Visualizing Data with Bokeh and Pandas

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

The ability to load raw data, sample it, and then visually explore and present it is a valuable skill across disciplines. In this tutorial, you will learn how to do this in Python by using the Bokeh and Pandas libraries. Specifically, we will work through visualizing and exploring aspects of WWII bombing runs conducted by Allied powers, i.e., the WW II THOR dataset (Theater History of Operations Reports (THOR).
At the end of the lesson you will be able to:
-Load tabular CSV data
-Perform basic data manipulation, such as aggregating and sub-sampling raw data
-Visualize quantitative, categorical, and geographic data for web display
-Add varying types of interactivity to your visualizations

Prerequisites
-This tutorial can be completed using any operating systems. It requires Python 3 and a web browser. You may use any text editor to write your code.
-This tutorial assumes that you have a basic knowledge of the Python language and its associated data structures, particularly lists.
-If you work in Python 2, you will need to create a virtual environment for Python 3, and even if you work in Python 3, creating a virtual environment for this tutorial is good practice.

Authoring Person(s) Name: 
Charlie Harper
Authoring Organization(s) Name: 
The Programming Historian
License - link to legal statement specifying the copyright status of the learning resource: 
Creative Commons Attribution 4.0 International - CC BY 4.0
Access Cost: 
No fee
Citation - format of the preferred citation for the learning resource: 
Charlie Harper, "Visualizing Data with Bokeh and Pandas," The Programming Historian 7 (2018), https://doi.org/10.46430/phen0081.
Primary language(s) in which the learning resource was originally published or made available: 
English
More info about
Keywords - short phrases describing what the learning resource is about: 
Data analysis
Data coding
Data skills education
Data visualization
Data visualization tools
Digital humanities
Humanities data
Network analysis
Programming
Python
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Arts and Humanities
Arts and Humanities: Digital Humanities
Arts and Humanities: History
Arts and Humanities: History of Art, Architecture, and Archaeology
Arts and Humanities: Other Languages, Societies, and Cultures
Published / Broadcast: 
Friday, July 27, 2018
ID - identifier that provides the means to locate the learning resource or its citation: 
10.46430/phen0081
Type - namespace prefix for the citable locator, if any: 
DOI
Publisher - organization credited with publishing or broadcasting the learning resource: 
The Programming Historian
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
Target Audience - intended audience for which the learning resource was created: 
Data manager
Data professional
Graduate student
Mid-career research scientist
Research faculty
Research scientist
Software engineer
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)