Plotting data in Python can be a beautiful, interactive experience! Modern plots include hover points, or info boxes that provide the user with a deeper understanding of the plots and the data behind them. We can also use Python to animate our results to show trends over time. This workshop will focus on libraries such as Bokeh to build colorful, dramatic graphical displays of your data. These plots go well beyond 2D published graphs and allow users to explore their outliers and demonstrate dramatic interactive data experiences either online or during conferences and presentations. For the user, the power of animated, interactive data plots translates into understanding and identifying micro-level data within macro-level analysis, all while looking at change over time!
This two-part series (February 16 and 23) will begin with syntax for basic data plots in Python, adding interactive labels and hover points in the first week. During the second session, we will focus on adding dimensionality to plots through colors, symbols and animation to make a typical bivariate plot capture up to five dimensions of information. These powerful visualization techniques add depth to data analysis without overwhelming users. Welcome to dynamic, interactive plotting in Python!
Objectives: Working with a shared data set, each participant will work through labelling plots, changing graph structure, altering the color scheme and adding interactive effects such as hover points, dynamic legends and flip chart options within the plot.
The primary aim is for the participant to grow comfortable reading and altering complex syntax in libraries like Bokeh to allow for the production of personalized graphical data presentation.
Prerequisites: Participants are expected to bring a laptop with a Mac, Linux, or Windows operating system that they have administrative privileges on. Prior programming experience in Python is helpful but not essential. Please install Anaconda on your machine prior to the workshop.