Adventures in the Python Visualisation Landscape

Calling the current python visualisation landscape fragmented would probably be an understatement, since itself requires a visualisation to even begin to comprehend. For various reasons I have been unhappy with the tool I was using for visualisation at that time and I have been searching for the one visualisation package to rule them all (spoiler: it doesn’t exist)

About a year ago I was using Matplotlib for all my figures, which enabled me to create anything I wanted, usually with a stackoverflow answer giving me a working example to adapt. Problem was, nearly everything required a stackoverflow post or the documentation, making the process of building these figures slow and tedious. Around the same time I was getting fed up with Matplotlib, I found Jake VanderPlas’ PyCon talk on The Python Visualisation Landscape which has since acted as both a map and a list of achievements to unlock.

Altair 1.2

Somewhat naturally the first package I looked at for improving my visualisation workflow was Altair which was at the time a 1.2 release. At the time I thought it was fine for simple figures, however my data was not formatted to make the most of what Altair could offer, which as a relative newbie to pandas was frustrating. Another sticking point with Altair was working out how to customise figures, After Matplotlib, I was expecting to search what I wanted to in Google and have appropriate stackoverflow answer on the first page of results. With Altair being a new package, this how-to style documentation didn’t exist, which meant I never worked out how to customise the figure. The most frustrating of the default settings was the axis labels which use the SI Prefix for large numbers i.e. 1M for $1 \times 10^6$ and 1n for $1 \times 10^{-9}$, something which I never worked out how to change at the time. Another problem I had with Altair (and Matplotlib) was the lack of interactivity. I was in the early stages of data investigation which required both a high level overview of the trends, while also being able to look at smaller regions in more detail. Having to constantly change axis ranges was making it slow and frustrating to create a figure with the appropriate view.

Bokeh

With an emphasis on interactivity, Bokeh was the tool I was looking for to investigate data on different scales. With the ability to generate figures in a notebook, for quick visualisations of datasets to understand the data, and compare it with previous studies, and also as a Bokeh server application, to perform standard analyses on large volumes of data, like ensuring a simulation is running properly. Despite having these interactive visualisations, bokeh is lacking in the same way as Matplotlib, it takes a long time to specify everything required to create the figures. While there was a start on a high level plotting interface in the form of bkcharts, the code is now unmaintained and directs users to Holoviews.

Holoviews

Like Altair, Holoviews is a declarative plotting interface, providing a quick and simple interface for constructing figures. Holoviews is structures around the idea of describing data on creation of the dataset, rather than the construction of the figure, allowing for simple figure definitions. Rather than being a library that actually creates a figure, Holoviews performs the reasoning about the dataset and passing the rendering off to Bokeh, Matplotlib, or Plotly. I initially saw this as a big strength of Holoviews, that I could generate interactive visualisations in bokeh, change the output to Matplotlib and have a configurable figure for publication. In practice this is not so simple, with some modifications to plot style parameters changing between the different output formats and having a limited range of customisations.

The drawback of having this special annotated data object is that you lose all the flexibility of having a pandas DataFrame and the vast array of operations that allows. This is important to me because my field of science has a long history of researchers taking some quantities and combining them to give a describable temperature dependence. This lack of flexibility led me back to Altair, this time version 2.0.

Altair 2.0

Why Altair again? When I first tried Altair I was approaching it from Matplotlib, with it’s extensive documentation in the form of the technical reference and the numerous how to guides. This time I was approaching it from Bokeh and Holoviews, both of which are relatively new libraries with some teething problems. As a consequence I had become much better at problem solving issues and navigating technical reference materials. Another major moment of discovery was making the connection that since Altair implements the Vega-Lite specification, I should have a look at the Vega-Lite documentation. This turned out to be particularly helpful because the Vega-Lite documentation is currently more extensive than for Altair.

It was in reading the Vega-Lite documentation that I finally understood how to use transform functions in Altair. These are a set of functions that perform computations on the input dataset to generate the resulting figure. This allows me to have a single canonical dataset, with data transformations like ratios of two quantities tied to the figure, rather than following awkwardly named variables around. One example of these functions is to make the data in the cars dataset useful for the majority of the world’s population by converting the units as part of the figure definition.

import altair as alt
from vega_datasets import data

cars = data.cars()

chart = alt.Chart(cars).mark_circle().transform_filter(
    alt.expr.datum.Miles_per_Gallon > 0
).transform_calculate(
    'Fuel Economy (L/100 km)', '235.2 / datum.Miles_per_Gallon'
).transform_calculate(
    'Weight (kg)', 'datum.Weight_in_lbs * 0.45'
)
chart.encode(
    x='Fuel Economy (L/100 km):Q',
    y='Weight (kg):Q',
    size='Acceleration:Q'
)

Fuel economy (L/100km) vs weight (kg) from the cars dataset.

This allows for only storing the fundamental values in the dataset and being able to compute derived values as part of the figure, something that is particularly useful in my workflow.

This computing of values, also extends to the computation of histograms, complete with shortened notation. Using the chart object from above it is possible to easily create a histogram

chart.mark_bar().encode(
    x=alt.X('Fuel Economy (L/100 km):Q', bin=True),
    y='count():Q',
)

Histogram of the fuel economy in the cars dataset.

Where setting bin=True will create bins with the default parameters, and the count():Q on the y axis counts the elements in each bin. Instead of count() it is also possible to perform other aggregations, like computing the mean of a column.

chart.mark_bar().encode(
    x=alt.X('Fuel Economy (L/100 km):Q', bin=True),
    y='mean(Weight (kg)):Q',
)

Histogram of the fuel economy in the cars dataset.

For a more comprehensive view of using Altiar, have a look at either the Example Gallery, or a case study.

Each of the visualisation libraries in python have their own strengths and weaknesses, types of visualisations they excel at, and others you wouldn’t want to try. For me, while Altair does still have a some quirks, most notably in the handling of large datasets, and a somewhat complicated method of setting titles, it provides a simple and intuitive interface to data which at the time of writing makes it the first tool I will reach for to understand a dataset.