Abstract: 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.
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.
All code examples will be presented using Jupyter Notebooks. Attendees should have basic knowledge of Python and be comfortable working in Jupyter Notebooks. In addition, a basic understanding of pandas will be beneficial, but is not required; reviewing the first section of this pandas workshop (https://github.com/stefmolin/pandas-workshop) will be sufficient.
Bio: Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She is also the author of Hands-On Data Analysis with Pandas, which is currently in its second edition. She holds a bachelors of science degree in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science. She is currently pursuing a masters degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.