Seaborn Objects Tutorial: Powerful but Frustrating? (Python Data Visualization Deep Dive)
In my last video, I showed how elegant and simple plotnine makes the Grammar of Graphics in Python. This time, I put Seaborn’s new Objects API to the test — and the results were… mixed.
In this tutorial, I’ll walk you through:
• ✅ Building scatter plots, regression lines, categorical bars, and distribution plots with seaborn.objects
• ✅ Layering marks and statistics to create publication-ready figures
• ⚠️ Where things get tricky: legends, theming, faceting, and inconsistent defaults
• 🧪 Applying these lessons to real spectroscopic plastic data for science-ready plots
You’ll see both the power and the pain points of seaborn.objects. If you’ve ever wondered whether this API can replace plotnine or the classic seaborn functional calls, this video is for you.
📊 Libraries featured: seaborn.objects, matplotlib, pandas
💡 Takeaway: Objects brings flexibility and composability, but theming and layout still need polish.
👉 Drop a comment with the visualization libraries you want me to explore next!
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