Data Visualization in Python

Updated: Aug 20

The aim of this article is to explore existing Python library for stunning data visualization functions. The library is known as 'bokeh'. Bokeh is a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open-source scientific computing community.

Bokeh is an interactive visualization library for Python that enables beautiful and meaningful visual presentation of data in modern web browsers. With Bokeh, one can quickly and easily create interactive plots, dashboards, and data applications.

The datashader library is designed to complement Bokeh by providing visualizations for very large datasets, focusing on faithfully revealing the overall distribution, not just individual data points. datashader installs separately from bokeh, e.g. using conda install datashader.

When to use datashader

  1. Actual big data; when Bokeh/Matplotlib have trouble.

  2. When the distribution matters more than individual points.

  3. When you find yourself sampling or binning to better understand the distribution.

Datashader currently supports:

  1. Scatterplots/heatmaps

  2. Time series

  3. Connected points (trajectories)

  4. Raster

For more information and collaborative project work using Python, please contact

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