- Understanding when categories beat strings and smaller floats beat larger ones
- What’s happening with NumPy behind the scenes
- How we can save 50% of our RAM (and so fit in more data to the same machine) by checking dtypes with my dtype_diet tool
- Considering that float16 is simulated on modern hardware and so is memory efficient but slow for calculating!
- Tips to install bottleneck & numexpr to make Pandas faster
- Digging into some Pandas internals when I filed a bug – and what I learned as a result (you can learn too by reading the bug report!)
In a few months I’ll run another of my Higher Performance Python virtual training classes, you’re most welcome to join. You’ll find details on my very-lightly-used “training email list“, you should join this if you’d like to hear about my upcoming training courses.
I make notes on some of these topics in my irregular “weekish notes” here on the blog and in my every-2-weeks “thoughts & jobs” email list. You’re welcome to join the list (your email is always kept private) if getting it in your inbox is more convenient.
At the end of my talks I always ask for a postcard “if you learned something”, I’ve just received the first for last week’s talk from the Netherlands – thanks!
Thanking Henry for sending through a lovely postcard ("enthusiastic talk, very valuable insights, best presentation" – humbled!) from the Netherlands after my talk @pydataamsterdam pic.twitter.com/jPwKU880Nz
— Ian Ozsvald (@ianozsvald) June 23, 2020
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.