Focus is key to creating product value with software. In this article, I illustrate what happens if you do not focus, using a Python project as an example. I then conclude that product value is best created by being conscious about the outcome you are trying to achieve while prioritizing ruthlessly and choosing boring technology by default.
Every data analysis contains a plethora of assumptions that you need to test, either manually or explicitly as part of your workflow. The latter is usually the better approach, because explicit data tests enforces and documents your assumptions, while making your analysis repeatable. In this article, I show you how to explicitly test your data assumptions with PostgreSQL.
This post shows you how to calculate CAGR for a metric in Pandas, while handling edge cases that may appear in real-life data. The final code can be found here.