3.7 Visualization that communicates
You can make a chart a non-technical person reads correctly in five seconds.
Before:01. Mathematics for Machine Learning02. Python — Basics to AdvancedUnlocks:05. Classical Machine Learning
A chart's job is to put one finding into a reader's head in seconds, and this topic covers both the mechanics — the matplotlib figure and axes model, seaborn, plotly — and the judgement: chart choice, colour, annotation. It sits in the data module because analysis unread is analysis undone. The ethics are practical: truncated axes and dual axes lie by accident, and the reader most misled is the one who trusted the chart enough not to check.
Work through these
matplotlib figure/axes mental model
The two-level structure of the plotting library, where a figure holds one or more sets of axes. Holding this model is what turns plotting from copied snippets into something you can control.
seaborn for statistical plots
A statistics-oriented plotting layer that produces informative defaults with far less code. It is the fastest route to a distribution or relationship plot.
plotly for interactive charts
Interactive charts where the reader can hover and zoom, which suits exploration and dashboards rather than print. Knowing when interactivity earns its place is part of the choice.
SQLBolt — Interactive SQL Lessons · TutorialChart choice, colour, annotation, and lying with axes
Choosing the chart from the question, using colour deliberately, annotating the point you want noticed, and refusing the axis tricks that exaggerate. The last one is an ethical matter as much as a technical one.
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