Welcome. My name is Andrea Giussani, and I am going to be your instructor for this course on Interactive Data Visualization with Python using Bokeh. In this course, we will cover the fundamental tools that are necessary to build interactive plots in Python. Although it’s not essential, I encourage you to attend the course Data Visualization with Python using Matplotlib before starting this course.

Bokeh is an interactive visualization library that provides visual artefacts for modern web browsers. The main difference when compared to other python visualization libraries, such as Matplotlib, is that Bokeh allows you to generate interactive and flexible plots without the need to learn Javascript or advanced visualization tools such as D3. Hence, Bokeh allows you to create  elegant, concise, and versatile graphics, and provides high-performance interactivity over large or streaming datasets.

Bokeh’s first release came out in April 2013, and was mainly developed by Peter Wang and other developers as a project supported by Continuum Analytic. It became a NumFOCUS-sponsored Project in 2018.

In general, Bokeh exposes two interface levels to users. On one hand, we have bokeh.plotting, which is described as a higher-level interface centred around composing visual glyphs (for instance a figure), or bokeh.models, which is described as a low-level interface that provides more flexibility to application developers (for instance, through widgets).

This course will cover both interfaces, but we will mainly focus on the former, and therefore we will not dive into all advanced aspects of the bokeh.models interface. If you are curious to understand how this family works in the back-end, please visit the official documentation (https://docs.bokeh.org/en/latest/docs/reference/models.html).

Another great thing about bokeh is that it provides at least two ways to embed plots and data into HTML documents.

On one hand, we have what we call Standalone Documents: output is translated into standalone HTML or JS outputs, which gives data scientists more flexibility during the development phase of a project since they are not backed by a Bokeh server.

On the other hand, we have Bokeh Applications: these are very interesting since, in addition to all the features of standalone documents, it is also possible to connect events and tools to real Python callbacks that execute in the Bokeh server. In this way, we can build interactive dashboards and bokeh web apps in just a few lines of code.

Please note that in this course, we will not investigate the development of a web app in a bokeh server but the main focus will be on the production of standalone documents.

I strongly recommend that you visit the GitHub repository for this course: there you can find the data we will use to illustrate the methods in this course, and possible additional material related to the course.

This course is structured as follows: in Lecture 2 we will look at two important key concepts in bokeh: `Column Data Source` and `Glyphs`. In Lecture 3 we will cover different aspects related to the customization of a bokeh plot, and we will focus on the possibility to introduce interactivity into a bokeh object.

In Lecture 4, we will dive into Inspectors, which are passive tools that report information about the plot.

In Lecture 5, we will investigate different ways to plot multiple bokeh objects in one figure, and finally, in Lecture 6, we will focus on plot methods for categorical variables.

I am really excited to get started, so I’ll see you in lecture two!