add copy edits

index.md

@@ -10,23 +10,6 @@ The Python Intro for Libraries lesson had a major redesign on June 17, 2024. Thi
 
 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.
 
-## Learning Objectives
-
-After attending this training, participants will be able to:
-
-- Navigate the JupyterLab interface and run Python cells within a notebook.
-- Assign values to variables, identify data types, and display values in a Jupyter Notebook.
-- Create and manipulate lists in Python, including indexing, slicing, appending, and removing items to manage data collections effectively.
-- Call built-in Python functions, and use the help function to understand their usage and troubleshoot errors.
-- Use Python libraries like Pandas to import modules, load tabular data from CSV files, and perform basic data analysis.
-- Apply for loops to iterate over collections, using the accumulator pattern to aggregate values and trace variable states to predict loop outcomes.
-- Manipulate pandas DataFrames to select data, calculate summary statistics, sort data, and save results in various formats, demonstrating basic data handling and analysis proficiency.
-- Write Python programs using conditional logic with if, elif, and else statements, including Boolean expressions and compound conditions within loops.
-- Construct Python functions that encapsulate tasks, manage parameters, local, and global variables, and return values to enhance code modularity and readability.
-- Transform complex datasets into a tidy format using pandas functions like melt() for reshaping, group by () for aggregation, and to_datetime() for date handling. Address practical challenges and demonstrate the benefits of tidy data for analysis.
-- 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.
-- Prepare for advanced Python topics such as web scraping and APIs.
-
 ::::::::::::::::::::::::::::::::::::::::::  prereq
 
 ## Prerequisites
@@ -36,8 +19,24 @@ After attending this training, participants will be able to:
 
 2. 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.
+  Please see <a href="#installing-python-using-anaconda">setup instructions below</a> for details.
 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
+## Learning Objectives
+
+After attending this training, participants will be able to:
+
+- Navigate the JupyterLab interface and run Python cells within a notebook.
+- Assign values to variables, identify data types, and display values in a Jupyter Notebook.
+- Create and manipulate lists in Python, including indexing, slicing, appending, and removing items to manage data collections effectively.
+- Call built-in Python functions, and use the help function to understand their usage and troubleshoot errors.
+- Use Python libraries like Pandas to import modules, load tabular data from CSV files, and perform basic data analysis.
+- Apply 'for' loops to iterate over collections, using the accumulator pattern to aggregate values and trace variable states to predict loop outcomes.
+- Manipulate pandas DataFrames to select data, calculate summary statistics, sort data, and save results in various formats, demonstrating basic data handling and analysis proficiency.
+- Write Python programs using conditional logic with 'if', 'elif', and 'else' statements, including Boolean expressions and compound conditions within loops.
+- Construct Python functions that encapsulate tasks, manage parameters, local, and global variables, and return values to enhance code modularity and readability.
+- 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.
+- 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.
+- Prepare for advanced Python topics such as web scraping and APIs.