Archives of Data science

Weekish notes

I gave another iteration of my Making Pandas Fly talk sequence for PyDataAmsterdam recently and received some lovely postcards from attendees as a result. I’ve also had time to list new iterations of my training courses for Higher Performance Python (October) and Software Engineering for Data Scientists (September), both will run virtually via Zoom & […]

“Making Pandas Fly” for PyDataAmsterdam 2020

I thank the PyDataAmsterdam 2020 organisers for another chance to speak on Making Pandas Fly (PyDataAmsterdam 2020). This variant of the talk focuses more on: 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 […]

Weeknote (dtype-diet)

Over the weekend I hacked on dtype_diet – a tool for Pandas users that checks their DataFrame to see if smaller datatypes might be applicable. If so they’d offer no data loss and a reduction in RAM, for Categorical data there’s also the possibility of faster calculations. This tool makes no changes, it recommends the […]

Week(ish) note

So – High Performance Python 2nd ed finally shipped (Amazon, Goodreads) – yay! In brief we’ve added notes on how you can be a “highly performant programmer”, added some more profiling, added Pandas onto NumPy, improved the Compiling to C chapter with more Numba and a new full section on GPUs (in the first edition […]

Notes on last week’s Higher Performance Python class

Last week I ran a two-morning Higher Performance Python class, we covered: Profiling slow code (using a 2D particle infection model in an interactive Jupyter Notebook) with line_profiler & PySpy Vectorising code with NumPy vs running the original with PyPy Moving to Numba to make iterative and vectorised NumPy really fast (with up to a […]

Notes from Zoom call on “Problems & Solutions for Data Science Remote Work”

On Friday I held an open Zoom call to discuss the problems and solutions posed by remote work for data scientists. I put this together as I’ve observed from my teaching cohorts and from conversation with colleagues that for anyone “suddenly working remotely” the process has typically not been smooth. I invited folk to join […]

Another Successful Data Science Projects course completed

A week back I ran the 4th iteration of my 1 day Successful Data Science Projects course. We covered: How to write a Project Specification including a strong Definition of Done How to derisk a new dataset quickly using Pandas Profiling, Seaborn and dabl Building interactive data tools using Altair to identify trends and outliers […]

Higher Performance Python (ODSC 2019)

Building on PyDataCambridge last week I had the additional pleasure of talking on Higher Performance Python at ODSC 2019 yesterday. I had a brilliant room of 300 Pythonic data scientists at all levels who asked an interesting array of questions: This talk expanded on last week’s version at PyDataCambridge as I had some more time. […]

“Higher Performance Python” at PyDataCambridge 2019

I’ve had the pleasure of speaking at the first PyDataCambridge conference (2019), this is the second PyData conference in the UK after PyDataLondon (which colleagues and I co-founded 6 years back). I’m super proud to see PyData spread to 6 regional meetups and now 2 UK conferences. We had over 200 attendees and the conference […]