---
license: other
viewer: true
language:
- en
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-regression
tags:
- goodreads
- books
- social-network
- reading-behavior
pretty_name: Goodreads Books Metadata
dataset_info:
  features:
  - name: book_id
    dtype: string
  - name: url
    dtype: string
  - name: title
    dtype: string
  - name: first_author
    dtype: string
  - name: first_author_url
    dtype: string
  - name: first_author_num_books
    dtype: int64
  - name: first_author_num_followers
    dtype: int64
  - name: average_rating
    dtype: float64
  - name: num_reviews
    dtype: int64
  - name: first_published
    dtype: string
  - name: publisher
    dtype: string
  - name: language_code
    dtype: string
  - name: num_pages
    dtype: int64
  - name: description
    dtype: string
  - name: genres
    dtype: string
  - name: format
    dtype: string
  - name: series
    dtype: string
  - name: num_currently_reading
    dtype: int64
  - name: num_want_to_read
    dtype: int64
configs:
- config_name: default
  data_files:
  - split: train
    path: books-part*.parquet
---

# Goodreads Books Metadata

## Dataset Description

**Goodreads Books Metadata** is a structured dataset of book records scraped directly from [Goodreads](https://www.goodreads.com), a social platform for book readers and recommendations. 
The dataset was collected in **July 2026** and contains rich metadata per book: bibliographic information, crowd-sourced ratings, reader engagement signals (currently reading, want to read), author-level statistics, genre tags, and descriptive text.

The primary research objectives behind this dataset are:

- **Understanding how users rate books on Goodreads** — what drives average ratings, what genres score highest, how reviewer volume correlates with ratings.
- **Identifying the most preferred books** — top-rated titles, best-sellers by engagement, most discussed books.
- **Profiling the type of readers on the platform** — inferred from engagement metrics such as `num_want_to_read`, `num_currently_reading`, and `num_reviews`.

This dataset is part of a **personal data science portfolio project**, demonstrating end-to-end data collection, transformation, and analysis on a real-world social platform.

- **Homepage:** https://www.goodreads.com
- **Repository (source code):** *[goodreads_books](https://github.com/p-f-a-h-acouetey/goodreads_books)*
- **Point of Contact:** pfaha (My Hugging Face profile)

---

## Dataset Summary

| Property | Value |
|---|---|
| Source | Goodreads Books (scraped) |
| Collection date | July 2026 |
| Format | Parquet (partitioned) |
| Language | English (primary) |
| Storage size | ~45 MB |
| Number of files | ~50 part files |

---

## Supported Tasks

This dataset can support various tasks, among which we identify the following:

- **Book rating prediction** — Predict a book's `average_rating` from other features in the dataset.
- **Genre classification** — Classify books into genre categories using `description` text and other metadata.
- **Popularity analysis** — Rank and compare books by reader engagement (`num_want_to_read`, `num_currently_reading`, `num_reviews`).
- **Author influence analysis** — Study how author follower counts and book counts correlate with book ratings.
- **Reading behavior profiling** — Use `num_currently_reading` and `num_want_to_read` as proxies for user interest distribution.

---

## Data Collection

### Scraping pipeline

The dataset was collected using a custom asynchronous Python ETL pipeline. The collection process works as follows:

1. **Sampling**: The scraper randomly samples book IDs within a valid range (min ID = 1 and max ID = 60,000,000) to build a target list of books to fetch.
2. **Fetching**: For each sampled book ID, the scraper fetches the corresponding Goodreads page (`https://www.goodreads.com/book/show/{id}`) using session pooling and automatic rotation to avoid rate limits and blocks.
3. **Parsing**: Each page is validated against a **hard JSON-LD gate**:
   - The scraper first attempts to extract `application/ld+json` structured data from the page.
   - If JSON-LD is missing or empty, the page is **discarded entirely** — no record is created, and the crawler immediately draws a fresh random
     book ID as a replacement.
   - Only once JSON-LD is confirmed present does the scraper additionally pull the `__NEXT_DATA__` hydration payload and raw HTML elements
     (via CSS selectors) to **enrich** fields like `description`, `genres`, `format`, `series`, `num_pages`, `publisher`, and reading-engagement
     counts. These are supplementary sources, never substitutes for JSON-LD.
4. **Enrichment**: After extracting book metadata, the crawler additionally fetches the **first author's Goodreads profile page** to enrich the record with their total number of published books and follower count.
5. **Transformation & Load**: Records are buffered in memory, schema-validated, and cleaned. Missing numeric counts are filled with zeros. Finally, records are **checkpointed to the Hugging Face Hub periodically** (e.g., every 1,000 books) as numbered Parquet files (`books-partN.parquet`).
6. **Tracking**: A manifest of already-scraped IDs is maintained locally and on the Hub to allow resumable, fault-tolerant runs across multiple sessions.

### Politeness and rate limiting

The scraper handles rate limits smoothly through active session management. 
It manages concurrency and limits execution to a default of tasks per minute. 
It automatically triggers session rotation when encountering HTTP 429 (Too Many Requests) or HTTP 403 blocks to ensure polite and sustained extraction.

### Text cleaning

All text fields undergo a standard cleaning pipeline: HTML entity unescaping, NFKC Unicode normalisation, non-breaking space replacement, BOM stripping, control character removal, parenthesized segment removal, whitespace collapsing, and lowercasing.

