In this post I’m going to build off the last post on Working with US Census Bureau Data and discuss how to visualize it. That post walked through working with the Census Bureau’s API, so in this post I’ll skip those details.

I found that the US Census API is difficult to work with and even LLMs don’t provide working code for it. So I thought it might be helpful to share some techniques that did work. In this post, I’m going to focus on both raw API calls and the Python wrapper.

Google Cloud authentication can be confusing. This post explains how Application Default Credentials (ADC) work and how to fix common authentication errors.

In this post, let’s visualize the internals of a transformer model. These visualizations reveal some interesting patterns that can help us understand how well the training is going.

The dis module is a great tool for understanding how code runs. While I mainly use it out of curiosity, it can also be valuable for optimization and debugging. The module allows you to translate your Python code into bytecode—a low-level, intermediate representation of your Python code. By examining bytecode, programmers can glimpse the Python interpreter’s view of their code, shedding light on performance characteristics and operational behaviors that aren’t apparent at the source code level.

YData Profiling used to be know as pandas-profiling, but it’s moved to a new name and new home. I talked about in my post on cleaning DNA splice junction data, but since it was kind of buried in the post and the name has changed, I thought I would do a quick tutorial that only covers YData Profiling. There isn’t much to demo here because it does so much of the work for you, but I’ll still go over it.

This tutorial shows how to plot geospatial data on a map of the US. There are lots of libraries that do all the hard work for you, so the key is just knowing that they exist and how to use them.