Day 13 - Operating on Lists

Skills: None

Pre-reading: 5.1.4 (aside from 5.1.4.1)

FIXME

Operating on Lists and Transforming Data

Focus: Work with built‑in operations on lists—computing statistics on numeric lists and using functions like filter, map, distinct, and getting elements by position.

1. Warm-Up & Recap (10 minutes)

• Recap:
  • Quick review of what lists are from the previous lecture.
• Do Now:
  • “Imagine you have a list of recipe ratings (numbers). What might you want to know? (e.g., highest rating, average rating)”
  • Share ideas with the class.

2. Built‑In Operations on Numeric Lists (15 minutes)

• New Example: Consider a table for recipe reviews:

  reviews =
    table: recipe, rating
      row: "Pasta", 4.5
      row: "Salad", 3.8
      row: "Soup", 4.2
      row: "Sandwich", 5.0
      row: "Cake", 4.9
    end
• Extracting the Numeric Column:

  ratings = reviews.get-column("rating")
• Computations:
  • Use math/statistics libraries:

    import math as M
    import statistics as S
    
    M.max(ratings)      // highest rating
    S.mean(ratings)     // average rating
    M.sum(ratings)      // total of ratings
• Interactive Exercise:
  • Ask: “What is the median rating?”
  • Have students compute using S.median(ratings).
• Discussion:
  • How could these statistics inform decisions (e.g., which recipe is most popular)?

3. Built‑In Operations on Lists of Strings (15 minutes)

• Discount Codes in a Food Delivery App: Imagine a list of discount codes used:

  discount-codes = [list: "NEWYEAR", "student", "NONE", "student", "VIP", "none"]
• Finding Unique Values:
  • Use the lists library:

    import lists as L
    
    unique-codes = L.distinct(discount-codes)
  • Show the result (e.g., [list: "NEWYEAR", "student", "NONE", "VIP", "none"]).
• Filtering:
  • Remove codes that represent no discount (in this case, normalized "none").
  • Define a helper function:

    fun is-real-code(code :: String) -> Boolean:
      not(string-to-lower(code) == "none")
    end
    
    real-codes = L.filter(is-real-code, unique-codes)
  • Do Now:
    • Ask: “What is the length of the resulting list?” (Hint: Use L.length(real-codes).)
• Getting Elements by Position:
  • Like how tables have row-n(), lists have a way of getting an element by position:

    first-code = real-codes.get(0)
  • Discussion:
    • Explain zero-based indexing and why get(0) returns the first element.
• Transforming Lists with Map:
  • Extract the lowercase versions of each discount code:

    lower-codes = L.map(lam(code): string-to-lower(code) end, codes-real)
  • Ask students to predict the output.
  • L.map is similar to how transform-column worked to update a column in a table, but since a list is just a single sequence of values, it updates the whole thing.

4. Wrap-Up & Reflection (10 minutes)

• Recap Key Operations:
  • Filter, distinct, length, get, and map.
• Interactive Question:
  • “How would you remove all ingredients that are completely lowercase from a list of ingredients?”
  • (Set up for next lecture’s custom list functions.)