---

## Dataset Structure

### Data Fields

The output strictly follows the schema defined in the project:

| Column | Type | Description |
|---|---|---|
| `book_id` | `string` | Numeric Goodreads book ID, extracted from the page URL (kept as string). |
| `url` | `string` | Full Goodreads URL for the book page (e.g. `https://www.goodreads.com/book/show/92`). |
| `title` | `string` | Book title as displayed on Goodreads, lowercased. |
| `first_author` | `string` | Name of the first (primary) author listed on the page. |
| `first_author_url` | `string` | Goodreads author profile URL for the first author. |
| `first_author_num_books` | `int64` | Number of distinct works listed on the author's Goodreads page. Proxy for author prolificacy. (0 if missing) |
| `first_author_num_followers` | `int64` | Number of Goodreads followers for the first author at the time of scraping. Proxy for author popularity. (0 if missing) |
| `average_rating` | `float64` | Crowd-sourced average star rating (1.0–5.0) aggregated across all Goodreads user ratings. (0.0 if missing) |
| `num_reviews` | `int64` | Total number of text reviews submitted by Goodreads users. (0 if missing) |
| `first_published` | `string` | First publication date as a string (e.g. `"october 1, 2020"`). Not normalised to a date type to preserve original granularity. |
| `publisher` | `string` | Publisher name for the edition referenced on the Goodreads page. `null` if not available. |
| `language_code` | `string` | Language of the edition (e.g. `"eng"`, `"en-US"`). |
| `num_pages` | `int64` | Page count for the edition. (0 if not available or bad scraping). |
| `description` | `string` | Full book description text from the Goodreads page, lowercased and cleaned. |
| `genres` | `string` | Comma-separated list of genre tags (e.g. `"fiction"`, `"classics"`). `null` if not available. |
| `format` | `string` | Publication format type (e.g. `"hardcover"`, `"ebook"`). `null` if not available. |
| `series` | `string` | Name of the literary series, if applicable. `null` if standalone. |
| `num_currently_reading` | `int64` | Number of Goodreads users who had this book marked as "currently reading" at scrape time. Engagement/opularity/demand signal. (0 if missing) |
| `num_want_to_read` | `int64` | Number of Goodreads users who had this book on their "want to read" list at scrape time. Engagement/opularity/demand signal. (0 if missing) |

### Data Splits

This dataset has no predefined train/validation/test split. 
All records are provided as a single collection partitioned across numbered Parquet files (`books-part1.parquet`, `books-part2.parquet`, etc.). Users should define their own splits depending on the downstream task.

### Example Row

```text
book_id                     : "1"
url                         : "https://www.goodreads.com/book/show/1"
title                       : "harry potter and the half-blood prince"
first_author                : "j.k. rowling"
first_author_url            : "https://www.goodreads.com/author/show/1077326.J_K_Rowling"
first_author_num_books      : 743
first_author_num_followers  : 235611
average_rating              : 4.58
num_reviews                 : 73768
first_published             : "july 16, 2005"
publisher                   : "scholastic"
language_code               : "eng"
num_pages                   : 652
description                 : "it is the middle of the summer, ... "
genres                      : "fantasy, fiction, young adult, harry potter, magic, audiobook, children"
format                      : "paperback"
series                      : "harry potter"
num_want_to_read            : 57250
num_currently_reading       : 607895
```

---

## Dataset Creation

### Curation Rationale

Goodreads is a social reading platform hosting over a million user ratings and reviews. 
This dataset was created to enable research into **collective reading preferences**, **rating dynamics**, and **book popularity patterns** as expressed by a large and diverse online community. 
It provides a real-world, richly annotated corpus suitable for social science research.

### Source Data

All data originates from the public Goodreads web page. No authentication or account was required for access. 
Only publicly visible metadata was collected, and no user-identifiable information is present in this dataset.

### Annotations

This dataset contains **no manual annotations**. 
All labels and values are scraped directly from Goodreads pages and reflect the community-aggregated state of the platform at the time of collection (July 2026).

---

## Considerations for Using this Dataset

### Limitations

- **Sampling bias from JSON-LD gating**

Some valid Goodreads book IDs are systematically excluded from this dataset simply because their pages lack JSON-LD, independent of genre or
content quality.
This may introduce subtle selection bias toward more "standard" or actively maintained Goodreads listings.

- **Snapshot in time** 

Data was scraped in July 2026. Books which are added after this date aren't taken into account.
Additionally, metrics like `first_author_num_books`, `first_author_num_followers`, `num_reviews`, `num_want_to_read`, and `num_currently_reading` change continuously on the live platform.

- **Parsing coverage**

Only pages with valid JSON-LD structured data are included in this dataset; pages lacking JSON-LD are skipped and replaced with a different randomly
sampled book ID, so they never appear as rows here.
For pages that pass this JSON-LD gate, secondary fields sourced from `__NEXT_DATA__` or raw HTML (description, genres, format, series, publisher, num_pages) may still
be `null` or `0` or `1` if those specific selectors fail to match.

- **Author enrichment** 

Only the **first listed author** is enriched with follower and book count data.
Co-authors, illustrators, and secondary authors are not captured.

- **No ratings distribution** 

Individual star ratings (1★ to 5★ breakdown counts) are not included - only the aggregated `average_rating` is captured.

- **Social Impact**

Findings derived from this data may not be representative of global reading preferences.

### Licensing & Terms

This dataset was collected from publicly accessible Goodreads pages for **research and educational purposes**. 
Users of this dataset are responsible for complying with Goodreads' [Terms of Service](https://www.goodreads.com/about/terms). 

**Do not use this dataset for commercial purposes without verifying applicable terms.**

---

## Citation

If you use this dataset in your research or projects, please cite it as:

```
@dataset{pfaha_goodreads_books_2026,
  author    = {pfaha},
  title     = {Goodreads Books},
  year      = {2026},
  month     = {July},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/pfaha/goodreads-books}
}
```