Personal technical and software engineering knowledge.
Exploratory Data Analysis Core Guidelines
Jupyter Notebooks Jupy.0. Good notebooks use data to tell a story and comments need to concisely and tastefully facilitate the storytelling. Jupy.1. Persist notebooks in a format that is amenable to version-control, executable, and well-documented. Export to HTML or PDF as desired. As of 2026, jupytext works well for this. Reason: Version control and well-documented formats enable reproducibility and repeatability of analysis. With jupytext, some guidelines enable all analytical notebooks to be executable and immediately reproducible. ...
Python Core Guidelines
This guide provides ideals for production-grade Python code, code that humans are likely to read, code that will likely be edited and/or re-executed at least once, and code that I will review in detail. Code whose correctness, reproducibility, and reliability matter should follow these guidelines. This guide partly exists because Python is so flexible. There are enough ways of using Python to warrant a clean separation between “scripts that will be executed once, never reviewed by another person, or used again” from “services or ‘important’ tasks whose correctness matters and for which I might be called in to debug at midnight.” ...