The data-exploration GitHub includes several Jupyter notebooks and other files that contain tutorials on how to use the API, with example scripts. The tutorials are loosely categorized by topic. Under each heading, you can find details on what the tutorial documents contain, the assumed background knowledge, and possible applications of the code provided.

Almost all of these documents are Jupyter notebooks, which is a programming environment that runs in a web browser. A notebook can contain both text in a markdown format and Python code that can be run directly as part of the workflow.

If this is your first time using Jupyter notebooks, here are a couple great tutorials online to help you install and set up the software:

Also, remember that the applications listed are only a few examples intended as a starting point! The possibilities are far wider. We’re excited to see what you do with the collections.

General Search and Query

JSON API Overview

An overview of how to retrieve information in a JSON format from the Library of Congress API. This tutorial sets a baseline for doing powerful data retrieval and visualization projects.

Background knowledge:

  • Understand URLs for API requests and how to modify them



Brief guide to searching directly from a web browser.

Background knowledge: None

Applications: Searching the website


Describes Sitemaps and how to get information about the frequency of page updates.

Background knowledge: None, but understanding the basics of Sitemaps and how the formatting works is recommended


  • Determining how often collections or parts of the website are updated
  • Finding the number of items in a collection or sub-items on a page of the site


Accessing Images for Analysis

How to access, display, and download images in bulk. Also provides information about what metadata is available and how to get particular details.

This tutorial is for accessing images directly via the the API, so the images are generally smaller (150 px on one side) and low resolution. For accessing larger images that can be manipulated (size, rotation, crop, etc.), see the next tutorial, on IIIF.

Background knowledge:

  • Understand URLs for API requests and how to modify them


  • Find URLs for images
  • Download images in bulk
  • Get information about the images, such as copyright and usage details, dates, locations, etc.


How to scale, rotate, reflect, crop, and otherwise manipulate images using the IIIF API.

IIIF stands for the International Image Interoperability Framework. It is a standardized way to get images that is used by various libraries, museums, digital archives, etc.

Background knowledge:

  • Where to find the IIIF URLs (can be found in image metadata accessed via the JSON API)
  • (optional) Details of IIIF URL structure


  • Get higher resolution images and manipulate them
  • Display images - individually or in galleries
  • Can also be done in bulk

Image Color Analysis

How to find and analyze the colors in an image. This tutorial uses k-means clustering to analyze and group the pixel values into 6 colors per image, but you can adjust that as needed.

Background knowledge:


Downloading Monographs as Images

Similar to the Accessing Images for Data Analysis notebook — provides code for accessing and downloading images specifically from the Lessing J. Rosenwald Collection.

See background knowledge and applications for the accessing images notebook.

Geographic Data and Maps

Extracting Location Data

Demonstrates how to retrieve geographic data (latitude and longitude) and plot it onto a map. This tutorial focuses on items in the Historic American Engineering Record (HAER). The way that geographic data is stored across collections does vary, so some collections may require more data cleaning and manipulation before doing geographic visualizations.

Background knowledge:


  • Map item locations
  • Analyze geographic data and connect it to other information, such as date
  • Compare geographies across collections

Maps Downloading and Querying

How to query and download cartographic material. Includes:

  • performing bulk downloads of cartographic materials using the API and Python
  • crafting advanced API queries for map content
  • performing post-query filtering

Background knowledge: None, though it may be useful to have some familiarity with Python


  • Download and display images from the collections
  • Create sets of images that can be used in a number of other applications

Maps Metadata

How to find, analyze, and visualize cartographic metadata. This tutorial focuses on metadata associated with the files in the Maps Downloading and Querying tutorial as well as items in the Sanborn Maps collection.

Background knowledge:


  • Search for items within a collection/dataset that have particular locations, dates, etc.
  • Analyze the different parts of the metadata (longest/shortest/average item length, most common dates or locations)
  • Create charts with the data that compare all of the items

Historic Newspapers

Chronicling America

The Chronicling America database also has an API that functions similarly to the wider API. Its URLs start with instead of but the endings for querying are the same.

This tutorial provides an introduction to what information is available via the API — one notable difference is that since this collection consists of newspaper pages, you can retrieve the text from the page (collected using OCR) via the API. It also covers searching for keywords and analyzing data from bulk records.

Background knowledge:

  • Understand URLs for API requests and how to modify them


  • Visualize search results on a map
  • Find certain quotes through time
  • Do historical research
  • See these projects for more!

Chronicling America CSV

The Python script here creates a csv file of all of the digitized titles in Chronicling America, with their associated metadata.

Background knowledge:

  • Command line basics
  • Need Python 3 installed - basic understanding of how Python and the API work needed for modification

Applications: The produced csv can be used for a number of different data analysis and visualization applications. See the Memegenerator and GIPHY tutorials for more on how to use Python code to analyze data in csv file formats.

Chronicling America Issue Counts CSV

These scripts create csv files where each row contains the state name, year, and the number of newspaper issues in the Chronicling America database available in that state and year. Each script does this for one state.

See above (Chronicling America CSV) for background knowledge needed and potential applications.


Memegenerator Metadata

Introduction to csv data analysis in Python using the Memgenerator dataset from this page. Shows how to:

  • find column headers to see what data is available
  • count occurrences within the dataset
  • visualize data in a bar graph
  • retrieve and display images

Background knowledge: None, though it may be useful to have some familiarity with Python

Applications: Metadata

Slightly more advanced csv data analysis in Python using the dataset taken from this page. Shows how to:

  • Get dates for the GIFs
  • Group and visualize the dates
  • Find the GIF file sizes
  • Search the titles in the dataset
  • Download all of the GIFs

Background knowledge: some familiarity with Python and/or data analysis - the Memegenerator tutorial is a good place to start


  • Exploring GIFs
  • Searching through datasets
  • Data analysis and visualization for any dataset

Accessing and Remixing Sound

Provides code for selecting random audio segments and combining them together. Assumes that users have already downloaded the audio files into the same folder as this notebook. The dataset includes 1000 randomly selected audio clips. For more information on how this dataset was generated, see the README.

Background knowledge:


  • Create remixed audio
  • Manipulate existing audio files
  • Build interactive audio sampling tools, like this

American Folklife Center Scripts

Includes 2 bash scripts, and These scripts intend to make processing and data analysis easier by automating some of the initial steps. They can be run on any command line interface (i.e. Terminal for macOS, PowerShell or Command prompt for Windows), and the script will prompt user inputs.

Reportmd - Reports collection records out into CSV, GZ, and XML files.

Processfiles - Does processing to clean up files, including batch file renaming, deleting files, and flattening directories.

Note: These scripts are complex and require background knowledge to fully understand. However, they can be run and used without that background.

Background knowledge:


  • Get data into a different file format
  • Automate some data cleaning/processing
  • Can be modified to work for other datasets and collections outside of the American Folklife Center