Norton Anthology & SPL Checkouts Data Exploration (Solution)

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Published

February 25, 2026

Solution

Norton Anthology & SPL Checkouts Data Exploration

Exercise Without Solutions

These exercises explore checkout data from the Seattle Public Library for authors in the post-1945 volumes of the Norton Anthology of American Literature, one of the most widely used literary anthologies in U.S. college classrooms. The dataset was compiled as part of the research paper “The Canon in Circulation” (Gupta et al., 2025). The code and data are available on GitHub, and you can explore more of the analysis here. For more context, see the Library Checkouts for the Top 500 “Greatest” Novels data essay.

Concepts covered:

  • Groupby and aggregation (sum of checkouts)
  • Sorting and ranking (top N values)
  • Horizontal bar charts
  • Filtering by a specific author
  • Time series line plots (monthly checkouts over time)

Load the data

Code 
import pandas as pd
import plotly.express as px

norton_df = pd.read_csv("https://seattle-library-checkout-data.s3.us-west-2.amazonaws.com/norton-anthology_spl-checkouts_2005-2025.csv")
norton_df.head()
/tmp/ipykernel_8165/3604736084.py:4: DtypeWarning:

Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.
CheckoutDate UsageClass CheckoutOutType MaterialType CheckoutYear CheckoutMonth Checkouts Title Creator Subjects Publisher PublicationYear ISBN Normalized.Creator Normalized.Title BirthYear DeathYear Work.In.Norton
0 2016-10-01 Digital OverDrive AUDIOBOOK 2016 10 1 The Natural (Unabridged) Bernard Malamud Classic Literature, Fiction Blackstone Audio, Inc. 2008 NaN Bernard Malamud The Natural 1914 1986.0 False
1 2016-10-01 Digital OverDrive EBOOK 2016 10 1 The Assistant Bernard Malamud Fiction, Literature Macmillan Publishers 2013 NaN Bernard Malamud The Assistant 1914 1986.0 False
2 2016-11-01 Digital OverDrive EBOOK 2016 11 1 The Magic Barrel Bernard Malamud Fiction, Literature Macmillan Publishers 2013 NaN Bernard Malamud The Magic Barrel 1914 1986.0 True
3 2016-11-01 Digital OverDrive EBOOK 2016 11 1 The Assistant Bernard Malamud Fiction, Literature Macmillan Publishers 2013 NaN Bernard Malamud The Assistant 1914 1986.0 False
4 2016-12-01 Digital OverDrive EBOOK 2016 12 1 The Fixer: A Novel Bernard Malamud Fiction, Literature Macmillan Publishers 2013 NaN Bernard Malamud The Fixer 1914 1986.0 False

Exercise 1

Find the top 10 authors by total checkouts in the Norton Anthology SPL checkouts dataset. Create a horizontal bar chart to visualize the results.

Save the top authors as top_authors.

Code 
top_authors = norton_df.groupby('Normalized.Creator')['Checkouts'].sum().nlargest(10).reset_index()
top_authors.columns = ['Author', 'Total Checkouts']

fig = px.bar(top_authors, x='Total Checkouts', y='Author', orientation='h',
             title='Top 10 Norton Anthology Authors by SPL Checkouts')
fig.update_layout(yaxis={'categoryorder': 'total ascending'})
fig.show()

Discuss/consider: Which authors from the Norton Anthology are most popular at the Seattle Public Library? Are these authors you expected to see at the top?

Exercise 2

Find the top 10 titles by total checkouts. Create a horizontal bar chart to visualize the results.

Save the top titles as top_titles.

Code 
top_titles = norton_df.groupby('Normalized.Title')['Checkouts'].sum().nlargest(10).reset_index()
top_titles.columns = ['Title', 'Total Checkouts']

fig = px.bar(top_titles, x='Total Checkouts', y='Title', orientation='h',
             title='Top 10 Norton Anthology Titles by SPL Checkouts')
fig.update_layout(yaxis={'categoryorder': 'total ascending'})
fig.show()

Discuss/consider: Which titles are checked out most frequently? How do these compare to the most popular authors?

Exercise 3

Create a time series line plot showing monthly checkouts for Octavia E. Butler over time.

Filter the data for Octavia E. Butler, group by year and month, and plot the results.

Code 
butler_df = norton_df[norton_df['Normalized.Creator'] == 'Octavia E. Butler']
butler_monthly = butler_df.groupby(['CheckoutYear', 'CheckoutMonth'])['Checkouts'].sum().reset_index()
butler_monthly['date'] = pd.to_datetime(
    butler_monthly['CheckoutYear'].astype(str) + '-' +
    butler_monthly['CheckoutMonth'].astype(str) + '-01')

fig = px.line(butler_monthly.sort_values('date'), x='date', y='Checkouts',
              title='Monthly SPL Checkouts for Octavia E. Butler')
fig.show()

Discuss/consider: What patterns do you notice in the checkout trends for Octavia E. Butler? Are there any notable spikes or dips? What might explain them?