examples
fastpages
examples | fastpages | |
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2 | 8 | |
17 | 3,418 | |
- | - | |
10.0 | 4.3 | |
8 months ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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.
examples
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Datapane - Build internal data products in 100% Python
Next, we added the ability to build data web apps by connecting Python functions to forms and front-end events. We handle the entire network and RPC layer, so you only need to write plain Python functions that take parameters and return other blocks. You can check out an example of the code to create a simple app: https://github.com/datapane/examples/blob/main/apps/iris-plotter/app.py
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Show HN: Datapane – A new way to build reports, dashboards, and apps in Python
Hello HN! We’re Leo and Mandeep, founders of Datapane.
We're building a way to create reports, dashboards, and web apps from your existing data using Python. Think of it as a combination of React and htmx, specifically designed for the Python data stack.
Our GitHub is https://github.com/datapane/datapane and you can try building a report or app in ~2 minutes on Codespaces: https://try.datapane.com
We started building Datapane at our previous start-up, where we struggled to deliver ML model results to clients. Much to our surprise, the data science took less time than repeatedly creating reports by copying and pasting plots into PowerPoint decks.
It seemed absurd that we had to switch to PowerPoint or legacy BI tools like Tableau to share, and our initial goal was to programmatically generate reports using the datasets and plots we had in Python. To enable this, we started hacking on a Python-based UI framework for constructing HTML views from data-centric blocks – like plots, data tables, and layout components.
You can export these to standalone HTML files, or host them as a web app on somewhere like GitHub Pages or Fly.io. We recently also added the ability to connect Python functions to forms and front-end events so you can build web apps which run backend code. We handle the entire network and RPC layer, so you only need to write plain Python functions that take parameters and return other blocks.
You can check out an example of the code to create a simple app: https://github.com/datapane/examples/blob/main/apps/iris-plotter/app.py
Datapane’s philosophy is pretty different from other products in the space.
We wanted to keep things simple, but avoid the footguns our users faced with frameworks like Streamlit, where the reactive/network-aware model was hard to move beyond an MVP or POC. For backend interactivity, we believe the original web got a lot right, and unlike reactive models which rely on websockets, Datapane is unashamedly request/response. This takes inspiration from HTTP and our own experiences with htmx, which offers an elegant way to add interactivity to HTML. Under the hood, we actually compile down to a (gasp!) XML-based hypermedia format, akin to HTML, but tailored specifically for constructing data UIs.
The result is that not every change in your app requires a server round trip, as much of it can be pre rendered and most interactivity happens on the client-side. In addition to improving performance, this also makes running in production become 10x simpler.
This separation between the view and backend compute also makes Datapane modular. If our app server isn’t a good fit for your use-case, serve Datapane views from the web-framework of your choice (we’ve been hacking on serving views from Django). Want to compute blocks from inside Airflow or generate them on a schedule or from a webhook? Computation can happen out of band of the UI. You can even build and host apps from inside of Jupyter, where you can preview blocks live and convert notebook cells to blocks in your view.
We currently offer a hosting platform on https://datapane.com for sharing reports publicly (free) or with your team (paid), and will be adding serverless app hosting support to it in the next few weeks.
Our ultimate goal is to create an open-source toolkit for building data products across the entire stack – from reports, to dashboards, to full-stack apps – all using 100% Python. You can see a few we’ve built already in our gallery: https://datapane.com/gallery
We’d love to hear your feedback.
Thanks!
fastpages
- [P] I Made An Easy-To-Use Python Package That Creates Beautiful Html Reports From Jupyter Notebooks
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[D] What do you use to make your blog/personal websites?
I use FastPages by fastai community: https://fastpages.fast.ai/ It's similar to Markdown in terms of writing the blog and you can even convert your Jupyter Notebook to a blog page directly.
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Ask HN: Share Your Personal Site
My blog isn't all that amazing visually, but I'll use this to recommend fast pages. If you know a bit of coding and your goal is to write and not tinker, it's the perfect tool.
[1] www.adithyabalaji.com
[2] https://github.com/fastai/fastpages
- Utterances – a lightweight comments widget built on GitHub issues
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A personal website portfolio - yay or nay?
Fastpage blog
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Show HN: Render Jupyter notebooks as interactive articles with Deepnote
Thanks for mentioning nbdev (which as mentioned works well with DeepNote).
FYI, the blog post you linked to is a bit out of date - we have something much better for blogging with jupyter notebooks nowadays, which is fastpages: https://fastpages.fast.ai/ . It's compatible with the same annotations used in nbdev.
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Ask HN: Convert Jupyter Notebook into Static Site Blog Post?
Have you looked into https://github.com/fastai/fastpages
From the readme:
>An easy to use blogging platform, with support for Jupyter notebooks, Word docs, and Markdown.
There's also that: https://www.scottcondron.com/jupyter/blogging/visualisation/...
Found by searching for "jupyter notebook to blog"
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Simple website framework to show data science work
Try fastpages from fastai. You can create blogs from your notebook https://github.com/fastai/fastpages
What are some alternatives?
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
pydna - Clone with Python! Data structures for double stranded DNA & simulation of homologous recombination, Gibson assembly, cut & paste cloning.
python-training - Python training for business analysts and traders
gitlab
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
utterances - :crystal_ball: A lightweight comments widget built on GitHub issues
datapane - Build and share data reports in 100% Python
BestPractices - Things that you should (and should not) do in your Materials Informatics research.
ppde642 - USC urban data science course series with Python and Jupyter
git-bug - Distributed, offline-first bug tracker embedded in git, with bridges
gutenberg - A fast static site generator in a single binary with everything built-in. https://www.getzola.org
fastbook - The fastai book, published as Jupyter Notebooks