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Notes on data visualization and graphics in Python

Yesterday, I gave a talk at ChiPy's Scientific SIG meeting, providing an overview of the data visualization packages in Python, from the perspective of a former scientist who switched from using R to Python for large-scale data analysis. My talk was framed around the current limitations in Python's data visualization packages and what improvements I would like to see. Here is a written, and considerably more polished, version of what I spoke about.

What we talk about when we talk about data visualization

It's hard to talk about "Big Data" because it is complicated. What does that mean? There are many variables, both continuous and discrete, exhibiting patterns and trends that cannot be easily modeled linearly and frequently riddled with a lot of noise. The data does not yield easy takeaways that can be summarized in a single phrase. Thus, it becomes more important than ever that we have tools to not only analyze our data correctly but also to communicate our findings to others.

That's the importance of data visualization. Edward Tufte, who is one of the pioneers in this field, sums up the criteria for an effective graphic in this quote from his classic, Visual Display of Quantitative Information:

Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.

It's important that we convey the overall message as well as key details in a format that is clear, simple, and easy to interpret.

We can start to develop some guidelines for creating such "excellent" data graphics by looking at how humans process visual information. How do we distinguish between different quantities? There's a maxim that pie charts are much worse than bar charts for displaying categories with similar amounts because our eye is better able to notice small differences in length than area or angle. Another question to ask is how we notice relationships among individual data points. Graphical elements that are located close together or share similar properties, like color or shape, or even directly creating connections or boundaries help the viewer draw connections between data components.

Broader considerations include thinking about directing the viewer's attention: where does their eye fall and are there indicators that show where to look next? Possibly most important of all, every data graphic has a story to tell, and the success of any data visualization depends on having a coherent narrative. "A picture is worth a thousand words," but we need to know what those words are in the first place.

ggplot2 and a grammar of graphics

Another pioneer in the field, Leland Wilkinson, tried to systematize how we go about creating data visualizations through what he called a "grammar of graphics".It breaks down the components of a plot into the following abstract elements:

  • Aesthetics, which visually represent data variables
  • Geometric objects, which depict data points
  • Scales and coordinates, which communicate information about quantities
  • Statistics, which are transformations applied to the data to illustrate analysis
  • Facets, which are how multiple related plots can be tied together in a graphic
  • Annotations, which are text labels applied to a plot

These elements form the foundation for the R library, ggplot2, which has set a high standard for data visualization packages and is widely used in the R community. Its particular strengths are a clear and consistent syntax, based on the grammar of graphics; a layering system, which allows you to quickly add new elements to a plot; and a faceting system, which makes it easy to generate data graphics with multiple related plots, when dealing with complicated data.

Of particular note is how simple it is to apply statistical functions, like calculating the mean and standard error to summarize a lot of data points, and useful, attractive plotting methods, like geom_smooth(), which automatically applies a smoothing loess function to your data and draws a line on top of a translucent ribbon that represents the 95% confidence interval.

It also makes it easy to go from a single plot to multiple plots split across an additional variable. In my code examples, I show how I go from a single plot depicting mean car mileages measured during city and highway driving over time to multiple plots depicting how this relationship changes with engine displacement.1 The final version illustrates the trend among four different variables (miles per gallon, year, type of mileage measured, and engine displacement), in an uncluttered two-dimensional graphic that is easy to interpret.

So if this R library is so awesome, why would we ever want to use Python instead? Well, Python has quite a few advantages over R for data science, particularly when we are dealing with large amounts of data. It is overall faster and more efficient than R, and because it is a general purpose programming language, it is easier to integrate into applications that may want to use the results of your data analysis. It has highly readable syntax and testing frameworks that make it easier to write robust code. In an industry setting, Python is much better suited to deploying and releasing data-based products, which is why it is more widely used.

