Last week I attended the SciPy2019 conference for the first time! It was great fun, and I had an opportunity to catch up with my Elegant SciPy co-authors Juan and Stefan (only the second time we’ve all been in the same room!). These are my notes from the conference, including talks I enjoyed and tools or ideas that I found interesting.

Schedule: https://www.scipy2019.scipy.org/confschedule Proceedings (papers): http://conference.scipy.org/proceedings/scipy2019/ Talks on YouTube: https://www.youtube.com/playlist?list=PLYx7XA2nY5GcDQblpQ_M1V3PQPoLWiDAC

Day 1: July 10, 2019

Vaex: Out of Core Dataframes for Python - Maarten Breddels

Vaex - allows real-time data exploration of a DataFrame with a billion rows. Similar syntax to pandas. Creates “expressions” that are not actually executed until necessary.

To Comment or Not to Comment? A Data-Drive Look at Conflicting Attitudes Towards Commenting - Patricia Hanus

Why everyone is so afraid of commenting, and how we can use comments as a learning opportunity. Really engaging talk!

Inclusive Leadership: Engaging Contributors in the Long-Term - Tania Allard

I haven’t had a chance to watch this one yet, as I was in a concurrent session, but it got a rave on Twitter!

A Geographers Journey into AI: Mapping Urban Trees from Scratch - Verena Griess

A nice machine learning talk, well presented.

Lightning talks

These are great fun. Many puns!

How to create a minimal python package to collect your random functions

Day 2: July 11, 2019

Generational Changes in Support for Gun Laws: A Case Study in Computational Statistics - Allen Downey

Interesting topic. Basically found that young people are less likely to support gun control. Uses linear models and talks about how to interpret them. Also comments on confounding variables.

Supporting Open Source Software for Science - J. Freeman, N. Sofroniew

Talk about the currently open Chan Zuckerberg Initiative funding call for Essential Open Source Software for Science. CZI partner with researchers to help them build open source software. Currently sppporting bioconductor, seurat and scanpy They kept funding science, or scientists, and found themselves indirectly funding open source packages. So they decided to specifically fund software!

Getting Lost in Community Building - Matthew Turk

Choose your own adventure open source project game - funny

Inequality of Underrepresented Groups in Core Project Leadership - Anthony Scopatz

Another amusing choose your own adventure talk. I would note that the format actually works quite well to make a point about the speaker’s relationship to the topic, although the point gets a little lost in the fun a times.

Real World Numba: Creating a Skeleton Analysis Library - Juan Nunez-Iglesias

How fast can I make my code?! Note the use for actually drawing on paper in at a computer science conference!

How to Accelerate an Existing Codebase with Numba - Stanley Seibert

Similar concept to above, except taking a more pragmatic approach.

Lightning Talks

here() R function for Python - useful in Jupyter notebooks to find your data files

Day 3: July 12, 2019

Keynote: Jupyter: Always Open for Learning and Discovery - Carol Willing

Open source, open science - very thoughtful and moving talk. Book recommendation: Economics for the Common Good, Jean Tirole (or listen to the interview - link in slides) Papers: Building a Community of Open Source Contributors Ten Simple Rules for Reproducible Research in Jupyter Notebooks

Refactoring the SciPy Ecosystem for Heterogeneous Computing - Matthew Rocklin

cupy - run things on the GPU - uses the same interface to numpy and (most of) pandas. Thoughts about how we need standards interfaces to bring together multiple backends.

Turning HPC Systems into Interactive Data Analysis Platforms - A. Banihirwe

Microscopium: Interactive Exploration of Large Imaging Datasets - Genevieve Buckley

Building and Replicating Models of Visual Search Behavior with Tensorflow, Nengo, and the Scientific Python Stack - David Nicholson

Neuro science and machine learning. Can we teach a neural network to have the same biases as a human? Cool talk!

Lightning Talks