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Data Wrangling is a book category focused on the practical work of turning messy, heterogeneous raw data into reliable, analysis-ready datasets. Books in this genre teach readers how to ingest data from files, databases, APIs, and web sources; clean and standardize formats; handle missing values and outliers; parse, reshape, join, and deduplicate tables; integrate schemas across systems; and engineer features for downstream analytics and machine learning. They emphasize reproducible pipelines and ETL workflows, data quality and lineage, documentation of assumptions, and considerations of privacy and ethics. While often hands-on with tools such as SQL, Python (pandas), R (tidyverse), and Spark, the focus is on robust techniques and patterns that help analysts, data scientists, and engineers prepare trustworthy data for BI, research, and production use.

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