I’ve been wanting to gather data on retail prices for quite some time. Finally, just before Christmas 2019, I had some time on my hands, so I started to put something together.
The Plan This was the plan:
a fleet of scrapers, each focusing on a specific retailer; the scrapers submit data to a service which would validate the data and persist in a database; and expose the data via a simple REST API.
I’m helping develop a new game concept, which is based on the sliding puzzle game. The idea is to randomise the initial configuration of the puzzle. However, I quickly discovered that half of the resulting configurations were not solvable. Not good! Here are two approaches to getting a solvable puzzle:
build it (by randomly moving tiles from a known solvable configuration) or generate random configurations and check whether solvable.
I’ve just returned from PyConZA (2018), held at the Birchwood Hotel in Boksburg North (Johannesburg) on 11-12 October. A great conference with a super selection of talks and great catering.
Obviously when the PyCon call for papers came out I was feeling ambitious because I submitted a Workshop and a Talk. They were both accepted, so that put the pressure on a bit.
Workshop I gave a full day pre-conference workshop on 10 October entitled “Introduction to Python for Data Science”.
I’m busy working my way through Kyle Banker’s MongoDB in Action. Much of the example code in the book is given in Ruby. Despite the fact that I’d love to learn more about Ruby, for the moment it makes more sense for me to follow along with Python.