New York Times Best Seller Analysis

Project Overview

This project provides data collection and analysis of the New York Times Best Seller lists, tracking trends and patterns in bestselling fiction books over time. The data contains two lists:

  • Hardcover Fiction (1931-present)
  • Combined Print & E-Book Fiction (2011-present)

Key Research Questions

  • How has the NYT bestseller list evolved over time?
  • Which authors and titles have dominated the lists?
  • What patterns can we observe in bestseller performance?
  • How do hardcover bestsellers compare to e-book inclusive listings?

Data Collection

Get the Data

  • Hardcover Fiction (1931-present)
  • Combined Print & E-Book Fiction (2011-present)

Data Sources

This project combines data from multiple sources:

Automated Updates

The dataset is automatically refreshed weekly through GitHub Actions workflows, ensuring the analysis remains current with the latest bestseller information.

Project Navigation

This website contains four main analysis sections:

  1. Hardcover Fiction Data Cleaning - Methodology for preparing the hardcover fiction dataset
  2. Combined List Data Cleaning - Methodology for preparing the combined print and e-book dataset
  3. Hardcover Fiction Analysis - Findings and visualizations from the hardcover fiction dataset
  4. Combined List Analysis - Findings and visualizations from the combined print and e-book dataset

About the Author

This project is maintained by Aislyn Gaddis, a senior journalism major at UT Austin interested in data, newsroom innovation and multimedia journalism. The work began as a class project in spring 2023 for Reporting with Data with Professor Christian McDonald at The University of Texas at Austin and has evolved into an ongoing project. In early 2025, I added GitHub Actions to automatically scrape the new list and update the data each week.

Repository

The complete code and data for this project are available on GitHub.