Further Reading: Strings — Text Processing and Manipulation

Python String Documentation

Python Official Documentation: "Text Sequence Type — str." docs.python.org/3/library/stdtypes.html#text-sequence-type-str (Tier 1) The authoritative reference for how strings work: literals, the str() constructor, and everything they support. Bookmark this page — you'll return to it constantly.
Python Official Documentation: "String Methods." docs.python.org/3/library/stdtypes.html#string-methods (Tier 1) A subsection of the page above that lists every string method alphabetically, with a short description and often an example. Jump straight here when you just need to look up one method, like rfind() or isdecimal().

Ned Batchelder (2013). "Facts and Myths about Python Names and Values." nedbatchelder.com/text/names.html (Tier 1) An excellent explanation of how Python variables work — names as labels, not boxes. Directly relevant to understanding why text.upper() doesn't change text. Batchelder later turned it into a PyCon 2015 talk, "Python Names and Values," so you can read it or watch it.
Luciano Ramalho (2022). Fluent Python, 2nd edition, Chapter 6: "Object References, Mutability, and Recycling." O'Reilly. (Tier 1) A deep dive into Python's object model, including string interning, identity vs. equality, and why immutability matters for performance and correctness. More advanced than this chapter, but worth reading after Chapter 14 (OOP).

Jurafsky, D. & Martin, J. H. Speech and Language Processing, 3rd edition draft. web.stanford.edu/~jurafsky/slp3/ (Tier 1) A free online textbook covering natural language processing from the ground up. The draft is revised every year or so, so chapter numbers can shift; in the August 2026 draft, Chapter 2 ("Words and Tokens") covers tokenization and edit distance, extending the ideas in this chapter to industrial-scale text processing. The edit distance material connects directly to the autocorrect case study.
Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python — Analyzing Text with the Natural Language Toolkit. O'Reilly. (Tier 1) Available free online (nltk.org/book). A practical guide to text processing with Python. Chapters 1-3 build directly on the string techniques from this chapter, adding the NLTK library for more sophisticated analysis.

Cock, P. J. A., et al. (2009). "Biopython: freely available Python tools for computational molecular biology and bioinformatics." Bioinformatics, 25(11), 1422-1423. doi:10.1093/bioinformatics/btp163 (Tier 1) The Biopython library provides production-quality FASTA parsing (and much more). If Dr. Patel's case study interested you, Biopython's SeqIO module is the professional tool for the job.
Lesk, A. (2019). Introduction to Bioinformatics, 5th edition. Oxford University Press. (Tier 2) A textbook-length introduction to the field. Chapters on sequence analysis show how string operations scale to genome-level problems.

Spolsky, J. (2003). "The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!)." joelonsoftware.com (Tier 1) A famous blog post explaining why "plain text" isn't simple. This chapter focused on ASCII strings, but real-world text includes accented characters, emoji, and scripts from every human language. Spolsky's article explains the history and practical implications in an engaging, accessible way.
Python Official Documentation: "Unicode HOWTO." docs.python.org/3/howto/unicode.html (Tier 2) Python's guide to working with Unicode strings. Important reading when you need to handle international text, emoji, or non-Latin scripts.

Python Official Documentation: "Format Specification Mini-Language." docs.python.org/3/library/string.html#format-specification-mini-language (Tier 1) The complete reference for f-string format specifications. Goes well beyond what this chapter covered — including binary/octal/hex formatting, sign handling, and more.
Real Python: Python string formatting tutorial. realpython.com/python-string-formatting/ (Tier 2) A practical comparison of Python's string formatting approaches (the % operator, str.format(), and f-strings) with guidance on when to use each. Since we teach f-strings as the modern standard, this article provides useful context for the older styles you'll meet in other people's code.

Norvig, P. "How to Write a Spelling Corrector." norvig.com/spell-correct.html (Tier 1) The essay behind the autocorrect case study. In about half a page of Python, Norvig builds a working spelling corrector using exactly the slicing-and-concatenation trick from edit_distance_one, then adds word frequencies to pick the most likely correction. It uses a few tools you haven't met yet (sets, dictionaries, collections.Counter), so it's a good one to revisit after Chapter 9.
Knuth, D. E. (1973). The Art of Computer Programming, Volume 3: Sorting and Searching, Section 6.3: "Digital Searching." Addison-Wesley. (Tier 3) The trie (prefix tree) data structure is purpose-built for string operations like autocomplete and dictionary lookup. This is the data structure behind the "suggestions as you type" feature in search bars and IDEs. Advanced reading for after you complete Part 6 (Algorithms and Data Structures).
Regular Expressions — Preview for Chapter 22. (Tier 2) If the string processing in this chapter left you wanting more power, Chapter 22 introduces regular expressions — pattern-matching syntax that can do in one line what might take 20 lines of find()/split()/replace() calls. The raw strings you learned in Section 7.9 are used extensively with regular expressions.