Computational documents attempt to guarantee that results included within them—such as graphs—correspond to the code and data claimed to generate them. They typically achieve this by generating the outputs from the code at the time the document is generated or viewed. This solves significant problems, including those …
Continue readingPosted on Thu 14 November 2024 in TDDA • Tagged with TDDA, reproducibility, process
I have long considered writing a post about the various problems I see with computational notebooks such as Jupyter Notebooks. As part of a book I am writing on TDDA, I created four parables about good and bad development practices for analytical workflows. They were not intended to form this …
Continue readingPosted on Sun 21 July 2024 in TDDA • Tagged with TDDA, tutorial
PyData London had its tenth conference in 2024, and it was excellent.
I gave a tutorial on TDDA, and the video is available on YouTube and below:
The slides are also available here.
Continue readingPosted on Thu 20 June 2024 in TDDA • Tagged with TDDA, reproducibility, errors, interpretation
I was at the tenth PyData London Conference last weekend, which was excellent, as always. One of the keynote speakers was Rebecca Bilbro who gave a rather brilliant (and cleverly titled) talk called Mistakes Were Made: Data Science 10 Years In.
The title is, of course, a reference to the …
Continue readingPosted on Mon 04 March 2024 in TDDA • Tagged with TDDA, names
This is just a bit of fun, but I've always been interested in the different kinds of names allowed, encouraged, and used in different areas of computing and data.
A few years ago, I tweeted some well-known naming styles and a collection of lesser-known naming styles. I was playing about …
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