Python

This is Part 3 of a series on modern development workflows. Part 1: The Strategic Value of Thinking in Notebooks and Part 2: The Technical Guide to Jupyter Setup set the stage. Now, let’s look at actual code. In the previous parts, we discussed why Jupyter is a “thinking environment.” In this final part, we’ll walk through four concrete scenarios where a notebook outperforms a traditional IDE for a senior engineer. 1. API Archaeology: Mapping the Unknown When you’re dealing with a complex API, you don’t want to build a full client just to see what the data looks like. ...

This is Part 2 of a three-part series. In Part 1: The Strategic Value of Thinking in Notebooks, we discussed why and when to use Jupyter. Here, we dive into the technical implementation. Part 3: Real-World Code Examples covers practical use cases. The Modern Jupyter Stack For a software engineer, the “standard” way of installing Jupyter (global pip install) is often the wrong way. It leads to dependency hell and “it works on my machine” syndrome. ...