plot
gostat
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| plot | gostat | |
|---|---|---|
| 38 | - | |
| 3,836 | 32 | |
| 2.6% | - | |
| 9.1 | 0.0 | |
| 8 days ago | about 6 years ago | |
| HTML | Go | |
| ISC License | BSD 3-clause "New" or "Revised" License |
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plot
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Vega-Altair: Declarative Visualization in Python
I love Vega(-lite) / Altair, the grammar of graphics plotting system is really great to build any kind of chart even when it wasn't thought through by the authors of the library. There are other wrappers for languages that lack viz libraries, such as Elixir / Livebook [0]
However, when I used it a couples years back it struggled with large vizs, I think due to Vega(-lite)'s way of embedding the data in the viz artifact.
Also, interactive is nice but often I just need a quick static plot, and matplotlib is more convenient for this, you can easily see the png in any environment etc.
These days I'm eager to see an Observable Plot [1] wrapper for Python !
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Observable 2.0, a static site generator for data apps
Good questions.
1. It’s just JavaScript so you can fetch stuff dynamically too (see https://observablehq.com/framework/lib/duckdb). But yeah, only client-side. (Though see https://github.com/observablehq/framework/issues/234.)
2. Sure, it’s all open source, I bet you could make that work. Or `yarn deploy` to Observable and configure sharing there (though it wouldn’t let you charge others).
3. Yup. Which is part of the appeal of model of running data loaders at build time: you can query some private data and viewers would only be able to see the final result set. (The lack of something like this has always been a huge problem for Observable notebooks. You’d make some great query-driven charts and then couldn’t make it public without some awkward manual dance of downloading and re-uploading a file to a fork of the notebook.)
4. I wish I knew! It’s being tracked here https://github.com/observablehq/plot/issues/1711. Lately there’s been a lot more work on Framework naturally but now that that’s out…
5. Another good question. We’re definitely interested in tailoring it more to this sort of use case but lots is TBD!
We’re working on zooming and panning for Observable Plot (https://github.com/observablehq/plot/pull/1738) and other interactions such as brushing (https://github.com/observablehq/plot/pull/721) — all of this is already possible, we just haven’t packaged it up in a convenient way yet (https://github.com/observablehq/plot/pull/1871).
We’ve been focused primarily on the static display of visualizations because that’s what viewers see first, and often that’s often the only thing they see. Relying too heavily on interaction places an onus on the user to find the insights; a good display of data should be opinionated about what it shows and guide the user to what is interesting.
We’re not trying to convince you to switch to JavaScript here — a main value prop of Observable Framework is that you can write data loaders in any language (Python, R, Go, Julia, etc.). So do all your data preparation and analysis in whatever language you like, and then do your front-end in JavaScript to leverage the graphics and interactive compute capabilities of modern browsers. It’s pipes and child_process.spawn under the hood. And you still get instant reactivity when you save changes to your data loaders (when you edit Python) because Framework watches files and pushes new data to the client with reactive hot data & module replacement.
And you can compress (aggregate or filter) the data as much as you like, so it’s up to you how much data you send to the client. For example your data loader could be a minimal CSV file that’s just the numbers you need for a bar chart. Or it could be a Parquet file and you use DuckDB (https://observablehq.com/framework/lib/duckdb) on the client to generate dynamic visualizations.
Yep, Evidence is doing good work. We were most directly inspired by VitePress; we spent months rewriting both D3’s docs (https://d3js.org) and Observable Plot’s docs (https://observablehq.com/plot) in VitePress, and absolutely loved the experience. But we wanted a tool focused on data apps, dashboards, reports — observability and business intelligence use cases rather than documentation. Compared to Evidence, I’d say we’re trying to target data app developers more than data analysts; we offer a lot of power and expressiveness, and emphasize custom visualizations and interaction (leaning on Observable Plot or D3), as well as polyglot programming with data loaders written in any language (Python, R, not just SQL).
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Using Deno with Jupyter Notebook to build a data dashboard
Observable Plot: A library built on top of D3.js used to visualize data and iterate more quickly on different plot chart
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What website frameworks are used to build these websites?
https://observablehq.com/
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Yandex open sourced it's BI tool DataLens
Observable Plot [0] is also nice. AFAIU it's the same library powering the visualizations within Observable itself.
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Observable Plot: The JavaScript library for exploratory data visualization
I expect we’ll release tooltips in a few weeks; I linked that newer PR in another comment. We’ve been exploring interaction while working on many other things (https://github.com/observablehq/plot/blob/main/CHANGELOG.md). Now that we’re feeling good about the foundation for static charts we are starting to address interaction.
If you’re primarily interested in static charts (or your interactivity is driven by external controls, such as drop-downs and sliders, where you can regenerate the chart as needed for interaction), then Plot is ready for use. On the other hand if you need direct manipulation interaction, such as brushing or painting dots in a scatterplot, then you might stick with Vega-Lite for now.
We’re working on tooltips now, and interaction next. There’s a PR here with an early sketch and demo video: https://github.com/observablehq/plot/pull/721
It’s not easy to do radial stuff right now; you can upvote this issue: https://github.com/observablehq/plot/issues/133
One bizarre workaround right now is to abuse the geographic projections: https://observablehq.com/@observablehq/pie-to-donut-chart
In general, you can do a lot of customization, including plugins (though that’s not well-documented yet), so you could make it work… see https://observablehq.com/@fil/radial-transform, https://observablehq.com/@observablehq/plot-radial-line for other approaches to hacking radial stuff into Plot.
You can do Cartesian tree diagrams in Plot, e.g. https://observablehq.com/@observablehq/plot-tree-flare, https://observablehq.com/@observablehq/plot-custom-tree-layo...
If you want to make a chord diagram like that “Müsli Ingredient Network” example, you could try one of these D3 examples: https://observablehq.com/@d3/chord-diagram, https://observablehq.com/@d3/hierarchical-edge-bundling/2
gostat
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Tracking mentions began in Dec 2020.
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