clarify scope to emphasize circulation dataset

episodes/libraries.md

@@ -25,7 +25,7 @@ exercises: 10
 
 ## Python libraries are powerful collections of tools.
 
-A *Python library* is a collection of files (called *modules*) that contains functions that you can use in your programs. Some libraries (also referred to as packages) contain standard data values or language resources that you can reference in your code. So far, we have used the Python [standard library][stdlib], which is an extensive suite of built-in modules. You can find additional libraries from [PyPI][pypi] (the Python Package Index), though you'll often find references to useful libraries as you're reading tutorials or trying to solve specific programming problems. Some popular libraries for working with data in library fields are:
+A *Python library* is a collection of files (called *modules*) that contains functions that you can use in your programs. Some libraries (also referred to as packages) contain standard data values or language resources that you can reference in your code. So far, we have used the Python [standard library][stdlib], which is an extensive suite of built-in modules. You can find additional libraries from [PyPI][pypi] (the Python Package Index), though you'll often find references to useful libraries as you're reading tutorials or trying to solve specific programming problems. Though we do not have time to cover these all in this lesson, some popular libraries for working with data in library fields are:
 
 - [Pandas](https://pandas.pydata.org/) - tabular data analysis tool.
 - [Pymarc](https://pypi.org/project/pymarc/) - for working with bibliographic data encoded in MARC21.

index.md

@@ -3,12 +3,11 @@
 site: sandpaper::sandpaper_site
 ---
 ::::::::::::::::::::::::::::::::::::: instructor
-## Major lesson update: June 17, 2024
-The Python Intro for Libraries lesson had a major redesign on June 17, 2024. This new Python lesson features a different dataset (of library usage data), uses JupyterLab instead of Spyder, and most of the content was rewritten. If you were familiar with the previous version of the lesson and are planning to teach it again, please give yourself time to review the lesson in full as your prepare.
-
+## Lesson scope
+The Python Intro for Libraries lesson focuses on library circulation data to introduce Python skills and concepts. Those Python fundamentals should be relevant for many other library use-cases, but the lesson does not provide direct examples of using Python with other relevant library data formats such as XML, JSON, or MARC. 
 :::::::::::::::::::::::::::::::::::::::::::::::::
 
-This lesson is an introduction to programming in Python for library and information workers with little or no previous programming experience (see the [Learner Profiles](profiles.html) for examples of the kinds of people who might benefit from this lesson). It uses examples that are relevant to a range of library use cases, and is designed as a prerequisite for other Python lessons that will be developed in the future (e.g., web scraping, APIs). The lesson uses the JupyterLab computing environment and Python 3.
+This lesson is an introduction to programming in Python for library and information workers with little or no previous programming experience (see the [Learner Profiles](profiles.html) for examples of the kinds of people who might benefit from this lesson). It uses library circulation data to introduce Python fundamentals, and is designed as a prerequisite for other Python lessons that we hope will be developed in the future (e.g., web scraping, APIs). The lesson uses the JupyterLab computing environment and Python 3.
 
 ::::::::::::::::::::::::::::::::::::::::::  prereq
 

profiles/learner-profiles.md

@@ -4,6 +4,8 @@ title: LC Python Learner Profiles
 
 Sarah runs circulation services at a large public library system. She's handy with ILS exports, but compiling monthly stats across dozens of branches still takes hours of tedious Excel copy-pasting. Learning Python loops and Pandas will help her aggregate those branch CSVs automatically so she can streamline her monthly reporting.
 
+While this lesson doesn't directly address other Python use-cases in libraries, the skills learned here should also be relevant for the following kinds of Python users: 
+
 David is a community college assessment librarian who feels confident in spreadsheets, but has never written code. He's currently stuck wrestling with years of messy gate counts and inconsistent vendor stats, making this lesson's focus on JupyterLab and Pandas perfect for learning how to tidy up and plot his data.
 
 Ling advises university faculty and students on data management, but doesn't write code herself. Mastering Python basics like lists, functions, and built-in help will allow her to read her researchers' scripts with confidence and run quick exploratory checks on repository datasets.