This textbook covers the contents of an introductory statistics class, as typically taught to undergraduate psychology, health or social science students.

Fun little introductory book for descriptive statistics, visualizations, then some theory, regressions and at the very end a tiny bit of bayesian stats.
Uses 'jamovi', a statistical software, but can be acommodated with all manner of toole like SPSS, PSPP, R, Pandas, Polars, etc etc

Read and write json line format with python, easy, efficient.

Learning to model causality and make causal inferences with R (though applicable to other data science toolkits). Very nice, and splitting right down the middle of statistical and programmatic learning.

Enable scraping (and interaction) with websites, a little more high-level and a different api than beautifulsoup

Fantastically easy approach for .jsonl format to be loaded into pandas.

A side-by-side comparison of the Polars and Pandas libraries. Nice gentle comparison and thus simultaneous 'introduction' to the tools.

(It's not really an introduction, it does expect you to have some prior knowledge on e.g. the pandas core concepts.)

Json but less overhead. (and binary not plain but eh)

The JSON lines format (.jsonl or I believe some also do .jl ?)

Pretty easy to handle, good for streaming through information, can be extended and is faairly readable.

A little less readable than csv/tsv (if they are well formatted) but you can extend a file with another column at any point (which is a huge pain with csv), cells can have actual types and the formatting is much easier.