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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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buckaroo
Buckaroo - The data table UI for Notebooks. Quickly explore dataframes, scroll through dataframes, search, sort, view summary stats and histograms. Works with Pandas, Polars, Jupyter, Marimo, VSCode Notebooks
Great tables has done some really nice work on python/jupyter tables. It looks like they are almost building a "grammar of tables" similar to a grammar of graphics. More projects should write about their philosophy and aims like this.
I have built a different table library for jupyter called buckaroo. My approach has been different. Buckaroo aims to allow you to interactively cycle through different formats and post-processing functions to quickly glean important insights from a table while working interactively. I took the view that I type the same commands over and over to perform rudimentary exploratory data analysis, those commands and insights should be built into a table.
Great tables seems built so that you can manually format a table for presentation.
https://github.com/paddymul/buckaroo
https://youtu.be/GPl6_9n31NE
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mito
Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet
2. The report you're sending out for display is _expected_ in an Excel format. The two main reasons for this are just organizational momentum, or that you want to let the receiver conduct additional ad-hoc analysis (Excel is best for this in almost every org).
The way we've sliced this problem space is by improving the interfaces that users can use to export formatting to Excel. You can see some of our (open-core) code here [2]. TL;DR: Mito gives you an interface in Jupyter that looks like a spreadsheet, where you can apply formatting like Excel (number formatting, conditional formatting, color formatting) - and then Mito automatically generates code that exports this formatting to an Excel. This is one of our more compelling enterprise features, for decision makers that work with non-expert Python programmers - getting formatting into Excel is a big hassle.
[1] https://trymito.io
[2] https://github.com/mito-ds/mito/blob/dev/mitosheet/mitosheet...
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perspective
A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
Why do you want to render to canvas?
Perspective seems to be the most performant html table. It is more focused on extremely fast updates than styling, although it looks good.
Glide is a newcomer that also renders to canvas.
https://github.com/finos/perspective
https://github.com/glideapps/glide-data-grid
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Glide Data Grid
🚀 Glide Data Grid is a no compromise, outrageously react fast data grid with rich rendering, first class accessibility, and full TypeScript support.
Why do you want to render to canvas?
Perspective seems to be the most performant html table. It is more focused on extremely fast updates than styling, although it looks good.
Glide is a newcomer that also renders to canvas.
https://github.com/finos/perspective
https://github.com/glideapps/glide-data-grid
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How does this compare to https://github.com/jieter/django-tables2? That one makes it really easy to display database models with column sorting and pagination.
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If anyone is looking for a great open source table, I recommend slick grid: https://github.com/6pac/SlickGrid it has all the features shown in the article and has proven itself capable of everything I've needed for the last 2+ years.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
