Recommended:
Jake Vanderplas, A Whirlwind Tour of Python
Allen B. Downey, Think Python 2: How to Think Like a Computer Scientist
Quinn Dombrowski, Jupyter notebooks for digital humanities (a collection of sources)
Other General Sources:
Peter Broadwell & Simon Wiles, Introduction to Python (Stanford CIDR Workshops)
Ethan Swan and Bradley Boehmke, Introduction to Python for Data Science
## Python Best Practices (Style & Optimization)
The Hitchhiker's Guide to Python
Best of the Best Practices Guide for Python
Python Guide - Testing your Code
Python Guide - Structuring Your Project
Python Programming for the Humanities - course centered on text analysis
NLTK: Natural Language Processing with Python
Cleaning text for Machine Learning
Programming Historian - Counting Frequencies
## Conditionals Python 4 Everybody - Conditional Execution
## Files
Python 4 Everybody - Files Lesson (video)
Esri's Beginner's Guide to Python in ArcGIS
Esri: Beginner's Guide - Using the API
Generative Digital Art - Tutorials and Inspiration
https://library.capture.duke.edu/Panopto/Pages/Viewer.aspx?id=28e9066b-d529-438e-9b23-aab600ef4e4a
Eric Monson, Python for Data Science: Pandas 102
Eric Monson's Pandas & JupyterLab GitHub Repository
Best Python Libraries and Packages for Beginners
Pandas and Numpy are common for manipulating data and using mathematics, and could almost be considered Python standards. We will see Pandas for handling .csv files, and learn more about it in Lesson 10.
Several libraries or tools are available for humanities-specific tasks with Python:
Text analysis: Textblob, NLTK, Spacy
Imaging: Pillow/PIL
Mapping: ArcGIS
Drawing/Generative Art: ipycanvas,Processing.
## Other Debates in the Digital Humanities