Shape, coverage and known defects of the Goodreads dataset.

Call this before answering anything substantive. It reports live row and population counts for every column that has a coverage problem, and returns the full caveat list, including three defects measured from the loaded tables that the project's own DATA_NOTES.md does not mention.

How book ratings are distributed: a histogram of per-book mean ratings, plus the pooled share of 1-5 star ratings across every rating in scope.

Answers "are ratings clustered high?", "what does a typical rating look like?", "how unusual is a 4.5?".

Highest- or lowest-rated books, subject to a minimum-ratings threshold.

The threshold is the whole point: raise it for a result about well-known books, lower it to reach the long tail. Ties break toward the more heavily rated book.

Under the default unit="editions" a work with several editions can take several places in the list -- all with the same rating, since editions of one work largely share a rating pool. Pass unit="works" for a list of distinct works.

Rating statistics grouped by language.

Grouped on language_normalised, never the raw language column. Read the coverage caveat before quoting anything from this: only 13.6% of books carry a language label, and 83% of those are English.

Rating statistics and publication volume per publication year.

publish_year is the only reliable temporal field in this dataset -- use this rather than publish_month for any real time series. Always ordered chronologically.

Rating statistics grouped by publisher string.

publisher is unnormalised free text with 79,423 distinct values, so each row is one spelling rather than one publisher. Penguin alone occupies six or more separate rows. Treat every figure here as a lower bound on that imprint's real output.

Rating statistics grouped by author string.

authors is one free-text field per book, not a list, so a co-authored book forms its own group rather than counting toward each author. There are 675,289 distinct author strings.

Book length against rating: page-count quartiles overall, and rating statistics for each band of book length.

Answers "do longer books rate higher?". Books with a NULL pages_number are excluded and counted separately -- 11,216 implausible values were nulled during cleaning.

Coarse publishing seasonality by month, plus per-month rating averages.

January is inflated -- it holds 17.72% of rows against a uniform 8.3% because unknown dates were recorded as January 1. The January row is flagged in the output. Prefer stats_by_year for real time-series work.

Shape of the user_ratings table: how the 1-5 stars are distributed, and how active the users are.

This describes 4,154 users only. It is a separate dataset from books, not a sample of it, and does not generalise to Goodreads.

Best- or worst-liked titles among the 4,154 users in user_ratings.

Stays entirely inside user_ratings -- no join, so no title-matching loss. These are the opinions of a small user panel, not the books table.

Where the 4,154-user panel disagrees with the wider Goodreads rating.

This is the only tool that crosses the two tables, and the join is on normalised title text because user_ratings carries no book ID. It reaches 52,016 of 98,686 rated titles (52.7%) -- roughly half the panel's ratings have no book row to match and are simply absent. Editions of the same title are pooled, so book rating counts are summed across up to 36 rows.