Minimalist Data Wrangling with Python

TL;DR

This paper introduces a comprehensive, beginner-friendly guide to data wrangling in Python, covering essential techniques for cleaning, transforming, analyzing, and visualizing data, aimed at students and newcomers.

Contribution

It provides an accessible, high-level overview of data science concepts with practical Python methods, serving as an educational resource for beginners.

Findings

Effective data cleaning and transformation techniques

Methods for exploratory data analysis and clustering

Guidance on pattern modeling and reporting

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/.