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Use Plotly make_subplots to view related charts side by side with linked axes, independent scales, and safe defaults for dense data.
Add error bars to Plotly.js scatter plots using the error_y property with array data for symmetric errors or plus/minus arrays for asymmetric uncertainties. Includes worked example, styling options, performance limits, and verification steps.
Learn how D3.js’ enter‑update‑exit pattern keeps bar charts in sync by adding, updating, and removing only the changed SVG elements.
Generate publication-ready PNG, SVG, PDF, or JPEG files from Plotly figures using the Kaleido engine—no browser required. Covers installation, configuration, common rendering issues, and CI-friendly verification.
Stop the cycle of manual cell re-execution. Learn how to use ipywidgets to create dynamic, interactive data exploration tools directly within your Jupyter Notebooks.
Learn how to use the Chart.js Decimation plugin to optimize the rendering of massive time-series datasets without losing critical visual outliers.
Plotly Express gets you to an interactive chart fast, but custom layouts and large datasets often require dropping into Graph Objects. Here is how to decide when to switch.
Stop reloading GeoJSON files. Learn how to use Mapbox GL JS sources, layers, and data-driven expressions to implement instant filtering and styling on the GPU.
Stop recreating your D3 charts from scratch on every data update. Learn how to use the General Update Pattern and .join() to create smooth, data-driven SVG transitions.
python
plotly
d3js
chartjs
javascript
mapbox