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0 pointspea4y ago0 comments

If you can write visualisations in Python itself, I am a big fan of Altair's syntax (https://github.com/altair-viz/altair), which is based on vega-lite. A while back, I wrote a brief guide and comparison of the main plotting libraries: https://datapane.com/reports/87NNEJ7/the-ultimate-guide-to-p...

One benefit of having them in actual code is that you can programmatically automate the creation of things like dashboards and reports. For instance, schedule a script to share an interactive plot every Monday morning, or build a live dashboard that updates every 10m. This opens up a lot of possibilities that would be impossible in a traditional drag-and-drop tool.

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aarondia4y ago

> programmatically automate the creation of things like dashboards and reports.

That's an awesome use case for Python, and that sort of script generation is one of the main reasons that we see people adopting Python/Mito. And specifically, graphing[1] is one of the most popular features in Mito.

Mito generates Plotly [2] graphs, and of course generates the Plotly graph code too, so you can customize the graphs to your perfect liking (Plotly has great documentation and a lot of customizations) or schedule the script to run automatically.

[1] https://docs.trymito.io/how-to/graphing [2] https://plotly.com/

kite_and_code4y ago

Thanks for mentioning Altair. I am personally also a big fan.

I am one of the co-founders of bamboolib and we are actively thinking about adding support for altair to the Plot Creator (instead of just relying on Plotly).

Since we are talking other viz options in Python, there are of course also matplotlib, seaborn, plotly, and more.

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