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Comment by rjtavares | Hacker News Reader

Can't wait to see what people come up with using the new widgets capabilities. Here's a demo of Excel-like grid editing for Pandas: http://nbviewer.ipython.org/gist/rossant/9463955

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Yes! This is precisely the use case I have been waiting for. I just used days to implement a simple bar chart and a map visualization in Notebook. In IPython 1.0 this requires matplotlib know-how, the whole Python GIS library system to get shapefiles into Python format, not to mention changing projections and such to our somewhat peculiar needs.

Now I can just create widgets from my D3 visualizations, connect the Pandas dataframe and it is done. Also, I can continue to refine my standalone D3 visualizations and the widgets will benefit. All of this I used to do by hand, in a separate multistage process, I can now do directly and interactively.

Getting a real table view from Dataframe is the missing link. Now to search for the JS table plugin that can handle multi-index, hierarchical tables...

I'd love a drop-down filter mechanism on pandas similar to the one Excel has. That's the one thing that makes me open Excel when I'm working on a new dataset on python. It's just so much easier to inspect data that way....

Some Drake University students and I have been working on wrapping the three.js library for 3d webgl graphics and are nearly complete [0]. We also implemented a simple webcam widget [1]. Here's a live demo illustrating both, using the Sage Cell Server: http://sagecell.sagemath.org/?q=qjjurl (you may need to click Evaluate once, and you'll need to click the permission notification to allow the browser to use your webcam)

[0] https://github.com/jasongrout/pythreejs

[1] https://github.com/jasongrout/ipywidgets

I'm hugely excited to work with the new widgets framework. It might be slightly outside the designed use-case of the IPython notebook, but I'm interested in doing live plotting inside of a notebook and using a notebook as a sort of analytics dashboard.

Bokeh (http://bokeh.pydata.org) has excellent IPython notebook support, and provides interactive, live plots inside the notebook.

It also has matplotlib support, so you can trivially turn Matplotlib figures into interactive web plots (e.g. this interactive plot built via Seaborn, which is a statistical plotting package that uses matplotlib): http://bokeh.pydata.org/docs/gallery/violin.html

The "technical vision" document is really off-putting. It starts with some BS about photography and then asks me

What are the best perceptual approaches to honestly and accurately represent the data to domain experts and SMEs so they can apply their intuition to the data?

I dunno mate, what's an SME for fucking starters?

How do the interactive bokeh plots work? Is it necessary to start up a separate server, or can it work through the widget communication channels?

Actually, Bokeh plots are not (yet) integrated with the widget infrastructure at all, and work in IPython 1.x. There is a fully embeddable javascript library (BokehJS) which handles all the interactivity entirely in the browser. Data can be directly and fully embedded in the DOM.

If you want to view larger data, access the downsampling capabilities of the Bokeh plot server, coordinate views between multiple instances of the notebook, or do streaming and animated plots, then you will need to run the bokeh server.

FYI, Plot.ly allows live updating plots in a notebook. I haven't used them, but it looks neat. Here's a double-pendulum plotting example: http://nbviewer.ipython.org/github/plotly/Streaming-Demos/bl...

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