What's available in Python

When it comes to data visualization packages for Python, options: matplotlib, seaborn and ggplot. All of these fall short of R's ggplot2 and have certain limitations that stop them from being useful for generating excellent data graphics. I'll start with matplotlib, which is probably the most widely used library.

matplotlib is a port of the Matlab library. I often joke that I haven't met a single person who enjoys coding in Matlab. Unfortunately, that also applies to matplotlib. A lot of people coming to Python are already familiar with this library from Matlab, and it does have powerful methods for rendering 3D or interactive plots. But matplotlib has several pitfalls. Its syntax does not take any advantage of Python's clarity and is difficult to read. Without customization, it generates some truly ugly plots...and it doesn't make that customization easy to do either. (The plots shown in the slides are actually much better than what the default matplotlib style looks like, because Jupyter notebooks automatically apply a style that is meant to resemble the default theme for ggplot2.) It's certainly possible to "prettify" matplotlib if you dig down into its functions to control the appearance of your plots, but it doesn't make it easy. Finally, matplotlib doesn't have any functions that actually deal with subsetting or transforming your data. For example, you have to manually write code to filter your data by categories if you want to show data points in multiple colors on a single plot. The lack of such higher-level functions make using matplotlib unsatisfactory for data visualization despite all its power as a graphics package.

seaborn is a wrapper for matplotlib that simplifies its syntax and generates much more attractive looking graphs. It also has some data handling functions similar to ggplot2 that facilitate the process of making more complex plots. However, there are some obvious gaps in its functionality, which seems to be reflected in how it also seems to have incomplete documentation. In particular, what I've noticed is that it has a limited range of plot types, each of which is fairly inflexible in how it will handle data. In my code examples, I show how the stripplot() and factorplot() functions treat the variable for year as discrete rather than continuous data, which means that labels along the x-axis end up unreadable as every value for year is treated as a separate category. There's also no support for more specialized plots, like contour plots. The methods available for customizing text labels and annotations are limited and have inconsistent syntax from plot type to plot type. In some cases, it requires drilling down into the underlying matplotlib code to get the effect you want. So while there are many promising aspects to seaborn, it seems that it needs further development to be truly mature as a data visualization tool.

The last option, which may be the least known or used out of the three, is ggplot, a Python port of ggplot2 that is being developed by Ŷhat. Now at first, this package sounds like the answer we've been wanting: a way to use ggplot2's powerful approach to data visualization in a Python environment. However, it is also an incomplete package still under development, which becomes clear when you look at its documentation, where many pages are completely empty. What's great is that it emulates ggplot2's syntax, breaking down a plot into abstract elements that helps you think about how to visually represent your data in an effective way. What's not so great is that it's missing a lot of key functions for statistical transformations and plot types. In the code examples, when I attempt to use the geom_jitter() method to prevent overplotting, there is no actual jittering on the points. The implementation of geom_smooth() also seems to have bugs in how it calculates the 95% confidence interval and how it applies loess smoothing to the data. And faceting just creates a visually unappealing, proportionally unbalanced set of plots. In short, there needs to be more development of this package in order for it to be fully usable.

Where do we go from here?

When compared to the kind of data graphics we can produce in R with ggplot2, the status quo in Python is far from satisfactory...but we don't have to be demoralized. I think we can look at the situation as an opportunity: to create a Python data visualization package that can rival ggplot2 in its power and ease of use. matplotlib, seaborn and ggplot are all open-source projects to which Python programmers and data scientists can contribute. Or perhaps the Python community can build a new package from the ground up that has ggplot2's functionality.

I would like to see a data visualization tool that implements what is key to ggplot2's success in the R community. People tend to try to imitate the look and style of ggplot2 graphics and stop there. But the core innovation of ggplot2 is how it utilizes the "grammar of graphic" concepts to organize how we construct plots. There's no reason that we can't create that in Python.

In the meantime, we also have the option of using R and Python together, which is particularly easy to do in a Jupyter notebook environment with the rpy2 package. (Or if you want to run Python from R, there is the rPython library on CRAN.)

As a footnote, while I've focused on static data graphics here, there are also tools available for constructing interactive plots in both languages, ggvis for R and bokeh for Python, which I encourage people to check out as well.

For code examples and pretty plots, please look at my Jupyter notebook or my slides.


  1. The data for all the plots shown in my slides comes from an open-source data set provided by the EPA, processed and made available as an R package, fueleconomy

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