An Introduction to Interactive Data Visualization with Bokeh
Data Visualization is an essential step for developing data driven solution. With proper visualization, we get direct insights that lead us towards further stages of model development. While performing visualization in python, we have libraries like Matplotlib, seaborn for our help. But they come with certain limitations. Recently developed libraries with interactive plotting options, are taking up their place slowly. Bokeh is one of them. It is interactive and data-driven, suitable for real-time visualization.
In this talk, I am going to present a complete introduction to Bokeh with coded examples for data science enthusiasts.
The outline of the planned talk consists of the following module.
1. Basic Introduction
A complete introduction to bokeh, how to use and other relevant information.
why it is a better choice over other libraries.
Functions of Interest
1. charts function 2. figure function
PLotting a simple plot with charts function
A Simple Line Plot with Figure function
Multiple Glyphs in a Single Plot
Working with error-reports
Different type of glyphs
Scatter Markers, NaN Plotting (Handling missing data), Bar Plot, Single Patch, Multiple Patches
Special type of Glyphs
Segments, Wedges, Time-Series plotting
Handling Categorical Data
Mapping of Geo Locations
Next Places to look for
This section provides a complete list of resources which will help the attandees to take their bokeh knowledge to the next level .
1.Python installed laptop with necessary libraries like numpy,pandas etc.
2.Familiarity with jupyter notebook.
3.bokeh installed with python (https://bokeh.pydata.org/en/latest/docs/installation.html)
I am a MTech student at Dept of CSE, IIT Hyderabad, Currently working in the area of Machine Learning and Data Mining. My research interest revolves around various aspects of social-network analysis.
I have experiences of using sophisticated Data Science libraries in python and always interested towards new technologies that keep coming every now and then, and help us to understand the problem statement more clearly.