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https://github.com/gagolews/datawranglingpy

Minimalist Data Wrangling with Python (Open-Access Textbook)
https://github.com/gagolews/datawranglingpy

data-analysis data-science data-visualisation data-wrangling jupyter machine-learning matplotlib modelling numpy pandas python python3 scikit-learn scipy scipy-stats seaborn statistics

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Minimalist Data Wrangling with Python (Open-Access Textbook)

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README 

        



# [Minimalist Data Wrangling with Python](https://datawranglingpy.gagolewski.com/)






*Minimalist Data Wrangling with Python* 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.



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. You can read it at:



*  (a browser-friendly version)

*  (PDF)



You can also order a

[paper copy](https://datawranglingpy.gagolewski.com/order-paper-copy.html).



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 this project.**



Consider citing this book as:

[Gagolewski M.][1] (2025), *Minimalist Data Wrangling with Python*,

Melbourne,

DOI: [10.5281/zenodo.6451068](https://dx.doi.org/10.5281/zenodo.6451068),

ISBN: 978-0-6455719-1-2,

URL: .



Any remarks and bug fixes are appreciated. Please submit them via

this repository's *Issues* tracker. Thank you.



## About the Author



[Marek Gagolewski][1] is currently an Associate Professor

in Data Science at the Faculty of Mathematics and Information Science,

Warsaw University of Technology.



His research interests are related to data science, in particular: modelling

complex phenomena, developing usable, general-purpose algorithms, studying

their analytical properties, and finding out how people use, misuse,

understand, and misunderstand methods of data analysis in scientific, business,

and decision-making settings.



He is an author of ~100 publications, including journal papers

in outlets such as *Proceedings of the National Academy of Sciences (PNAS)*,

*Journal of Statistical Software*, *The R Journal*, *Journal of Classification*,

*Information Fusion*, *International Journal of Forecasting*,

*Statistical Modelling*, *Physica A: Statistical Mechanics and its Applications*,

*Information Sciences*, *Knowledge-Based Systems*,

*IEEE Transactions on Fuzzy Systems*, and *Journal of Informetrics*.



In his "spare" time, he writes books for his students

(check out [*Deep R Programming*](https://deepr.gagolewski.com/))

and [develops](https://github.com/gagolews) open-source software for data analysis, such as

[`stringi`](https://stringi.gagolewski.com/) (one of the most often downloaded

R packages) and

[`genieclust`](https://genieclust.gagolewski.com/) (a fast and robust

hierarchical clustering algorithm in both Python and R).



--------------------------------------------------------------------------------



Copyright (C) 2022–2025, [Marek Gagolewski][1]. Some rights reserved.



This material is licensed under the Creative Commons

[Attribution-NonCommercial-NoDerivatives 4.0 International][2] License

(CC BY-NC-ND 4.0).



[1]: https://www.gagolewski.com/

[2]: https://creativecommons.org/licenses/by-nc-nd/4.0