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README.md

python_visualizations

python talk on data visualization - focused on matplotlib and bokeh libraries

Outline

Introduction

Objectives

Understand why effective visualizations are important

Introduction to the grammar of graphics and how to choose the right visual approach

Get a snapshot of the python visualization universe

Explore Foundation - learn about matplotlib, understand core use cases & pitfalls + ways to make it better, do some live coding

Look at the Future - Learn about the bokeh library, learn about interactive visualizations, even more live coding

Notes

inspiration - effective visualizations

charles minard - march to moscow (charles minard)[https://en.wikipedia.org/wiki/Charles_Joseph_Minard#The_map_of_Napoleon%27s_Russian_campaign]

challenger disaster - Roberts Report (presentation obscured lack of data)[https://www.vice.com/en_us/article/kbb3qz/could-better-data-design-have-prevented-challenger] (13 charts failed to stop the launch - analysis of engineering discussions)[https://spacegrant.carthage.edu/live/files/2505-tap16workshop-4-tuftepdf] (primary chart - by launch date)[https://history.nasa.gov/rogersrep/v5p896.htm] (Report from Presidential Commission Hearings)[https://history.nasa.gov/rogersrep/v4p645.htm] (main report)[https://history.nasa.gov/rogersrep/v4part6.htm#645]

Illustrate Python Visualization universse; compare libaries - Matplotlib ; Bokeh; Plotly

Matplotlib -

Matplotlib - use to explore Challenger Disaster Visualization

Matplotlib - explain and demo different APIs

Matplotlib - illustrate simple ways to improve... & Matplotlib - you need to know it, and how to solve

Bokeh - the new hotness

Bokeh - syntax, basic cases

Bokeh - interactivity - hover, zoom, pan, linking

Bokeh - key concepts; some ideas

Bokeh - animation / streaming data

Supporting Resources -- examples of inspirational visualizations

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