"Bokeh is a Python interactive visualization library that targets modern web browsers for presentation."
A description of how one gets from Python to a web browser display would be nice. Is there a translation from Python to Javascript somewhere? Is there a Python web server backend? Are there dynamic visual updates or does this thing just generate a static output like a .png file?
There is a full-featured javascript runtime "BokehJS" which is designed from the ground up to be driven by remote (aka server-side) models, which are sent over the wire as JSON. The server-side models are generated programmatically via Python, R, Scala, etc. The cool thing is that a lot of the interactivity is completely native in BokehJS, so you can build interactive Javascript visualizations from Python, and have an entirely static HTML document that embeds a lot of rich interactivity.
When you move the slider, it generates JS events which drive model updates completely in the browser, which then update the objects that comprise the plot. All of that is built straight from Python, but there is no Python kernel running in the background.
you might be interested in http://demo.bokehplots.com/ which are all examples of bokeh apps that use websockets to allow you to run python functions based on user interactions with plots. the code for all the examples is linked from that page also.
> When you execute this script, you will see that a new output file "lines.html" is created, and that a browser automatically opens a new tab to display it. (For presentation purposes we have included the plot output directly inline in this document.)
This is based on an example calling an output_file() function, but the docs also mention output_notebook(), and I'm sure you can output to HTML as a string as well. I didn't spend too much time digging yet.
> A description of how one gets from Python to a web browser display would be nice.
Check out Jupyter notebooks.
> Is there a Python web server backend?
That's possible for dynamic charts, but for most usage, Bokeh saves all the chart's data inside the .html file (yielding sometimes huge files). See http://bokeh.pydata.org/en/latest/docs/gallery.html for examples.
i would say bokeh is more similar to d3 than highcharts (although my under-standing of highcharts may be off)
1) you can create any viz you want to - you have access to very low-level elements and can build up anything from them
2) it's liberally open source licensed
why i initially got sucked into bokeh over d3 is that:
a) I prefer python
b) I find it more intuitive (d3 always messes with my head)
I forgot the really important difference between bokeh and highcharts - you can throw 100k points at it and have it be fully interactive without your browser blowing up!
HiCharts has dynamically loadable data. I have a database that has 120M points. I just keep some pre aggregated data to populate the wide periods. As you zoom it it selects smaller aggregation sets. (Think; 1 month for 10 years, 2 weeks for 1 year, etc). If you have the data you only need about 20 lines of JS so support this with json.
That said, bokeh requires little to no javascript and that's a huge advantage if you need to get multiple visualizations. It does, pretty much stock, what you can do with HiCharts without needing as much full-stack expertise.
A description of how one gets from Python to a web browser display would be nice. Is there a translation from Python to Javascript somewhere? Is there a Python web server backend? Are there dynamic visual updates or does this thing just generate a static output like a .png file?