Exercise 3: The Four Deliberately Unfixed Problems, and Why They're Left Visible — Possible Solution ==================================================================== THE FOUR PROBLEMS, PER THIS CHAPTER'S OWN SAMPLE DATASET ------------------------------ Per this chapter's own closing paragraph on the dataset: "a missing store_id, an inconsistent date format on row 1004, an inconsistent product-name capitalization on row 1005, and a fully duplicated row at the end." Concretely, in the table itself: row 1003 has a NaN in the store_id column; row 1004's date reads "07/11/2026" while every other row uses the "2026-07-10"-style format; row 1005's product reads "latte" in lowercase while every other row capitalizes it "Latte"; and the final row exactly repeats order 1005 in every column. WHY THESE ARE LEFT UNFIXED HERE RATHER THAN CORRECTED IMMEDIATELY ------------------------------ Per this chapter, "this exact table — messy details included — is what ds1-4 spends its own chapter cleaning up, so nothing here needs fixing yet," and "ds1-4 addresses each one by name." The dataset is introduced in this chapter specifically as raw, uncleaned input — the same dataset ds1-1's own retail-sales narrative example already referenced, now made concrete with an actual table. WHY THIS SERVES THE COURSE'S OWN STATED TEACHING APPROACH ------------------------------ Per ds1-1's own description of the course's chapter-to-chapter design (and echoed directly in this chapter), "ds1-4's cleaning techniques (missing data, duplicates, type coercion) are applied directly to the same messy sample dataset ds1-3 introduced, rather than a fresh one, so the reader watches one dataset actually get fixed" — this course deliberately favors continuity across chapters over introducing a fresh, disconnected example for every new technique. Leaving the four problems visible and unaddressed here means ds1-4 isn't demonstrating cleaning techniques on an abstract, made-up example — it's solving a problem the reader has already personally seen and can already recognize by name. WHY THIS IS A DELIBERATE PEDAGOGICAL CHOICE, NOT AN OVERSIGHT ------------------------------ The chapter explicitly names all four problems by category right after presenting the table, rather than silently leaving them for the reader to stumble on unannounced — demonstrating that their presence is intentional and tracked, not an accidental mistake in the example data that happened to slip through. WHY THIS WORKS AS AN ANSWER ------------------------------ It lists all four problems using the chapter's own naming and locates each one concretely in the sample table, then explains — using the course's own stated cross-chapter continuity design — why presenting a genuinely messy, unfixed dataset here specifically sets up ds1-4's own cleaning chapter to work on a problem the reader already recognizes rather than a fresh, unfamiliar one.