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Source : db507fc
Branch : main
Author : Tim Dennis <tdennis@library.ucla.edu>
Time : 2026-09-03 01:28:41 +0000
Message : Merge pull request #177 from LibraryCarpentry/learning_obj
Refine learning objectives in index.md
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@@ -8,7 +8,7 @@ The Python Intro for Libraries lesson had a major redesign on June 17, 2024. Thi
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This lesson is an introduction to programming in Python for library and information workers with little or no previous programming experience. 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.
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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.
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:::::::::::::::::::::::::::::::::::::::::: prereq
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After attending this training, participants will be able to:
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- Navigate the JupyterLab interface and run Python cells within a notebook.
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- Assign values to variables, identify data types, and display values in a Jupyter Notebook.
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- Create and manipulate lists in Python, including indexing, slicing, appending, and removing items to manage data collections effectively.
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- Call built-in Python functions, and use the help function to understand their usage and troubleshoot errors.
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- Use Python libraries like Pandas to import modules, load tabular data from CSV files, and perform basic data analysis.
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- Apply 'for' loops to iterate over collections, using the accumulator pattern to aggregate values and trace variable states to predict loop outcomes.
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- Manipulate pandas DataFrames to select data, calculate summary statistics, sort data, and save results in various formats, demonstrating basic data handling and analysis proficiency.
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- Write Python programs using conditional logic with 'if', 'elif', and 'else' statements, including Boolean expressions and compound conditions within loops.
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- Construct Python functions that encapsulate tasks, manage parameters, local, and global variables, and return values to enhance code modularity and readability.
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- Transform complex datasets into a tidy format using pandas functions like 'melt()' for reshaping, 'groupby()' for aggregation, and 'to_datetime()' for date handling. Address practical challenges and demonstrate the benefits of tidy data for analysis.
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- Create and customize data visualizations using Pandas and Plotly, generating various plot types (line, area, bar, histogram) to analyze trends and draw insights from time-series data.
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- Prepare for advanced Python topics such as web scraping and APIs.
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- Learn core Python syntax, variables, data types, and functions while using the JupyterLab interface.
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- Control program execution using lists, loops, conditional logic, and custom functions to automate data tasks.
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- Load, clean, aggregate, and reshape tabular datasets using the Pandas library to create analysis-ready data.
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- Generate insightful charts from tidy datasets using Pandas and Plotly.
This is a placeholder file. Please add content here.
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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.
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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.
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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.
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Mateo works with digital collection metadata and knows XML inside out, but spreadsheets are no longer cutting it for his huge batch cleanup jobs. Building a foundation in Python logic and functions will give him the exact leverage he needs for heavy data wrangling and future API integration.
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## Glossary
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FIXME This is a placeholder file. Please add content here.
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Please refer to the [Carpentries' Glosario project](http://glosario.carpentries.org/), a multilingual glossary for computing and data science terms, for definitions of terms related to Python, programming, and data organization/analysis throughout this lesson.
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2. Unzip the ```data.zip``` file, which should create a new folder called ```data```.
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3. Create a new folder on your Desktop called ```lc-python``` and put the ```data``` folder in this folder.
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This lesson uses circulation data in multiple CSV files from the Chicago Public Library system. The data was compiled from records shared by the Chicago Public Library in [the data.gov catalog](https://catalog.data.gov/dataset/?q=chicago+%22circulation+by+location%22). Please do not download the circulation data from data.gov since the dataset you downloaded following the steps above has been altered for our purposes.
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This lesson uses circulation data in multiple CSV files from the Chicago Public Library system. The data was compiled from records shared by the Chicago Public Library via data.cityofchicago.org, and available in the [the data.gov catalog](https://catalog.data.gov/?q=Libraries+Circulation+by+Location&sort=relevance). The original data is in the public domain with an [open license](https://resources.data.gov/open-licenses/) which is defined as "any legally binding instrument that grants permission to access, re-use, and redistribute a work with few or no restrictions." The CSV files used in this lesson were modified for ease of use and teaching purposes.
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