| permalink | index.html |
|---|---|
| site | sandpaper::sandpaper_site |
::::::::::::::::::::::::::::::::::::: instructor
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.
:::::::::::::::::::::::::::::::::::::::::::::::::
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 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.
:::::::::::::::::::::::::::::::::::::::::: prereq
-
Learners need to understand what files and directories are and what a working directory is.
-
Learners must install Python and JupyterLab, and download the dataset that will be used in the lesson, before the workshop begins.
Please see setup instructions below for details.
::::::::::::::::::::::::::::::::::::::::::::::::::
After attending this training, participants will be able to:
- Learn core Python syntax, variables, data types, and functions while using the JupyterLab interface.
- Control program execution using lists, loops, conditional logic, and custom functions to automate data tasks.
- Load, clean, aggregate, and reshape tabular datasets using the Pandas library to create analysis-ready data.
- Generate insightful charts from tidy datasets using Pandas and Plotly.