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in turn developed in the 1980’s. Matplotlib's longevity has left it with an awkward interface and some substantially out-of-date defaults.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.

Choropleth maps - pretty but misleading

Pretty, but misleading

You see choropleth maps everywhere: on websites, on the TV news, and in applications. They’re very pretty, they appeal to our sense of geography, but they can be horribly misleading. I’m going to show you why that’s the case and show you ways designers have sought to get around their problems.

What's a choropleth map?

A choropleth map is a geographic map with regions colored according to some criteria. A great example is election maps used in US Presidential elections. Each of the states is colored according to the party that won the state. Here’s an example result from a US Presidential election. Can you see what the problem is?

(Image Credit: adapted from Wikipedia.)

Looking at the map, who do you think won the election (I’m deliberately not telling you which election)? Do you think this election was a close one?

The trouble is, the US population density varies considerably from state to state, as does the number of Electoral College votes. In 2020, Rhode Island will have 4 Electoral College votes compared to Montana’s 3, but Montana is 120 times larger on the map. If you just glance at most US Presidential election choropleth maps, it looks like the Republican candidate won, even when he didn’t. The reason is the geographically large rural states are mostly Republican but have few Electoral College votes because their populations are relatively low. So the election choropleth map looks mostly red. Our natural tendency is to assume more ink = more important, but the choropleth map breaks this relationship giving a misleading representation.

By the way, the map I showed you is from the 1976 election, in which Jimmy Carter won 267 Electoral College votes to Gerard Ford's 240 (24 states to 27). Did you get who won from the map? Did you get the size of the victory?

Let’s take another example, the 2014 Scottish Independence referendum. Here’s the result by council area (local government area), pink for remain, green for independence. In this case, the remainers won, but what do you think the margin was? Was it close?

(Image credit: adapted from Wikipedia entry: https://commons.wikimedia.org/wiki/File:Scottish_independence_referendum_results.svg)

Despite the choropleth map’s overwhelming remain coloring, the actual result was 55%-45%. It looks like an overwhelming remain victory because the Scottish population is concentrated in a few areas. As in the US, there are large rural areas with few people that take up large amounts of chart space, exaggerating their level of importance.

Cartograms

Can we somehow represent data using some kind of map in a more proportional way? Cartograms distort the underlying geography to better represent some underlying variable. There’s a great example on the Geographical.co.uk website for the UK 2019 general election.

(Image credit: geographical.co.uk - taken from the article: http://geographical.co.uk/geopolitics/geopolitics/item/3557-ukge2019)

The chart on the left is a straight-up choropleth map of the UK by parliamentary constituency - note that constituencies have roughly equal populations but can have very different geographical areas. Blue is a Conservative parliamentary seat, yellow is SNP, orange is Liberal Democrat and red is Labour.  The result was a decisive Conservative victory, but the choropleth map makes it look like a wipeout, which it wasn't. The middle chart makes each parliamentary constituency the same size on the page (actually hexagons for reasons I won't go into). Rural areas are shrunk and urban areas (especially London) are greatly expanded. The cartogram shows a much fairer, and in my view, more reasonable representation of the result. The chart on the right is a 'gridded population cartogram' that sizes each constituency by the number of people living in it. There are several of these maps on the web, but frankly, I've never been able to make much sense of them.

Cartograms in the house of mirrors

Mark Newman at the University of Michigan has a site presenting the 2016 US Presidential election as cartograms which is worth a look. Here's his map scaling the states to their Electoral College votes. Because the proportion of red and blue ink follows the Electoral College votes, the cartogram gives a fairer representation of the result (I find this representation easier to understand than the 2019 UK election result in the third chart above.).

(Image credit: Mark Newman, University of Michigan.)

An alternative approach is to use hexagons to represent the result:

(Image credit: ESRI UK & Ireland: https://communityhub.esriuk.com/geoxchange/2016/11/1/us-election-2016-battle-of-the-maps)

The hexagon representation has become much more popular in recent years, leading to designers calling this kind of chart a hexagram. As you might have guessed, on the whole, I prefer this type of cartogram.

All this is great in theory, but in practice there are problems. Hexagrams are great, but they're still unfamiliar to many users and might require explanation. They can also distort geography, reducing the display's usefulness, for example, can you easily identify Utah on the 2016 US Presidential hexagram above? Most packages and modules that display data don't yet come with out-of-the-box cartograms, meaning developers have to create something from scratch, which takes more effort.

What I do

Here's my approach: use choropleth maps for planning and hexagrams or other charts to represent quantitative results. Planning usually involves some sense of geography, for example, territory allocation in sales, in this case, a choropleth map can be useful because of its close ties to the underlying geography. To represent quantitative information (like election results), I prefer bar charts or other traditional charts. If you want something that's more geographical, I recommend some form of hexagram, but with the warning that you might have to build it yourself which can be very time-consuming.

Finding out more

Danny Dorling has written extensively about cartograms and I recommend his website: http://www.dannydorling.org/
The WorldMapper website presents lots of examples of cartograms using social and political data: https://worldmapper.org/