Interactive Data Visualization with Bokeh for Python Programmers
Learn how to use the Bokeh package in Python for creating interactive plots and visualizations. Follow step-by-step instructions for installation, data preparation, plot generation, and customization. Enhance your data visualization skills with Bokeh and unleash your creativity in plotting data effectively.
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Presentation Transcript
Bokeh - BOH-kay http://bokeh.pydata.org/ Pete Alonzi University of Virginia Library Research Data Services Data.library.virginia.edu
Goals Introduce the Bokeh package Make some plots as a group Have time for individual work making plots
Installation of Bokeh conda list | grep bokeh pip freeze | grep bokeh conda install bokeh pip install bokeh
http://bokeh.pydata.org/en/latest/docs/user_guide/qu ickstart.html#userguide-quickstart Prepare some data (in this case plain python lists). Tell Bokeh where to generate output (in this case using output_file(), with the filename "lines.html"). Call figure() to create a plot with some overall options like title, tools and axes labels. Add renderers (in this case, Figure.line) for our data, with visual customizations like colors, legends and widths to the plot. Ask Bokeh to show() or save() the results.
