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Book . 2022
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image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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Book . 2022
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image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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Book . 2022
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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Book . 2022
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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Book . 2022
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Book . 2025
License: CC BY NC ND
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https://dx.doi.org/10.26187/de...
Book . 2023
License: CC BY NC ND
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Book . 2022
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https://dx.doi.org/10.26187/de...
Book . 2023
License: CC BY NC ND
Data sources: Datacite
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Book . 2022
Data sources: ZENODO
ZENODO
Book . 2022
Data sources: ZENODO
ZENODO
Book . 2022
Data sources: ZENODO
ZENODO
Book . 2025
License: CC BY NC ND
Data sources: Datacite
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Book . 2023
Data sources: Datacite
ZENODO
Book . 2023
Data sources: ZENODO
ZENODO
Book . 2022
Data sources: ZENODO
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Minimalist Data Wrangling with Python

Abstract

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. This textbook is a non-profit project. Its online and PDF versions are freely available at https://datawranglingpy.gagolewski.com/. To order a paper copy, see https://datawranglingpy.gagolewski.com/order-paper-copy.html. Marek Gagolewski is an Associate Professor in Data Science at 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 research, commercial, and decision making settings. In his spare time, he writes books for his students and develops free (libre) data analysis software, such as stringi – one of the most often downloaded R packages, and genieclust – a fast and robust clustering algorithm in both Python and R. See also: Deep R Programming at https://deepr.gagolewski.com/.

Please cite this book as: Gagolewski M. (2025), Minimalist Data Wrangling with Python, Zenodo, Melbourne, DOI: 10.5281/zenodo.6451068, ISBN: 978-0-6455719-1-2, URL: https://datawranglingpy.gagolewski.com/

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Keywords

Modelling and simulation, scipy, Software engineering not elsewhere classified, data frames, Numerical computation and mathematical software, vectors, matrices, numpy, FOS: Mathematics, Artificial intelligence not elsewhere classified, pandas, matplotlib, Data Wrangling, Data Science, Statistics, outliers, Programming languages, data cleansing, missing values, classification, Concurrent/parallel systems and technologies, regression, scikit-learn, data science, text processing, time series, data wrangling, Applied computing not elsewhere classified, Python, clustering

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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