Getting started with Bokeh / Let's build an interactive data visualization for the web..in Python!
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| Title | Getting started with Bokeh / Let's build an interactive data visualization for the web..in Python! |
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| Part Number | 16 |
| Number of Parts | 173 |
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| License | You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this license. |
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| Production Place | Bilbao, Euskadi, Spain |
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| Abstract | Sarah Bird - Getting started with Bokeh / Let's build an interactive data visualization for the web..in Python!
As a web developer, I find myself being asked to make increasing
numbers of data visualizations, interactive infographics, and more.
d3.js is great, as are many other javascript toolkits that are out
there. But if I can write more Python and less JavaScript... well,
that makes me happy!
Bokeh is a new Python library for interactive visualization. Its
origins are in the data science community, but it has a lot to offer
web developers. In this mini-tutorial, I'll run through how to build a
data visualization in Bokeh and how to hook it into your web
application. This will be a real-world example, that was previously
built in d3.js.
Along the way, I'll provide tips and tricks that I've discovered in my
experience including how Bokeh works wonderfully with the iPython
notebook which I use to prototype my visualizations, and many data
science people use as their native way to explore data.
For those of you who already know a little Bokeh, I'll be covering the
new "actions framework" that lets you write JS callbacks in your
python code so you can do lots of interactions all on the client side. |
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