This is an extended version of the Goodbooks 10k dataset, originally scraped from the Goodreads API in September 2017 by Zygmunt Zając. Additional fields are included in the books_enriched.csv file. The biggest advantage of this new version that it adds a text description field for 9943 of the 10 000 books. Please consult the original repository for additional information on the original files. As a reminder, the dataset contains six million numerical ratings of the platform’s ten thousand most popular books, with data collected from 53 424 different users.
A detailed analysis as well as modelling strategies are presented here.
Additional fields have been added to the original books.csv file via two strategies:
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Pulling attributes from cross-referenced titles in the Best Books Ever Dataset, collected from the Goodreads website in Fall 2020 by Lorena Casanova Lozano and Sergio Costa Planells. From what I gather, they used the Selenium package to parse book webpages.
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For the 1833 books missing from the above dataset, extended fields were scraped with the Goodreads api in October 2021. Although this API has officially been retired, I was able to find a developer key online that still worked.
The four new fields added to the original books.csv file are :
description: a free text summarizing the book's content. On average the description is 900 characters long, with 95% of book descriptions counting less than 1797 characters. This column is 99,43% complete.pages: the total page count. This column is 99.27% complete.publishDate: the publication date. This column is 99.92% complete.genres: the genre tags taken from the top shelves users have assigned to a book. Only the main Goodreads genres have been retained. On average, there are 4.7 genres per title, with 75% of books containing 6 genres or less. This column is 100% complete.
The two updated fields integrated in this version are :
authors: a newly scraped list of all book contributors. This can include illustrators and collaborators. This column is 100% complete.language_code: abbreviated language tags for all books, computed by scanning the book titles with the langid package. This column is 100% complete.
import pandas as pd
from ast import literal_eva
books_df = pd.read_csv('https://raw.githubusercontent.com/malcolmosh/goodbooks-10k/master/books_enriched.csv', index_col=[0], converters={"genres": literal_eval})
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