Minimalist Data Wrangling with Python#


Minimalist Data Wrangling with Python by Marek Gagolewski is envisaged as a student’s first introduction to data science, providing a high-level overview as well as discussing key concepts in detail. We explore methods for cleaning data gathered from different sources, transforming, selecting, and extracting features, performing exploratory data analysis and dimensionality reduction, identifying naturally occurring data clusters, modelling patterns in data, comparing data between groups, and reporting the results.

Although available online, it is a whole course, and should be read from the beginning to the end. In particular, refer to the Preface for general introductory remarks.

For many students around the world, educational resources are hardly affordable. Therefore, I have decided that this book should remain an independent, non-profit, open-access project (available both in PDF and HTML forms). Whilst, for some people, the presence of a “designer tag” from a major publisher might still be a proxy for quality, it is my hope that this publication will prove useful to those who seek knowledge for knowledge’s sake.

Please spread the news about it by sharing the above URLs with your mates, peers, or students. Any bug/typo reports/fixes are appreciated. Please submit them via this project’s GitHub repository. Thank you.

Consider citing this book as: Gagolewski M. (2023), Minimalist Data Wrangling with Python, Zenodo, Melbourne, DOI: 10.5281/zenodo.6451068, ISBN: 978-0-6455719-1-2, URL:

You can order a printed copy from Amazon: AU CA DE ES FR IT JP NL PL SE UK US. Note that I receive 0% revenue from sales (price = cost of printing + distributor fee). Let me know if you know a vendor who can deliver this book to some geographic regions more cheaply.

Make sure to check out my other open-access book, Deep R Programming [34].

Copyright (C) 2022–2023 by Marek Gagolewski. Some rights reserved. This material is published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).