Introductive guide to coding, data cleaning and analysis for Python 3, with many worked exercises.
A large portion of content was adapted from the SoftPython guide, so here below are its relevant acknowledgments.
The making of SoftPython was funded mainly by Department of Information Engineering and Computer Science (DISI), University of Trento, and also Sociology and Mathematics departments.
All the material in this website is distributed with license CC-BY 4.0 International Attribution https://creativecommons.org/licenses/by/4.0/deed.en
Basically, you can freely redistribute and modify the content, just remember to cite University of Trento and the authors