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1. #Bokeh python interactive plot how to
2. #Bokeh python interactive plot install
3. #Bokeh python interactive plot code
4. #Bokeh python interactive plot series
5. #Bokeh python interactive plot free

That should launch a browser with IPython Notebook where you should just create a new notebook.Īll you need to do to import and enable Bokeh for plotting within your notebook is to enter the following code within one of the cells: import otting Merely start your IPython Notebook by entering ipython notebook in your terminal (after having activated the environment in which Bokeh is installed).

#Bokeh python interactive plot how to

I’m not going to go into detail here over the inner-workings of Bokeh but rather give quick instructions on how to ‘enable’ it into your IPython Notebook and create a simple plot show-casing some of the interactivity offered by the package. Note that if you have multiple separate Python environments, e.g., a Python 2.7 and a Python 3.4 environment as was shown in this previous post, you will need to activate the environment you would like to use Bokeh in and use the above command in order to ‘link’ that package (upon installation into the root environment).

#Bokeh python interactive plot install

InstallationĪs with most things, a good place to start is the ‘Quick-start’ page.Īssuming you’re running an Anaconda distro, the best way to install bokeh is simply by opening a terminal and entering: conda install bokeh

#Bokeh python interactive plot free

While Bokeh doesn’t have as many bells and whistles as Plotly (see next post), its entirely free and unrestricted and works like a charm within the IPython console and the IPython Notebook. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. If I may quote:īokeh is a Python interactive visualization library that targets modern web browsers for presentation. A static version of such a plot, which I shamelessly screen-captured from the Plotly website can be seen in the figure below.īokeh is a package by Continuum Analytics, authors of the Anaconda distribution of which I spoke in this previous post.

#Bokeh python interactive plot series

Packages such as Bokeh and Plotly (see below) save you a lot of that trouble by providing an easily embeddable plot directly into your beloved IPython Notebook along with a series of ‘tools’ allowing you to zoom and pan the plot, resize the whole thing, save as a static image, or even hover over the plotted data and get lil’ tooltips with their underlying data. Well, with interactive plotting the days of the static plot are dwindling. One would have to change axes limits, margins, line-widths, etc etc. I will also provide some very rudimentary examples that should allow to get started straight away.Īnyone who’s delved into ‘exploratory’ data analysis requiring a depiction of their results would have inevitably come to the point where they would need to fiddle with plotting settings just to make the result legible (much more work required to make it attractive). This post will focus on Bokeh while the next post will be about Plotly. In this post I will talk about interactive plotting packages that support the IPython Notebook and allow you to zoom, pan, resize, or even hover and get values off your plots directly from an IPython Notebook. Make sure to run bokeh serve -show test.py from command line # test.pyįrom bokeh.models import Slider,ColumnDataSource,TextInputįrom otting import figure, curdocĭf1 = pd.DataFrame(np.random.randint(9,25,size=(2,2)),columns=)ĭf2 = pd.DataFrame(np.arange(4,8).Interactive Plotting in IPython Notebook (Part 1/2): Bokeh Summary I always go for bokeh server if I have to generate interactive plots.Īdvantage: You can write native python code without worrying about JS callbacks. I am looking forward to some hints, thanks! User_ID user_ID_count number_requested_plots If it does not not work with slider a button to click would be also a smart solution. I would like to build something with slider from bokeh to be able to "slide" over the different plots. Now, I have not only data_1 but also data_2 and so on. P.yaxis.major_label_text_font_size = "13pt"

P.yaxis.axis_label_text_font_size = "13pt" P.xaxis.axis_label_text_font_size = "13pt" P.yaxis.axis_label = "number requested report" User_ID: reports: plots: = figure(plot_width=1000, plot_height=500, tooltips=TOOLTIPS,

The following code works fine for data_1 and finally I get an html file: data_1 = ColumnDataSource(data=user_id_weekly01)ĭata_2 = ColumnDataSource(data=user_id_weekly02) I would like to create interactive plots in one figure with bokeh.