Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
from bokeh.plotting import figure from bokeh.io import output_notebook, show
Next, we'll import NumPy and create some simple data.
from numpy import cos, linspace x = linspace(-6, 6, 100) y = cos(x)
Now we'll call Bokeh's
figure functtion to create a plot
p. Then we call the
circle() method of the plot to render a red circle at each of the points in x and y.
We can immediately interact with the plot:
The toolbar below is the default one that is available for all plots. It can be configured further via the
tools keyword argument.
p = figure(width=500, height=500) p.circle(x, y, size=7, color="firebrick", alpha=0.5) show(p)
Bokeh's core display model relies on composing graphical primitives which are bound to data series. This is similar in spirit to Protovis and D3, and different than most other Python plotting libraries.
A slightly more sophisticated example demonstrates this idea.
Bokeh ships with a small set of interesting "sample data" in the
bokeh.sampledata package. We'll load up some historical automobile mileage data, which is returned as a Pandas
from bokeh.sampledata.autompg import autompg grouped = autompg.groupby("yr") mpg = grouped["mpg"] avg = mpg.mean() std = mpg.std() years = list(grouped.groups.keys()) american = autompg[autompg["origin"]==1] japanese = autompg[autompg["origin"]==3]
For each year, we want to plot the distribution of MPG within that year.
p = figure(title="MPG by Year (Japan and US)") p.vbar(x=years, bottom=avg-std, top=avg+std, width=0.8, fill_alpha=0.2, line_color=None, legend="MPG 1 stddev") p.circle(x=japanese["yr"], y=japanese["mpg"], size=10, alpha=0.5, color="red", legend="Japanese") p.triangle(x=american["yr"], y=american["mpg"], size=10, alpha=0.3, color="blue", legend="American") p.legend.location = "top_left" show(p)
To link plots together at a data level, we can explicitly wrap the data in a
ColumnDataSource. This allows us to reference columns by name.
We can use a "select" tool to select points on one plot, and the linked points on the other plots will highlight.
from bokeh.models import ColumnDataSource from bokeh.layouts import gridplot source = ColumnDataSource(autompg) options = dict(plot_width=300, plot_height=300, tools="pan,wheel_zoom,box_zoom,box_select,lasso_select") p1 = figure(title="MPG by Year", **options) p1.circle("yr", "mpg", color="blue", source=source) p2 = figure(title="HP vs. Displacement", **options) p2.circle("hp", "displ", color="green", source=source) p3 = figure(title="MPG vs. Displacement", **options) p3.circle("mpg", "displ", size="cyl", line_color="red", fill_color=None, source=source) p = gridplot([[ p1, p2, p3]], toolbar_location="right") show(p)
You can read more about the
ColumnDataSource and other Bokeh data structures in Providing Data for Plots and Tables
In addition to working well with the Notebook, Bokeh can also save plots out into their own HTML files. Here is the bar plot example from above, but saving into its own standalone file.
Now when we call
show(), a new browser tab is also opened with the plot. If we just wanted to save the file, we would use
from bokeh.plotting import output_file output_file("barplot.html") p = figure(title="MPG by Year (Japan and US)") p.vbar(x=years, bottom=avg-std, top=avg+std, width=0.8, fill_alpha=0.2, line_color=None, legend="MPG 1 stddev") p.circle(x=japanese["yr"], y=japanese["mpg"], size=10, alpha=0.3, color="red", legend="Japanese") p.triangle(x=american["yr"], y=american["mpg"], size=10, alpha=0.3, color="blue", legend="American") p.legend.location = "top_left" show(p)
Bokeh also has a server component that can be used to build interactive web applications that easily connect the powerful constellation of PyData tools to sophisticated Bokeh visualizations. The Bokeh server can be used to:
Click on any of the thumbnails below to launch a live Bokeh applications.
Find more details and information about developing and deploying Bokeh server applications in the User's Guide chapter Running a Bokeh Server.
Find more details and information at the resources listed below:
Mailing list: [email protected]
Gitter Chat: https://gitter.im/bokeh/bokeh