Archives of Data science

Weekish notes

I’ve recently switched back from Sourdough yeast to dried packet yeast mix, given a recipe by a colleague (thanks Nick!). I immediately set to work modifying his recipe (well, cutting out steps if we’re honest). The first loaf looked fine but was bland – I cut out too much salt. The next was really very […]

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. […]