In [1]:

    

# Standard imports 

from bokeh.io import output_notebook, show
output_notebook()


    

    






    

        
        Loading BokehJS ...
    






    

In [2]:

    

# 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)


    

In [5]:

    

data.head()


    

    
Out[5]:






  

    

      

      
sepal_length
      
sepal_width
      
petal_length
      
petal_width
      
species
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
setosa
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
setosa
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
setosa
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
setosa
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
setosa
    
  

In [3]:

    

from bokeh.charts import Scatter
p = Scatter(data, x='petal_length', y='petal_width')
show(p)


    

In [4]:

    

p = Scatter(data, x='petal_length', y='petal_width', color='species', 
            legend='top_left', marker='species', title="Ejemplo Scatter")
show(p)


    

In [9]:

    

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


    

    
Out[9]:




Bokeh Tutorial
Setup
Clone or download the repo
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
Install the dependencies
This tutorial has been tested on:

bokeh=0.12.0
pandas=0.18
ipython-notebook=4.0.4
ipywidgets=4.1.1

Other combinations may work also. Packages are available via PyPI and anaconda.org.
Installing with anaconda
Install anaconda
Anaconda should come with all the dependencies included, but you may need to update your versions.
Install with miniconda
Install miniconda.
Use the command line to create an environment and install the packages:

$ conda create -n bokeh_tutorial python=3.4
$ source activate bokeh_tutorial
$ conda install bokeh pandas ipython-notebook ipywidgets
Install with pip
We recommend creating a virtual environment.

$ pip install bokeh pandas "ipython[notebook]" ipywidgets
Get the sample data
Bokeh has a sample data download that gives us some data to build demo visualizations. To get
it run:

$ bokeh sampledata
Install datashader and holoviews (optional)
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

Run Jupyter/IPython notebook
From this folder run jupyter notebook, and open the 00-intro.ipynb notebook.

$ jupyter notebook

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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)


    

    




IPython - 4.2.0
Pandas - 0.18.1
Bokeh - 0.12.0

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