The DataFrame class (from Pandas) is a work of art. Even if you never "do data", priceless lessons can be gleaned by studying this class. It starts simple enough. Usually you will create a DataFrame by ingesting from a CSV file or database table or something. But you can whip up a small one like this: import pandas as pd df = pd.DataFrame({ 'A': [-137, 22, -3, 4, 5], 'B': [10, 11,
AI coding tools are starting to look similar on the surface: they all offer chat, agents, code edits, terminal awareness, and some form of autocomplete. But the real differences are in the workflow. The question is less “which one has AI?” and more “where does the AI live in your development process?” For me, VS Code is still the baseline. It is flexible, extensible, familiar, and easy to compose
When we talk about Data Visualization and Dashboards, enterprise tools like Tableau or PowerBI often dominate the conversation. However, for Data Scientists and Developers, these GUI-based tools can feel restrictive. What if you need complex machine learning integration, custom UI logic, or automated CI/CD deployments? Enter the holy trinity of Python visualization tools: Streamlit, Dash, and Boke
[05] When to Pull the Trigger on FIRE — Monte Carlo Says You're Already Free This is Part 5 of a 6-part series: Building Investment Systems with Python "You need 25x your annual expenses." That's the standard FIRE rule. For ¥9.6M annual expenses, that's ¥240M. Most people see that number and think: "I'll never get there." But the 25x rule assumes a fixed 4% withdrawal rate, zero income, zero ada