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Stop manually updating layer styles. Learn how to use Mapbox GL JS expressions to build high-performance, data-driven choropleth maps with GPU-accelerated interpolations.
Plotly's frame-based animation API adds a playable slider to any chart with a few lines of Python. Here's a worked example, plus the file-size trade-offs that decide when it's the right tool.
Plotly’s scattergl renderer lets you plot massive point clouds with minimal lag. This guide walks through prerequisites, setup, performance checks, and fallback strategies for older browsers or low‑end GPUs.
Chart.js preserves a 2:1 canvas ratio by default, so charts overflow wide cards or float in whitespace. The two-option fix, the container-height rule, and when to keep the default.
Add smooth zoom and pan to D3 visualizations by mastering d3.zoom, scale/translate extents, and performance tricks. Learn how to keep large‑dataset charts responsive and avoid common pitfalls.
Hand-tuning subplot margins in Matplotlib is a guess-and-check loop you can skip. tight_layout() measures your labels and computes spacing automatically — here's how to use it, tune it, and recognize its limits.
Learn when to use SVG vs WebGL rendering in Plotly. This guide covers performance thresholds, implementation via render_mode, and how to diagnose rendering bottlenecks.
Chart.js's built-in decimation plugin cuts rendered points from 50k to ~500 using LTTB, preserving visual trends while restoring 60fps interaction. Requires 'index' scale and sorted data; tooltips need a callback for original values.
Learn how to correctly use the .update() method in Chart.js to handle dynamic data streams, avoid memory leaks, and optimize rendering performance using update modes.
mapbox
python
plotly
chartjs
javascript
d3js
matplotlib