Plotting in python

Jack of all trades is old master of none

I’m visualising data in python because it is the lingua franca of my team. I’d like it to be real-time and interactive or publication-quality, but I won’t be inconsolable if I cannot achieve both simultaneously.

Visualisation is not an especially strong suit of python; the strong suit is hodgepodge, decoupage, bricolage, and, uh, potpourri. Therefore our solution will be to cobble something together, or better, to use someone else’s cobbling.


The default option. Complicated enough that I made a new notebook. See matplotlib.


Originally a mostly browser-based visualisation, plotly’s native python suppprt (source is supposed to be quite good and quite general these days. It support high-resolution print-quality graphics, vector rendering and so on. Certainly the Plotly library is hipper than matplotlib and seems to incorporate the input of some graphic designers from the internet, which matplotlib seems to do rarely because it is old and/or confusing and/or unlikely to pop up as a highlight in your web portfolio since the main target is scientific journals.

Credit: I am indebted to Andy MacKinlay for reminding me that this is a viable concern.

Misc browser options

jupyter notebooks have a rich enough API to integrate various more exotic pure-browser graphics options; In fact, since you are now using the web browser, you can inspect a menu at browser datavis.

Here are some promising hacks:

  • superset is Airbnb’s python+browser interactive data exploration tool.

  • mpl3d plugs browser d3.js into jupyter to emulate matplotib.

    The mpld3 package is extremely easy to use: you can simply take any script generating a matplotlib plot, run it through one of mpld3’s convenience routines, and embed the result in a web page.

    2d only, AFAICT.

  • same tool (web browser), different approach: bokeh does “big-data” and streaming-based browser graphing for python. And its website probably looks the nicest out of everything I’ve mentioned, which counts for a lot. However, its print-output is bad; this is a web-oriented tool

GR wraps GR, a cross-platform visualisation framework:

GR is a universal framework for cross-platform visualization applications. It offers developers a compact, portable and consistent graphics library for their programs. Applications range from publication quality 2D graphs to the representation of complex 3D scenes. […]

GR is essentially based on an implementation of a Graphical Kernel System (GKS) and OpenGL. […] GR is characterized by its high interoperability and can be used with modern web technologies and mobile devices. The GR framework is especially suitable for real-time environments.

It will also function as a matplotlib backend. GR is somewhat brutalist in its graph presentation, but it works fine.


Visdom pumps graphs to a visualisation server.

  • Visdom aims to facilitate visualization of (remote) data with an emphasis on supporting scientific experimentation.
  • Broadcast visualizations of plots, images, and text for yourself and your collaborators.
  • Organize your visualization space programmatically or through the UI to create dashboards for live data, inspect results of experiments, or debug experimental code.


Holoviews has been crafted by some neurologists to serve science. Fresh, enthusiastic. Is it good?

HoloViews focuses on bundling your data together with the appropriate metadata to support both analysis and visualization, making your raw data and its visualization equally accessible at all times. This process can be unfamiliar to those used to traditional data-processing and plotting tools, and this getting-started guide is meant to demonstrate how it all works at a high level. More detailed information about each topic is then provided in the User Guide .

With HoloViews, instead of building a plot using direct calls to a plotting library, you first describe your data with a small amount of crucial semantic information required to make it visualizable, then you specify additional metadata as needed to determine more detailed aspects of your visualization. This approach provides immediate, automatic visualization that can be effortlessly requested at any time as your data evolves, rendered automatically by one of the supported plotting libraries (such as Bokeh or Matplotlib).

Part of a suite of visualisations tools and guides called Pyviz.

network-specific stuff

In browser datavis I found Sigma.js; there are surely more JS graph visualisations.


VisPy is OpenGL-backed data visualisation, focussing on science (ooh!). It also offers a matplotlib compatibility layer. Here are some howtos:

There seems to be a lot more writing of OpenGL shaders than one would like to draw a line graph.

However, there are less messy looking tools in the ecosystem: napari is a multidimensional image viewer.


Mayavi is an opinionated open-source commercially-backed interactive 3D visualiser. The source code repository is worryingly hard to find. For future reference, it’s here.

On a similar tip, although looking more basic and more bitrotten, is vtk - if I understand correctly, VTK is the engine used by Mayavi? Better maintained and possibly still vtk-based is Paraview, which supports pluggable backends.

Not exactly graphing libraries

  • Disney (!) has a game library Panda3d, that seems to do all the fun things

  • even more bareback, more-or-less-directly calling into openGL, but seriously, I’m a statistician, not a coder. I could also hand-pulp hemp to make my own graph paper to draw my visualisations, drawn in home-made iron gall ink, but I would find it equally hard to argue that it was an efficient prioritisation.

  • I haven’t used PREdator (although I understand it’s been around longer than I. Heh.) Wiedemann, C., Bellstedt, P., & Görlach, M. (2014). PREdator: a python based GUI for data analysis, evaluation and fitting. Source Code for Biology and Medicine, 9(1), 21. DOI. Online.

General image reading and writing

  • Imageio is a good workhorse python image system.



Of course, what we all truly want is animated GIFs. Here is a classic using Python, Pillow. See also the specialized array2gif.

There is a specialised and rather beautiful library in matplotlib, manimlib

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