Python is used by 46.9% of professional developers -- the most widely used language after JavaScript, SQL, and HTML/CSS -- making it the practical default for data engineering, according to the 2024 Stack Overflow Developer Survey.
The canonical reference. Chapters 4-9 cover everything you need: control flow, data structures, modules, I/O, and errors. Read these before any course.
Free online. The definitive book on pandas from its creator. Part II on data wrangling is required reading for any data engineer.
Clear, practical tutorials with runnable examples. Good for filling gaps in string handling, dicts, and list comprehensions.
Understanding how packages work -- virtual environments, pyproject.toml, uv -- prevents the dependency chaos that wastes days.
Python is the lingua franca of data engineering and of software broadly. According to the 2024 Stack Overflow Developer Survey, 46.9% of professional developers used Python in the past year -- behind only JavaScript (64.6%), SQL (54.1%), and HTML/CSS (52.9%). Among those learning to code, Python ranks first at 66.4%. You do not need to master the full language before you are productive -- but you do need iterators, comprehensions, context managers, and error handling.
The data engineering Python stack is narrower than it looks: standard library (pathlib, subprocess, logging), pandas or Polars for in-memory work, and requests or httpx for APIs. Master these before reaching for anything else. The fancy libraries are built on the same foundations.
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