Season 1 · Episode 18
9 Hidden Data Visualization Tricks to Transform Your Visuals using Plotly library in Python
Data & AI with Mukundan | Learn AI by Building · Mukundan Sankar – Practical AI & Analytics
December 20, 202410m 22s
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Show Notes
Key Takeaways:
1. Why Plotly is a Game-Changer
- Unlike Matplotlib or Seaborn, Plotly offers interactive and dynamic visualizations that are perfect for storytelling.
- Unlock powerful features that go beyond basic bar charts or scatter plots.
2. 9 Hidden Plotly Tricks:
- Custom Pairwise Correlation Matrix: Add annotations and custom color scales for deeper insights.
- Dynamic Data Highlighting: Like Excel, conditional formatting but on steroids.
- Density Contours: Visualize class distribution and clustering with ease.
- Faceted Histograms: Compare subgroups in a single view.
- Threshold Lines: Emphasize decision boundaries effectively.
- Custom Annotations: Turn visuals into storytelling tools.
- 3D Scatter Plots: Explore invisible relationships in 3D.
- Animated Visualizations: Reveal dynamic patterns over time.
- Interactive Tooltips: Make charts engaging and informative.
3. Real-world Applications
- Business intelligence, scientific research, and education examples.
- Techniques aren’t just about aesthetics—they’re about actionable insights.
4. Bonus Resources
Complete code examples are in the links below:
- Medium Members: https://medium.com/towards-artificial-intelligence/9-hidden-plotly-tricks-every-data-scientist-needs-to-know-eb7f2181df56
- Non-Medium Members can read for Free here: https://mukundansankar.substack.com/p/9-hidden-plotly-tricks-every-data
- Datasets from the UCI Machine Learning Repository for hands-on practice.https://archive.ics.uci.edu/datasets
- Twitter: @sankarmukund475