1. bokeh-notebooks
  2. tutorial

Bokeh Tutorial

00. Introduction and Setup

Tutorial Overview

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

  1. Basic Plotting
  2. Styling and Theming
  3. Data Sources and Transformations
  4. Adding Annotations
  5. Presentation and Layouts
  6. Linking and Interactions
  7. Bar and Categorical Data Plots
  8. Graph and Network Plots
  9. Geographic Plots
  10. Exporting and Embedding
  11. Running Bokeh Applications

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

In [ ]:
# Standard imports 

from bokeh.io import output_notebook, show
output_notebook()
In [ ]:
# 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)
In [ ]:
# Create and deploy interactive data applications

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

Getting set up

In [ ]:
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.

In [ ]:
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