GoodreadsScraper

A small Python project to pull data from Goodreads using Scrapy and Selenium

Table of Contents

1. Introduction
2. Installation
3. How To Run
   1. Author Crawls
   2. List Crawls
   3. My Books (Shelf) Crawls
4. Data Enrichment
   1. Cleaning and Aggregating
   2. Extracting Kindle Price
5. Data Schema
   1. Book
   2. Author
6. Note About Temporality
7. [Bonus] Project Ideas
8. Contributing

Introduction

This is a Python + Scrapy (+ Selenium) based web crawler that fetches book and author data from Goodreads. This can be used for collecting a large data set in a short period of time, for a data analysis/visualization project.

With appropriate controls, the crawler can collect metadata for ~50 books per minute (~3000 per hour). If you want to be more aggressive (at the risk of getting your IP blocked by Goodreads), you can set the DOWNLOAD_DELAY to a smaller value in settings.py, but this is not recommended.

Installation

For crawling, install requirements.txt

# Creates a virtual environment
virtualenv gscraper

# This may vary depending on your shell
. gscraper/bin/activate

pip3 install -r requirements.txt

How To Run

Run python3 crawl.py --help for all sub-commands that the CLI offers.

Currently supported subcommands are:

1. author
2. list
3. mybooks

Author Crawls

Run the following command to crawl all authors on the Goodreads website:

python3 crawl.py author

By default, this will store the result to a file called author_all.jl

Use python3 crawl.py author --help for all options and defaults.

List Crawls

Run the following command to crawl all books from the first 50 pages of a Listopia list (say 1.Best_Books_Ever):

python3 crawl.py list \
  --list_name="1.Best_Books_Ever" \
  --start_page=1 \
  --end_page=50 \
  --output_file_suffix="best_001_050"

This will

1. crawl the first 50 pages of this list, which is ~5k books, and
2. store all books in a file called book_best_001_050.jl, and all authors in a file called author_best_001_050.jl.

The paging approach avoids hitting the Goodreads site too heavily. You should also ideally set the DOWNLOAD_DELAY to at least 1.

Use python3 crawl.py list --help for all options and defaults.

My Books (Shelf) Crawls

Run the following command to crawl all books from the read shelf for Emma Watson:

python3 crawl.py my-books \
  --shelf="read" \
  --user_id="50993735-emma-watson"

Use python3 crawl.py my-books --help for all options and defaults.

Data Enrichment

Cleaning and Aggregating

Note that since the output files are in jsonlines (.jl) format, you can simply cat them together into a single jl file...

cat book_*.jl > all_books.jl
cat author_*.jl > all_authors.jl

and load them in for analysis using pandas (not included in requirements.txt):

import pandas as pd

all_books = pd.read_json('all_books.jl', lines=True)
all_authors = pd.read_json('all_authors.jl', lines=True)

Alternatively, you can use the cleanup.py file, which can be used as both a utility and a script.

As a utility, it provides multiple functions that can be used to transform the data into a format that might be more amenable to analysis or visualization.

As a script, it cleans up some of the multivalued attributes, deduplicates rows, and writes it out to the specified CSV file.

python3 cleanup.py \
  --filenames best_books_01_50.jl young_adult_01_50.jl \
  --output goodreads.csv

Extracting Kindle Price

A useful feature is the Kindle price of the book on Amazon. Since this data is populated dynamically on the page, Scrapy is unable to extract it. We now use Selenium to get the Amazon product ID as well as the Kindle price:

# Install selenium, not included in requirements.txt
pip3 install selenium

# Run the Kindle price populator script
python3 populate_kindle_price.py -f goodreads.csv -o goodreads_with_kindle_price.csv

The reason we don't use Selenium for extracting the initial information is because Selenium is slow, since it loads up a browser and works through that. This is only an additional step to make the data slightly richer, but is completely optional.

Now the data are ready to be analyzed, visualized and basically anything else you care to do with it!

Data Schema

Book

* Goodreads distinguishes between authors of the same name by introducing additional spaces between their names, so this column should be treated with special consideration during cleaning.

Author

* In some cases the death date appears to be earlier than the birth date. This is most likely because the dates are BC, and should be inspected to validate this.

** This blurb is most likely incomplete because it is shortened, and the complete version is available only through a Javascript function (which Scrapy is incapable of executing). If this is a desired field, then the URL can be used in conjunction with a library like selenium to extract the entire blurb.

Note About Temporality

Since Goodreads is a dynamic platform, with thousands of users constantly adding/deleting/updating reviews and ratings, the data collected through this scraper are valid at a particular timestamp only. Care must be taken while aggregating and deduplicating these data; in most cases one would want to retain the most recently scraped data, but this may change from a case-to-case basis.

[Bonus] Project Ideas

What can you do with these data? Well, here are a few ideas:

1. Each author has a set of other authors who influenced them, which can be naturally modeled as a directed graph. This graph can then either be visualized, OR one could perform graph analysis (community detection, central figures, determining oldest ancestor influencers, etc)
2. One could perform hypothesis testing to confirm/reject if:
   1. Female authors have the same number of ratings/reviews as male authors
   2. Fantasy novels have a higher average rating than non-fiction novels
3. As mentioned here, Goodreads is a dynamic platform, and thus if one chooses to collect these data periodically, one could generate time-series data, and observe trends for a particular novel/author over time. One could also perform event detection to determine if the author made a breakthrough in their writing career.

Contributing

Fixes and improvements are more than welcome, so raise an issue or send a PR!