Stephen Macke
I'm an engineer at Databricks where I work on tools and infrastructure for machine learning and data science. I'm passionate about pushing the limits of Python for data science use cases, and would love to chat with other tool developers to learn about the exciting developments in this area. In my free time, besides maintaining a few open source projects, I enjoy spending time with my wife and our cat in our vegetable garden.
Sessions
Did you know that the core Python syntax and semantics can be tailored for interactive computing use cases? It turns out that more is possible than what you would expect! For example, at the most basic level, Jupyter supports basic syntax extensions like so-called "magic" operations. It turns out, however, that one can go much deeper. In this talk, I'll show that it's possible to augment and abuse Python to support a plethora of interactive use cases. I'll start with the simple example of building an optional chainer for Python (supporting syntax reminiscent of javascript like a?.b()?.c). I'll then show how to use these same ideas to accelerate data science operations, concluding with an example of how to perform full dataflow tracking in order to give users the illusion of dataframe queries that run instantaneously.