#BOKEH PYTHON INSTALL#
#BOKEH PYTHON UPDATE#

on_click ( update ) # arranging the GUI and the plot. stream ( df_new ) # GUI: button = Button ( label = 'add points:' ) npoints = TextInput ( value = '50' ) button. sqrt ( df ** 2 + df ** 2 ) # only the new data is streamed to the bokeh server, # which is an efficient way to proceed source. DataFrame ( sample3, columns = ( 'x', 'y' )) df_new = np.

multivariate_normal (, ,], n ) df_new = pd. # we use the a narrow gaussian centred on (-1, 1), # and draw the requested number of points sample3 = np. value ) # new sample of points to be added. add_tools ( HoverTool ( tooltips = ) ) # this function is called when the button is clicked def update (): # number of points to be added, taken from input text box n = int ( npoints. scatter ( 'x', 'y', source = source, alpha = 0.5 ) p. We name the environment bokeh, and require several packages: bokeh of course, but also pandas, matplotlib, and jupyter.įrom bokeh.layouts import grid from bokeh.models import Button, TextInput def modify_doc ( doc ): # same as before source = ColumnDataSource ( df ) p = figure ( tools = tools ) p. Then, create an environment for this tutorial.

#BOKEH PYTHON INSTALL#

You will learn how to:Ĭreate an interactive plotting system with a user interface (featuring a button!)Īnd all the plotting will be done in a jupyter notebook.Īs usual, we will install all the needed tools with anaconda. In this post, I'll just give you a short demo. We can even set up a bokeh server to display data continuously in a dashboard, while it's being recorded. For example, it can be used in a jupyter notebook for truly interactive plotting, and it can display big data. That's already quite interactive, since you can modify your plots by editing a cell, or add new cells to create more detailed plots.īut bokeh will bring us a whole new set of possibilities. So far in this blog, we've relied mainly on jupyter notebooks and matplotlib. And when you find something, you want to be able to investigate further right away. It will allow you to find features and issues in your dataset. Visualization is absolutely essential in data analysis, as it allows you to directly feed your data into a powerful neural network for unsupervised learning: your brain. It might be worth making it a bit more prominent, since it is an important feature for those designing for the future.Interactive visualization and graphical user interface with bokeh. I'll bet 'Python 3' is mentioned somewhere on the site, but I didn't spot it right away. Now I wish I DID need it, but maybe I'll do a side project of my own with it, just for fun. (Thank you.) And, yes, it is apparently Python 3 enough that we wouldn't need to use any Python 2 if we adopted it. I thought I'd skim over these comments just in case someone asked the obvious question, and someone did. If it's just that euphemistic 'Python', it may as well be Cobol for all I'd care.

I don't have an immediate need for it, but if it's Python 3, I'll keep it in mind. The first thing I looked for on the Bokeh website was some sort of clear statement on the front page about it being Python 3. Python 3 was released eight years ago, and I will ALWAYS choose a competing technology (if it means a language other than Python, that's fine with me) before I'll let Python 2 play any role in a new project I architect. If wanting to know up front if this is designed for the future or the past is an edge case, it's a pretty crowded edge. Or you can watch this webinar recording (start at the 19 minute mark): įor more deep-dive on the architecture, you can start at this slide and walk through: You can use the lasso tool to select some points, and that computes a new sub-histogram. All of that is built straight from Python, but there is no Python kernel running in the background.īokeh also lets you write your event handlers in Python, and reside on the server, and get called back automatically when the user interacts with the plot in some way. 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. See, for instance, Sarah Bird's excellent GapMinder example: 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. The server-side models are generated programmatically via Python, R, Scala, etc. 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.

Bokeh python