Python Introduction: Lesson Design

Pacing and overall schedule

• Pacing: The modularity of the lesson allows instructors to select the amount of content appropriate to the needs of the learners.
• Each module has 15 minutes of stretch content which may be optionally included.
• There is an additional 30 minutes built into the startup time to accomodate delays.

Possible paths through the lesson

Slow morning OR afternoon

• Startup + standard library
• Startup + pandas

Average speed morning OR afternoon

• Startup + standard library + pandas
• Startup + standard library + standard library stretch
• Startup + pandas + pandas stretch

Full day option

• Startup + standard library + standard library stretch + lunch + pandas + pandas stretch

Modules and suggested timing

Startup (30 min)

• 9:00 Late buffer
• 9:15 Welcome
  • Code of conduct
  • Room logistics
  • Install/setup
• 9:30 What is Python
  • About Python and its uses in libraries
  • Overview of the lesson

Standard library (2 hours 30 min)

• 9:45: Understanding the environment and basic Python types and methods (15 min)
  • Familiarization with the IDE
  • Starting/stopping the IDE run or app
  • Print statements & evaluation
  • Strings
  • Variables
• 10:00: Introduction to scripts (15 min)
  • Examine an example script
  • Tracing the control flow
  • Using help commands to look up functions
  • Anticipate output and test
  • Modify output and test
• 10:15: csv module (15 min)
  • Using the csv module to open a file
  • Two-dimensional list data representation
  • if statements, for filtering
• 10:30: Coffee (15 min)
• 10:45: Working with data from the csv (15 min)
  • Accumulator patterns, for collecting strings from the data
  • Filtering rows and creating lists from the data
• 11:00 (stretch content) (15 min)
  • Accumulating lists of data for csv output
  • Transforming a script to run on multiple files using glob

Pandas (1 hour)

• 11:15 Introduction to pandas and Jupyter (15 min)
  • Importing pandas
  • How pandas is often used
  • Using Jupyter notebooks
  • Jupyter gotchas and good practices
• 11:30 Reading a csv into a DataFrame (15 min)
  • read_csv and formatting options
  • Viewing a DataFrame
  • Evaluation vs. print() in Jupyter
  • Modifying data using pandas
• 11:45 Working with DataFrames (15 min)
  • Column data types
  • NaNs and essential cleaning checks
  • Filtering data into a new DataFrame
  • Saving data as a new CSV file
• 12:00 (stretch content) (15 min)
  • Aggregation and summaries
  • Quick plots of a DataFrame
  • Transformations of a DataFrame, such as transposing

Wrap-Up and Feedback (15 min)