plot
mercury
plot | mercury | |
---|---|---|
40 | 78 | |
3,951 | 3,800 | |
3.4% | 1.5% | |
8.8 | 8.5 | |
28 days ago | 11 days ago | |
HTML | Python | |
ISC License | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
plot
- Ask HN: What's the best charting library for customer-facing dashboards?
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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 !
[0] https://github.com/livebook-dev/vega_lite
[1] https://github.com/observablehq/plot
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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!
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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.
[0] https://observablehq.com/plot/
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Best React charting libraries for data visualizations
I liked observablehq plot library: https://github.com/observablehq/plot
- Bank Failures Visualized
- Observable Plot: A JavaScript library for exploratory data visualization
mercury
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Ask HN: What's the best charting library for customer-facing dashboards?
I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.
My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/
disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury
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mercury VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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Show HN: Web App with GUI for AutoML on Tabular Data
Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
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streamlit VS mercury - a user suggested alternative
2 projects | 8 Jul 2023
- GitHub - mljar/mercury: Convert Jupyter Notebooks to Web Apps
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[P] Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
The GitHub repository https://github.com/mljar/mercury
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Show HN: Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.
It is very opinionated:
- it has no callbacks - we automatically re-execute cells below updated widget
- it has no layout widgets, all input widgets are always in the left sidebar
Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.
The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.
The GitHub repository https://github.com/mljar/mercury
Documentation https://RunMercury.com/docs/
Mercury Cloud https://cloud.runmercury.com
- Show HN: Build Web Apps in Jupyter Notebook with Python Only
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[OC] Analyzing 15,963 Job Listings to Uncover the Top Skills for Data Analysts (update)
Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.
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[OC] Data Analyst Skills in need based on 15,963 job listings
Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.
What are some alternatives?
plot-react - React wrapper for @observablehq/plot
streamlit - Streamlit — A faster way to build and share data apps.
blazor-samples - Explore and learn Syncfusion Blazor components using large collection of demos, example applications and tutorial samples
voila - Voilà turns Jupyter notebooks into standalone web applications
echarts - Apache ECharts is a powerful, interactive charting and data visualization library for browser
papermill - 📚 Parameterize, execute, and analyze notebooks
go-echarts - 🎨 The adorable charts library for Golang
voila-gridstack - Dashboard template for Voilà based on GridStackJS
d3 - Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
gonum - Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more
awesome-streamlit - The purpose of this project is to share knowledge on how awesome Streamlit is and can be