Next week we have our first PyDataLondon meetup in a couple of years! It’ll comprise a set of lightning talks to get folk ready for the post-meetup discussion. The venue is the same as we used to have (thanks to the Man Group!). Sadly I can’t make it, I hope to be along to the next.
A month later we have our PyDataLondon conference. I’ll be talking on “Successful Data Science Projects” (aka a list of some select past failures, with lessons learned, plus a happy ending) and running another “Executives at PyData” session aimed at leaders. If you’ve got a ticket for the conference you can attend both, I’d love to say hi in person.
I’ve been thinking a lot about past project failures in my consulting - so much of it comes down to human factors (not “algorithmic issues”) and I’m keen to hear more from attendees. Figuring out how to communicate these issues and to bake them into project plans to lessen their impact seems critical to getting more success for data teams (IMHO).
Eric Drass, artist and former keynote speaker at PyDataLondon has a new video out using the Flickr Faces dataset and latent space manipulation to create a mesmerising auto-morphed video for Frank&Beans. Skip 30s in for the morphing to really kick in. I follow hip-hop (anyone else attend the DMC world championships each year?) and this feels like a visual-scratch, I wonder if we’ll see such things for turntablists down the line. Eric describes the base
FFHQ StyleGAN2 model and building up “[a] series of non-existent human faces from the mind of the GAN – interpolated to the tempo of the track.”. Lovely stuff.
Very soon I’m going to list new course dates for the coming months for my Higher Performance Python, Software Engineering for Data Scientists and Successful Data Science Projects courses. Reply to this if you’d like a notification, I hope to get the events listed next week.
On the morning of Monday 13th I’ll host an informal Zoom-based demo and discussion around Pandas performance tips I’ve developed (from running recent versions of my course). If you’d like to watch along, or discuss your own Pandas annoyances and tips, I’d love to have you along. Reply to this and I’ll add you to the calendar invite. We’ll look a little at faster
groupbys and row block accessing in this call. In particular I’ll be asking you about your pains using Pandas (and I’ll try to answer your questions) as prep for my Pandas course later this year. Hopefully between those of us on the call we can answer the questions you bring. Reply for a calendar invite.
In part I’ve been surprised by the performance difference between
query and making a mask for multi-item access and how sorting (or not) can have an impact.
During my last Higher Performance Python course we got into a discussion about how Numba can make
numpy vectorised operations even faster by fusing intermediate results together. E.g.
a = np.arange(...); b = a+2; c = np.sqrt(b) never requires
b to be instantiated - instead you could build the array of numbers and
sqrt as you go, removing the need to allocate the RAM for the
b intermediate - this saves RAM and time. I love showing in the course that compiling well-vectorised
numpy code can lead to further performance improvements.
One of my attendees (thanks!) shared some Numba links for this, you can read up on Numba typed IR rewriting. There’s also a case study on array expressions which gets quite dense. I’ve got code that shows that multiple use of an array (e.g. twice in the same expression) seems to foil the array detection and fusion process, but that’ll require a fresh brain to dig into.
I got into a tweet discussion with Harry and Nick about the RAM implications for using a list comprehension and generator expression (and then went down a rabbit hole with Numba). For this trivial example you ended up using less RAM by running a regular
for loop (which is normally my preference - explicit and easy to read), gain speed by trading RAM with a generator expression and trade more RAM by using a list comprehension. I was quite surprised by the list comprehension result, my ipython_memory_usage tool showed 1.2GB RAM usage during that cell, about +700MB on just doing a
for loop for the same answer. Do you have any insights?
James Powell asks on Twitter “If you were to reïmplement pandas from scratch, would you preserve the
DataFrame distinction? What about the
MultiIndex distinction?”, you might want to see the thoughts from a few others.
If you use
Dask and you dig into the
.divisions attribute to understand whether you can get fast index-based accesses or not, be aware that you have to set
calculate_partitions now by hand whereas it used to be on-by-default. This cost me a couple of hours debugging prior to running my Higher Performance Python course last week.
I see that a new release candidate was available for
scikit learn 1.1 a few weeks back, see the changelog for details, it looks like there’s a lot of small speed improvements in with the usual set of bug fixes.
If you find this useful please share this issue or archive on Twitter, LinkedIn and your communities - the more folk I get here sharing interesting tips, the more I can share back to you.
About Ian Ozsvald - author of High Performance Python (2nd edition), trainer for Higher Performance Python, Successful Data Science Projects and Software Engineering for Data Scientists, team coach and strategic advisor. I’m also on twitter, LinkedIn and GitHub.
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