Python Data Visualization Libraries: Which One and When

Published: 06 August 2026
on channel: Nishat Sarker

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Choosing a Python data visualization library takes about ten seconds if you ask the right question.

Does it go in a paper? → Matplotlib or Seaborn
Does someone need to click it? → Plotly or Bokeh
Do they need to filter it themselves? → Dash
Are you still exploring? → PyGWalker

Everything after that is preference, and preference is allowed.

New on Multiome Academy: a 30-slide video covering 10 Python visualization libraries — Matplotlib, Seaborn, Plotnine, Plotly, Bokeh, Altair, Pygal, Geoplotlib, Dash and PyGWalker — with a code example and a rendered figure for each.

Every example uses single-cell data. UMAP embeddings, violin plots across cortical cell types, dot plots of marker genes, spatial spot coordinates.

Interleaved with the libraries are the principles that decide whether the output is publishable at all:

• Position beats area. Always.
• Perceptually uniform colormaps aren't a style choice.
• Anchor your diverging midpoint at zero, or the colour centre tells the story instead of the data.
• vmin just above zero, vmax at the 99th percentile, absence in grey.
• Shared scales are what make a faceted comparison valid.

Which library do you default to?

#Bioinformatics #Python #DataVisualization #scRNAseq #ComputationalBiology

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