Recitation 5 -- Lists

Skills: 3

Reading: 5.1, 5.2

Objectives

• List literals
• Math operations on lists: max, sum, mean
• Transforming lists with map & lambda

I. Introduction (5 minutes)

Overview: Explain that students will learn to work with lists—ordered collections of data that can be extracted from table columns or created directly. They'll use built-in mathematical operations and transformations to analyze cafe sales data and other real-world scenarios.

II. Extracting Lists from Tables (15 minutes)

A. The Cafe Sales Dataset

Present students with this cafe tracking data:

cafe-data =
  table: day, drinks-sold
    row: "Mon", 45
    row: "Tue", 30
    row: "Wed", 55
    row: "Thu", 40
    row: "Fri", 60
  end

Discussion: Explain that we often want to ask statistical questions like:

• What's the maximum number of drinks sold?
• What's the average sold per day?
• What's the total sold this week?

B. Extracting Columns as Lists

Show students how to extract data for analysis:

import math as M
import statistics as S

sales = cafe-data.get-column("drinks-sold")

Check Understanding: Ask, "What does the extracted list look like? How is it different from a table?"

C. Mathematical Operations on Lists

Demonstrate basic operations:

M.max(sales) # Find maximum sales day
M.min(sales) # Find minimum sales day  
S.mean(sales) # Calculate average daily sales
M.sum(sales) # Calculate total weekly sales

III. Creating List Literals (10 minutes)

A. Direct List Creation

Show students how to create lists without tables:

test-scores = [list: 85, 92, 78, 96, 88, 91]
empty-list = [list: ]
names = [list: "Alice", "Bob", "Carol"]

Check Understanding: Ask, "What types of data can lists contain?"

B. Practice with Basic Operations

Have students work with their test scores:

highest = M.max(test-scores)

average = S.mean(test-scores)

total = M.sum(test-scores)

IV. Transforming Lists with Map (10 minutes)

Introduction to Map

Introduce the concept of transforming every element:

import lists as L

temps-celsius = [list: 0, 10, 20, 30, 40]

fun celsius-to-fahrenheit(c):
  (c * 9/5) + 32
end

temps-fahrenheit = L.map(celsius-to-fahrenheit, temps-celsius)

Check Understanding: Ask, "What does map do to each element in the list?"

V. Combining Operations (15 minutes)

A. Working with Student Grade Data

Present a more complex scenario:

raw-scores = [list: 78, 85, 92, 67, 88, 94, 73, 81, 90, 86]

# Apply a 5-point curve to all scores
curved-scores = L.map(lam(score): score + 5 end, raw-scores)

letter-grades = L.map(lam(score):
  if score >= 90: "A"
  else if score >= 80: "B"
  else if score >= 70: "C"
  else if score >= 60: "D"
  else: "F"
  end
end, curved-scores)

Check Understanding: Ask, "How many students got each letter grade?"

B. Filtering and Distinct Operations

Introduce additional list operations (filter and distinct):

# Find unique letter grades assigned
unique-grades = L.distinct(letter-grades)

# Filter to find only passing scores
passing-scores = L.filter(lam(score): score >= 60 end, curved-scores)

# Count how many students passed
num-passing = passing-scores.length()

VI. Recap and Wrap-Up (5 minutes)

Key Points Discussion:

• Lists vs Tables: Lists are single-column, ordered collections
• Mathematical Operations: Built-in functions for statistical analysis
• Map Transformations: Apply functions to every element
• Filter Operations: Remove elements that don't meet criteria
• Distinct Operations: Remove duplicates to find unique values in a list

Reflection Questions:

• "When would you use a list instead of a table?"
• "What real-world data could you analyze using these list operations?"