“Making Pandas Fly” at EuroPython 2020

I’ve had a chance to return to talking about High Performance Python at EuroPython 2020 after my first tutorial on this topic back in 2011 in Florence. Today I spoke on Making Pandas Fly with a focus on making Pandas run faster. This covered:

  • Categories and RAM-saving datatypes to make 100-500x speed-ups (well, some of the time) including dtype_diet
  • Dropping to NumPy to make things potentially 10x faster (thanks James Powell and his callgraph code)
  • Numba for compilation (another 10x!)
  • Dask for parallelisation (2-8x!)
  • and taking a view on Modin & Vaex

We might ask “why do this” and my answer is “let’s go faster using the tools we already know how to use”. Specifically – without investing time learning a new tool (e.g. Intel SDC, Vaex, Modin, Dask, Spark and more) we can extend our ability to work with larger datasets without leaving the comfort of Pandas so you can get to your answers quicker. This message went down well:

If you’re curious about this and want to go further you might want to look at my upcoming training courses (this includes Higher Performance, Software Engineering and Successful Data Science Projects). If you want tips and you want to stay on top of what I’m working on they join my twice-a-month mailing list (see the link for a recent example post).

 


Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.

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