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Need to display a CSV in the browser without a backend? PyScript’s bundled Pandas stack lets you fetch, parse, and render a DataFrame as an interactive HTML table—all client‑side. Learn how to do it, the performance trade‑offs, and best‑practice tips.
Decide between Plotly's SVG scatter and WebGL scattergl traces: a compact comparison, the real trade-offs, a threshold-gated implementation, and how to validate with your own data.
Why the SVG <rect> element is often the sharpest, simplest tool for badges, chart bars, and outlines — with a worked example, crisp-edge fixes, and honest limits.
Plotly ships every figure as JSON to the browser, so the real architecture decision for large-data dashboards is where the rendering boundary sits. This note covers the smallest server-side aggregation design, trust boundaries, caching, operational checks, and failure modes.
Learn when and how to switch Plotly’s default SVG scatter trace to the WebGL‑accelerated scattergl type for smooth interaction with large datasets.
Build a robust D3.js render loop using selection.join() with keyed data joins. Covers enter/update/exit handling, transitions, and a reusable render(data) pattern that avoids common bugs.
Learn how to implement client-side point clustering in Mapbox GL JS to handle high-density datasets using GeoJSON sources, data-driven styling, and interactive zoom-to-expand functionality.
Matplotlib plot calls that run without errors but produce blank windows, empty PNGs, or missing lines — a diagnostic table, ordered checks, and fixes tied to each finding.
The D3 .join() method turns the fiddly enter-update-exit lifecycle into one declaration. Learn how keyed data joins keep dynamic SVG charts in sync, with a worked animated scatter plot example.
pandas
plotly
rect
d3js
javascript
mapbox
python
matplotlib