The tutorial is broken into several sections, which are each presented in their own notebook:
[Basic Plotting Interface](01 - plotting.ipynb)
[Column Data Sources](02 - column data source.ipynb)
[Layouts, Widgets, and Interactions](03 - interactions.ipynb)
[Styling Visual Attributes](04 - styling.ipynb)
[Data Transformations](05 - data transformations.ipynb)
[Bokeh Applications](06 - server.ipynb)
[Sharing and Embedding](07 - sharing.ipynb)
[Annotations](08 - annotations.ipynb)
[Models and Primitives](09 - models.ipynb)
[High Level Charts](10 - charts.ipynb)
[Geographic Data](11 - geo.ipynb)
[Datashader: Visualizaing Large Data](12 - datashader.ipynb)
[HoloViews and Bokeh](13- holoviews.ipynb)
Appendices
Bokeh is a Data Visualization library for
And most importantly:
# Standard imports
from bokeh.io import output_notebook, show
output_notebook()
# Plot a complex chart in a single line
from bokeh.charts import Histogram
from bokeh.sampledata.iris import flowers as data
hist = Histogram(data, values="petal_length", color="species", legend="top_right", bins=12)
show(hist)
# Build and serve beautiful web-ready interactive visualizations
import utils
p = utils.get_gapminder_plot()
show(p)
# Create and deploy interactive data applications
from IPython.display import IFrame
IFrame('http://demo.bokehplots.com/apps/sliders', width=900, height=500)
from IPython.core.display import Markdown
Markdown(open("README.md").read())
First get local copies of the tutorial notebooks:
$ git clone https://github.com/bokeh/bokeh-notebooks.git
Or download from: https://github.com/bokeh/bokeh-notebooks/archive/master.zip
This tutorial has been tested on:
Other combinations may work also. Packages are available via PyPI and anaconda.org.
Install anaconda
Anaconda should come with all the dependencies included, but you may need to update your versions.
Install miniconda.
Use the command line to create an environment and install the packages:
$ conda env create
$ source activate bokeh-notebooks
Run this from the tutorial directory where environment.yml lives.
Bokeh has a sample data download that gives us some data to build demo visualizations. To get it run:
$ bokeh sampledata
Optional tutorials 11 and 12 require the datashader and holoviews packages, respectively, which can be installed with:
$ conda install -c bokeh datashader
$ conda install -c holoviews/label/dev holoviews
From this folder run jupyter notebook, and open the 00-intro.ipynb
notebook.
$ jupyter notebook
Setup-test, run the next cell. Hopefully you should see output that looks something like this:
IPython - 5.1.0
Pandas - 0.18.1
Bokeh - 0.12.2
If this isn't working for you, see the README.md
in this directory.
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)
IPython - 5.1.0 Pandas - 0.18.1 Bokeh - 0.12.2rc3