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Looks like a Datasette[0] clone which runs on top of something (jupyter) which runs on top of Python (ipython). I think I would like to see how much time it takes to open a massive dataset in Mito & in Datasette :P
[0]: https://datasette.io/
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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Mito is open source, but using Pro features does actually require a Pro or enterprise license. You can check out this callout in the license [1], as well as the restrictions on Mito Pro features here [2]. We're in the process of fixing up the upgrade to Pro process a bit... as you can tell... :)
You can of course fork Mito and turn off telemetry as long as you open source your changes! Go for it - happy to hop on a call and help you get set up with the codebase, if you want. Yay open source!
[1] https://github.com/mito-ds/monorepo/blob/974091b455950c6c50e...
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I played around with many of these before:
https://github.com/quantopian/qgrid
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https://github.com/man-group/dtale
I find that I'm actually a lot faster using basic Pandas methods to get the data I want in exactly the form I want it.
If I really want to show everything, I just use:
'''
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pandas-profiling
Discontinued Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling] (by pandas-profiling)
For those who are going through the thread finding new tools: pandas-profiling[0] is a library for automatic EDA (part of what bamboolib[1] does).
[0]: https://github.com/pandas-profiling/pandas-profiling
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One cool library I saw recently for helping on the visualisation side is https://github.com/vegafusion/vegafusion
It allows you to use Altair in Python for visualising data, but does the computation in the backend using Arrow DataFusion. Not for 15GB perhaps, but cool nonetheless.
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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.