Bokeh Tutorial

00. Introduction and Setup

Tutorial Overview¶

The tutorial is broken into several sections, which are each presented in their own notebook:

As well as some extra topic appendices:

A1. Models and Primitives
A2. Visualizing Big Data with Datashader
A3. High-Level Charting with Holoviews
A4. Additional Resources

What is Bokeh¶

Bokeh is an interactive visualization library that targets modern web browsers for presentation. It is good for:

Interactive visualization in modern browsers
Standalone HTML documents, or server-backed apps
Expressive and versatile graphics
Large, dynamic or streaming data
Easy usage from python (or Scala, or R, or...)

And most importantly:

NO JAVASCRIPT REQUIRED¶

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

What can I do with Bokeh¶

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# Standard imports 

from bokeh.io import output_notebook, show
output_notebook()

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# Plot a complex chart with interactive hover in a few lines of code

from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg_clean as df
from bokeh.transform import factor_cmap

df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)

group = df.groupby(by=['cyl', 'mfr'])
source = ColumnDataSource(group)

p = figure(width=800, height=300, title="Mean MPG by # Cylinders and Manufacturer",
           x_range=group, toolbar_location=None, tools="")

p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Manufacturer grouped by # Cylinders"
p.xaxis.major_label_orientation = 1.2

index_cmap = factor_cmap('cyl_mfr', palette=['#2b83ba', '#abdda4', '#ffffbf', '#fdae61', '#d7191c'], 
                         factors=sorted(df.cyl.unique()), end=1)

p.vbar(x='cyl_mfr', top='mpg_mean', width=1, source=source,
       line_color="white", fill_color=index_cmap, 
       hover_line_color="darkgrey", hover_fill_color=index_cmap)

p.add_tools(HoverTool(tooltips=[("MPG", "@mpg_mean"), ("Cyl, Mfr", "@cyl_mfr")]))

show(p)

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# Create and deploy interactive data applications

from IPython.display import IFrame
IFrame('https://demo.bokeh.org/sliders', width=900, height=500)

Getting set up¶

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from IPython.core.display import Markdown
Markdown(open("README.md").read())

Setup-test, run the next cell. Hopefully you should see output that looks something like this:¶

IPython - 7.9.0
Pandas - 0.25.2
Bokeh - 1.4.0

If this isn't working for you, see the README.md in this directory.

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from IPython import __version__ as ipython_version
from pandas import __version__ as pandas_version
from bokeh import __version__ as bokeh_version
print("IPython - %s" % ipython_version)
print("Pandas - %s" % pandas_version)
print("Bokeh - %s" % bokeh_version)

Next Section¶

Click on this link to go to the next notebook: 01 - Basic Plotting

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