SciPy 2016 Retrospective

Last week I attended my first SciPy conference in Austin. I’ve been to the past three PyCons in Montreal and Portland, and aside from my excitement to learn more about the great scientific Python community, I was curious to see how it compared to the general conference I’ve come to know and love.

SciPy, by my account, is a curious microcosm of the academic open source community as a whole. It is filled with great people doing amazing work, releasing incredible tools, and pushing the frontiers of features and accessibility in scientific software. It is also marked by some of the same problems as the larger community: a stark lack of gender (and other) diversity and a surprising (or not) lack of consciousness of the problem. I’ll start by going over some of the cool projects I learned about and then move on to some thoughts on the gender issue.

Cool Stuff


Several new projects were announced, and several existing projects were given some needed visibility. The first I’ll talk about is nbflow. This is Jessica Hamrick’s system for “one-button button reproducible workflows with the Jupyter notebook and scons.” In short, you can link up notebooks in build system via two special variables in the first cells of a collection of notebooks – __depends__ and __dest__ – which contain lists of source and target filenames and are parsed out of the JSON to automagically generate build tasks. Jessica’s implementation is clean and can be pretty easily grokked with only a few minutes of reading the code, and it’s intuitive and relatively well tested. She delivered a great presentation with excellent slides and nice demos (which all worked ;)).

The only downside is that it uses scons, which isn’t Python 3 compatible and isn’t what I use, which must mean it’s bad or something. However, this turned out to be a non-issue due to the earlier point about the clean codebase: I was able to quickly build a pydoit module with her extractor, and she’s been responsive to the PR (thanks!). It would be pretty easy to build modules for any number of build systems – it only requires about 50 lines of code. I’m definitely looking forward to using nbflow in future projects.


The Jupyter folks made a big splash with JupyterLab, which is currently in alpha. They’ve built an awesome extension API that makes adding new functionality dead simple, and it appears they’ve removed many of the warts from the current Jupyter client. State is seamlessly and quickly shipped around the system, making all the components fully live and interactive. They’re calling it an IDE – an Interactive Development Environment – and it will likely improve greatly upon the current Python data exploration workflow. It’s reminiscent of Rstudio in a lot of ways, which I think is a Good Thing; intuitive and simple interfaces are important to getting new users up and running with the language, and particularly helpful in the classroom. They’re shooting to have a 1.0 release out by next year’s SciPy, emphasizing that they’ll require a 1.0 to be squeaky clean. I’ll be anxiously awaiting its arrival!


Binder might be oldish news to many people at this point, but it was great to see it represented. For those not in the know, it allows you to spin up Jupyter notebooks on-demand from a github repo, specifying dependencies with Docker, PyPI, and Conda. This is a great boon for reproducibility, executable papers, classrooms, and the like.


The first keynote of the conference was yet another plotting library, Altair. I must admit that I was somewhat skeptical going in. The lament and motivation behind Altair was how users have too many plotting libraries to choose from and too much complexity, and solving this problem by introducing a new library invokes the obligatory xkcd. However, in the end, I think the move here is needed.

Altair a python interface to vega-lite; the API is a straight-forward plotting interface which spits out a vega-lite spec to be rendered by whatever vega-compatible graphics frontend the user might like. This is a massive improvement over the traditional way of using vega-lite, which is “simply write raw JSON(!)” It looks to have sane defaults and produce nice looking plots with the default frontend. More important, however, is the paradigm shift they are trying to initiate: that plotting should be driven by a declarative grammar, with the implementation details left up to the individual libraries. This shifts much of the programming burden off the users (and on to the library developers), and would be a major step toward improving the state of Python data visualization.

Imperative (hah!) to this shift is the library developers all agreeing to use the same grammar. Several of the major libraries (bokeh and already use bespoke internal grammars, and according to the talk, looking to adopt vega. Altair has taken the aggressive approach: the tactic seems to be to firmly plant the graphics grammar flag and force the existing tools to adopt before they have a chance to pollute the waters with competing standards. Somebody needed to do it, and I think it’s better that vega does.

There are certainly deficiencies though. vega-lite is relatively spartan at this point – as one questioner in the audience highlighted, it can’t even put error bars on plots. This sort of obvious feature vacuum will need to be rapidly addressed if the authors expect the spec to be adopted wholeheartedly by the scientific python community. Given the chops behind it, I fully expect these issues to be addressed.

Gender Stuff

I’ve focused on the cool stuff at the conference so far, but not everything was so rosy. Let’s talk about diversity – of the gender sort, but the complaint applies to race, ability, and so forth.

There’s no way to state this other than frankly: it was abysmal. I immediately noticed the sea of male faces, and a friend of mine had at least one conversation with a fellow conference attendee while he had a conversation with her boobs. The Code of Conduct was not clearly stated at the beginning of the conference, which makes a CoC almost entirely useless: it shows potential violators that the organizers don’t really prioritize the CoC and probably won’t enforce it, and it signals the same to the minority groups that the conference ostensibly wants to engage with. As an example, while Chris Calloway gave a great lightening talk about how PyData North Carolina is working through the aftermath of HB2, several older men directly behind me giggled amongst themselves at the mention of gender neutral bathrooms. They probably didn’t consider that there was a trans person sitting right in front of them, and they certainly didn’t consider the CoC, given that it was hardly mentioned. This sort of shit gives all the wrong signals for folks like myself. At PyCon the previous two years, I felt comfortable enough to create a #QueerTransPycon BoF, which was well attended; although the more focused nature of SciPy makes such an event less appropriate, I would not have felt comfortable trying regardless.

The stats are equally bad: 12 out of 124 speakers, 8 out of 52 poster presenters, and 4 out of 37 tutorial presenters were women, and the stats are much worse for people of color. The lack of consciousness of the problem was highlighted by some presenters noting the great diversity of the conference (maybe they were talking about topics?), and in one case, by the words of an otherwise well-meaning man whom I had a conversation with; when the 9% speaker rate for women was pointed out to him, he pondered the number and said that it “sounded pretty good.” It isn’t! He further pressed as to whether we would be satisfied once it hit 50%; somehow the “when is enough enough?” question always comes up. What’s clear is that “enough” is a lot more than 9%. This state of things isn’t new – several folks have written about it in regards to previous years.

There are some steps that can be taken here – organizers could look toward the PSF’s successful efforts to improve the gender situation at PyCon, where funding was sought for a paid chair (as opposed to SciPy’s unpaid position). The Code of Conduct should be clearly highlighted and emphasized at the beginning of the conference. For my part, I plan to submit a tutorial and a talk for next year.

I don’t want to only focus on the bad; the diversity luncheon was well attended, there was a diversity panel, and a group has been actively discussing the issues in a dedicated channel on the conference Slack team. These things signal that there is some will to address this. I also don’t want to give any indication that things are okay – they aren’t, and there’s a ton of work to be done.


I’m grateful to my adviser Titus for paying for the trip, and generally supporting my attending events like this and rabble rousing. I’m also grateful to the conference organizers for putting together an all-in-all good conference, and to all the funders present who make all this scientific Python software that much more viable and robust. For anyone reading this and thinking, “I’m doing thing X to combat the gender problem, why don’t you help out?” feel free to contact me on twitter.