[CentralOH] plot.ly

[Erik Welch]

+1 for Bokeh.  Seaborn is another popular choice for creating static plots.

It has a few different interfaces that make trade-offs between convenience
and customizability, which may seem overwhelming to new users.  I recommend
watching a tutorial to start learning Bokeh:

https://www.youtube.com/watch?v=Cwnb_o0UORM&index=4&list=PLGB9meziqbzr6sEJ6lScTBMGYTWUGS-e_

If you want to learn more about colormaps and matplotlib's new default,
viridis, watch the video here:

http://matplotlib.org/style_changes.html

Full disclosure: I work for Continuum Analytics, which supports the primary
developers behind Bokeh.

Cheers,
Erik

On Sun, Jun 12, 2016 at 9:49 AM, Austin Godber <godber at gmail.com> wrote:

> Plotly actually released a fully stand alone open source version that does
> not rely on their services:
>
> https://plot.ly/javascript/open-source-announcement/
>
> See these docs for examples of stand alone use:
>
> https://plot.ly/javascript/open-source-announcement/
>
> Maybe it's still not what Eric wants, but I thought someone might at least
> be compelled to try it.  It looks great ... but because it started out
> closed and they're still pretty mum about its open source nature I never
> bothered to look at it.
>
> - Austin
>
>
> On Sat, Jun 11, 2016 at 6:20 PM, Andrew Kubera <andrewkubera at gmail.com>
> wrote:
>
>> Plot.ly <http://plot.ly> looks cool, but I think you need an API key to
>> use. This was a no-go for what I want in an open, data sharing environment.
>> I was pretty happy with Bokeh <http://bokeh.pydata.org/en/latest/>; it
>> didn't have *quite* everything I needed for my physics analysis, but the
>> integration into iPython notebooks is pretty great.
>>
>> -Andrew
>>
>>
>>
>> On Jun 11, 2016, at 8:35 PM, Eric Miller <miller.eric.t at gmail.com> wrote:
>>
>> anybody use this with python, or have recommendations for other simple
>> but beautiful data visualization that is .py friendly?
>>
>> tx
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