandas workshop will be sufficient.
Please set up your local environment beforehand per the instructions here.