Thursday, July 11, 2019

Python 3.7.3 : Testing the Bokeh python module.

This python module has a beautiful website:
Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, 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.
Let's install this python module with the pip tool:
C:\Python373>cd Scripts

C:\Python373\Scripts>pip install bokeh
Collecting bokeh
...
Successfully built bokeh
Installing collected packages: PyYAML, tornado, bokeh
Successfully installed PyYAML-5.1.1 bokeh-1.2.0 tornado-6.0.3
Let's test it with a simple example:
from bokeh.plotting import figure, output_file, show

output_file("test.html")
plot = figure()
plot.line([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 7, 6, 1, 5, 6, 7, 9, 1], line_width=2)
show(plot)
This will create a file into my python folder C:/Python373/test.html.
The HTML webpage comes with the graph and additional tool like Pan, Box Zoom, Wheel Zoom, Save, Reset and help.
If you need sample data that is not included in the Bokeh GitHub repository or released packages then you need to download it.
>>> import bokeh.sampledata
>>> bokeh.sampledata.download()
Creating C:\Users\catafest\.bokeh directory
Creating C:\Users\catafest\.bokeh\data directory
Using data directory: C:\Users\catafest\.bokeh\data
Downloading: CGM.csv (1589982 bytes)
   1589982 [100.00%]
Downloading: US_Counties.zip (3171836 bytes)
    229376 [  7.23%]
Bokeh comes with support for working with Geographical data: Mercator, Google Maps, GeoJSON Data.
You can see all example into the webpage gallery.