The Python plotting ecosystem

Python’s advance as a data processing language had been hampered by its lack of good quality chart visualization libraries, especially compared to R with its ggplot2 and Shiny packages. By any measure, ggplot2 is a superb package that can produce stunning, publication-quality charts. R’s advance has also been helped by Shiny, a package that enables users to build web apps from R, in effect allowing developers to create Business Intelligence (BI) apps. Beyond the analytics world, the D3 visualization library in JavaScript has had an impact on more than the JavaScript community; it provides an outstanding example of what you can do with graphics in the browser (if you get time check out some of the great D3 visualization examples). Compared to D3, ggplot2, and Shiny, Python’s visualization options still lag behind, though things have evolved in the last few years.

(An example Bokeh application. Multi-tabbed, widgets, chart.)

Matplotlib is the granddaddy of chart visualization in Python, it offers most of the functionality you might want and is available with almost every Python distribution. Unfortunately, its longevity is also its problem. Matplotlib was originally based on MATLAB’s charting features, which were in turn developed in the 1980’s. Matplotlib's longevity has left it with an awkward interface and some substantially out-of-date defaults. In recent years, the Matplotlib team has updated some of their visual defaults and offered new templates that make Matplotlib charts less old-fashioned, however, the library is still oriented towards non-interactive charts and its interface still leaves much to be desired.

Seaborn sits on top of Matplotlib and provides a much more up-to-date interface and visualization defaults. If all you need is a non-interactive plot, Seaborn may well be a good option; you can produce high-quality plots in a rational way and there are many good tutorials out there.

Plotly provides static chart visualizations too, but goes a step further and offers interactivity and the ability to build apps. There are some great examples of Plotly visualizations and apps on the web. However, Plotly is a paid-for solution; you can do most of what you want with the free tier, but you may run into cases where you need to purchase additional services or features.

Altair is another plotting library for Python based on the 'grammar of graphics’ concept and the Vega project. Altair has some good features, but in my view, it isn’t as complete as Bokeh for business analytics.

Bokeh is an ambitious attempt to offer D3 and ggplot2-like charts plus interactivity, with visualizations rendered in a browser, all in an open-source and free project. Interactivity here means having tools to zoom into a chart or move around in the chart, and it means the ability to create (browser-based) apps with widgets (like dropdown menus) similar to Shiny. It’s possible to create chart-based applications and deploy them via a web server, all within the Bokeh framework. The downside is, the library is under active development and therefore still changing; some applications I developed a year ago no longer work properly with the latest versions. Having said all that, Bokeh is robust enough for commercial use today, which is why I’ve chosen it for most of my visualization work.

Holoviews sits on top of Bokeh (and other plotting engines) and offers a higher-level interface to build charts using less coding.

It’s very apparent that the browser is becoming the default visualization vehicle for Python. This means I need to mention Flask, a web framework for Python. Although Bokeh has a lot of functionality for building apps, if you want to build a web application that has forms and other typical features of web applications, you should use Flask and embed your Bokeh charts within it.

If you’re confused by the various plotting options, you’re not alone. Making sense of the Python visualization ecosystem can be very hard, and it can be even harder to choose a visualization library. I looked at the various options and chose Bokeh because I work in business and it offered a more complete and reliable solution for my business needs. In a future blog post, I'll give my view of where things are going for Python visualization.