One of the great conveniences of performing a data science style analysis using Jupyter is that Jupyter notebooks are literate containers that combine code, text, results, and graphs. This is also one of the pain points in working with Jupyter notebooks with partners or with source control. That is: Jupyter […]
Introduction The data algebra is a Python system for designing data transformations that can be used in Pandas or SQL. The new 1.3.0 version introduces a lot of early checking and warnings to make designing data transforms more convenient and safer. An Example I’d like to demonstrate some of these […]
The data algebra is a system for specifying data transformations in Pandas or SQL databases. To use it, we advise checking out the README and introduction. These document what data operators are the basis of data algebra transformation construction and composition. I have now added a catalog of what expression […]
When working with multiple data tables we often need to know how for a given set of keys, how many instances of rows each table has. I would like to use such an example in Python as yet another introduction to the data algebra (an alternative to direct Pandas or […]
I’ve been tinkering a lot recently with the data_algebra, and just released version 0.7.0 to PyPi. In this note I’ll touch on what the data algebra is, what the new features are, and my plans going forward.
It looks like R is getting an official pipe operator (ref). R doesn’t work under an RFC process, so we hear about these things and they are discussed on the R-devel mailing list. I’ve written on this topic before (ref), and I have taped some new comments. This sort of […]
Nina and I are cleaning up websites, links, and projects. I would like to take the opportunity re-share my old genetic art project through a short demonstration video. Read more about the Genetic Art Project here.
I’d like to share a video of my old knot editing software in action. Read more about KnotEd here.
Win Vector LLC’s Dr. Nina Zumel has had great success applying y-aware methods to machine learning problems, and working out the detailed cross-validation methods needed to make y-aware procedures safe. I thought I would try our hand at y-aware neural net or deep learning methods here.
wrapr 2.0.0 is now up on CRAN. This means the := variant of unpack[] is now easy to install. Please give it a try!