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Day 12 - Extracting columns

Skills: None​

Pre-reading: 5.1.1 (PROBLEM: Google Sheets), 5.1.2, 5.1.3, 5.1.4.1​

Intro (15 min)​

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Basic Statistical Questions, Extracting Columns, and Understanding Lists​

Focus: Introduce basic statistical queries over tables, show how to extract a column from a table, and explain what lists are and how to create them.

1. Introduction & Motivation (10 minutes)​

  • Context:
    Imagine you run a small café that tracks daily sales in a table. Each day’s entry includes the number of coffees sold.
  • Discussion Prompt (Do Now):
    • “What basic statistics might you want to know about your daily coffee sales? (e.g., maximum, average, total)”
    • Have students share ideas.

2. Basic Statistical Questions on a Table (15 minutes)​

  • New Example Table:
    We define a simple table of daily coffee sales. 
    coffee-sales =
      table: day, coffees-sold
        row: "Mon", 45
        row: "Tue", 30
        row: "Wed", 55
        row: "Thu", 40
        row: "Fri", 60
      end
  • Statistical Questions:
    • Largest number sold on a day (maximum)
    • Average coffees sold per day (mean)
    • Total coffees sold (sum)
  • Interactive Coding:
    • Ask students: “How would you compute the average number of coffees sold?”
    • Introduce Pyret’s math/statistics libraries: 
      import math as M
      import statistics as S

      sales = coffee-sales.get-column("coffees-sold")
      M.max(sales)      // maximum sales
      S.mean(sales)     // average sales
      M.sum(sales)      // total coffees sold
    • NEED TO DISCUSS import, module names, etc.
  • Discussion:
    • Which statistic might best help you plan staffing on a busy day?

3. Extracting a Column & Transition to Lists (15 minutes)​

  • Extracting the Column:
    • Use the function get-column: 
      coffee-sales.get-column("coffees-sold")
    • Show that the result is printed as: 
      [list: 45, 30, 55, 40, 60]
  • Discussion:
    • How does a list differ from a table? (Lists have order but no column header, and only single value per entry.)
  • Creating Literal Lists:
    • Explain that lists can be defined directly: 
      sample-list = [list: 10, 20, 30]
      empty-list = [list: ]
    • Do Now:
      • Ask: “How would you create a list of weekdays? (Answer: [list: "Mon", "Tue", "Wed", "Thu", "Fri"])”

4. Wrap-Up & Reflection (10 minutes)​

  • Recap Key Points:
    • How to ask basic statistical questions on a table.
    • How to extract a column to obtain a list.
    • The notion that a list is an ordered, anonymous collection of values.