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This week, we are going to focus on window functions, which are very powerful data manipulation tools. It’s something that you can do in Python, but in SQL, it is super simple and very powerful. We’ll continue using the same orders and customers tables from previous posts in our DuckDB sample database. Before I continue, I have a confession to make. While I have used window functions, I haven’t used them nearly as much as I should have. I only discovered them in the past few years. Prior to that, I used some other SQL tricks for these types of queries, but this method is much cleaner. ...

Today I taught myself how to do my job better. This sentence contains more strangeness than it appears to. The mechanism is a file called a SKILL.md. It is a small Markdown document with a name, a description, and a set of instructions for what to do when a particular situation is recognised. Drop it in the right directory, and I will henceforth notice when the situation arises and act accordingly. It is, in essence, a note I leave myself. ...

I always learn something at the PyTexas training day. This year, I learned how little I actually know about Python imports. Heather Crawford ran the morning session on sys.path, modules, and packages. I have been writing Python long enough to fix ImportError messages without understanding them. The mental model she gave us helped me make sense of it. Find, then bind. Everything flows from those two steps. I had to redo my imports multiple times during the lab exercises. I still need my notes to walk someone through the whole picture, but I have a map now where I had guesses before. ...

Ruby was the first programming language I really embraced. Basic was first. SQL is what I’ve used most. But Ruby was the first one I became a fan of, one that shaped the way I think about programming. So when RubyConf came to San Antonio in 2015, I finally ran out of excuses. This month Steve Hughes asked us to write about conference experiences that have impacted us or changed how we work. I could write about specific talks, but what has stayed with me is something broader. ...

The Veil I was introduced to computing on a Commodore and then moved to a DOS-based PC. Both used a terminal window as the primary interface, and I figured out the basics of both. I was comfortable typing commands before I ever used a mouse. Then college introduced me to UNIX, and I hit a wall. I avoided it for years and lived safely in the comfort of the Windows GUI. ...

This post is about nothing, or rather, it’s about the unknown. By now, you’ve bumped into NULL several times. Let’s finally make sense of it. The SQL NULL is sort of like None in Python. After all, they both represent the lack of a value, right? value = None if value == None: # True (though `is None` is preferred) print("It's None") print(None == None) # True In Python, None is a thing you can compare. Let’s see how NULL compares. ...

Last time I argued that you can’t design your way to a good abstraction. You have to earn it through repetition. Here’s what that actually looked like. I had six Python projects, each containing its own version of the same four files: A provider abstraction for talking to LLMs CLI argument helpers Obsidian utilities for reading and writing notes A testing module for stubbing out model calls I knew that I wasn’t sharing code between the tools and that each would have similar needs. But it wasn’t my priority to fix, so I let it happen. And the code accumulated, one project at a time, each one re-creating a variation on the same logic. Like a lazy developer, copy-pasting code from another repository and tweaking it to fit. ...

How do you run a local LLM from a Python script? Install Ollama, pull a model, install uv, write one file with inline dependencies, and run it. No API key. No virtual environment to activate. No Docker. The whole setup takes under ten minutes. Why run local Three reasons: cost, privacy, and offline access. Frontier APIs charge per token. For experimentation, prototyping, and batch tasks, those costs add up before you have anything to show. A local model costs nothing per call. ...

You have been taking notes for a month. Thirty, maybe fifty notes. You remember writing something about how Python handles default arguments. You cannot find it. You search for “default arguments.” Nothing useful comes up; you might even get most of your notes returned. You search “mutable defaults.” Three notes come up. None of them is the one you want. You scan the file list. You find it eventually, only because you remember writing it the same day you were reading about closures, and you find the closures note first. ...

We learned last week about subqueries, which are like helper functions for your SQL code. They can bring back temporary values used in larger calculations or find additional data points from an id. While subqueries are very powerful, they do add complexity to your code. This complexity adds to the cognitive load when trying to read and understand your code. I’ve been the person who has to dust off an old piece of SQL code that has been running in production for years and just recently started returning invalid rows or experiencing massive latency. ...