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Class 13 - Extracting columns

Date: October 7, 2026

⚠️Homework
Homework 4 due October 11, 2026
‼️Ending Skills
Last week for skills: 0
🆕New Skills
First week for skills: 3

Reference

For all work with tables, refer to the Tables page in the menu at the top of the page!

Skills: None​

Notes (review before class)​

Questions About a Column​

So far, table operations have worked by rows and produced tables. But many questions are basic statistics (maximum, minimum, average, median, ...) over a single column, possibly after filtering rows: the largest ticket order, the average or most common number of tickets, the most used discount code. We'll use a cleaned copy of the events data from Class 11 and Class 12:

cleaned_data_raw = load_table("https://raw.githubusercontent.com/neu-pdi/cs2000-public-resources/refs/heads/main/static/support/dcic-events-cleaned.csv")
cleaned_data = cleaned_data_raw.transform_column("Num Tickets", int)

Extracting a Column​

select_columns produces a table, and no table method does these computations:

cleaned_data.select_columns(["Num Tickets"])

The column method produces just the values, as a list:

cleaned_data.column("Num Tickets")  # -> [2, 1, 5, 0, 3, 10, 3]

Lists​

  • A list is a sequence of values outside of a table. Like a one-column table, it has an order (first, ..., last), and its elements should all have the same type.
  • Unlike a column, a list is anonymous (it has no name): the program, not the data, says what it represents, as with a number like 3 (an age? a play count?). Tables, with their column names, are the exception.
  • So we can misinterpret a list, but one operation works on any list, whatever it represents (genericity). Some need numbers (average); others, anything that can be compared, like strings (maximum).
  • Lists are written in square brackets (as table rows have been since Class 7), and can be named. [] is the empty list.
[1, 2, 3]
["This", "is", "a", "list", "of", "words"]
shopping_list = ["muesli", "fiddleheads"]

Statistics on Lists of Numbers​

max, min, and sum are built into Python; more come from Python's statistics library, which we import (as we did math on Class 9):

import statistics as S

ticket_counts = cleaned_data.column("Num Tickets")

max(ticket_counts)       # largest number in a list
sum(ticket_counts)       # sum of numbers in a list
S.mean(ticket_counts)    # mean (average) of numbers in a list
S.median(ticket_counts)  # median of numbers in a list
  • import statistics as S names the library S (our choice), so S.mean is its mean, which can't be confused with built-in functions or yours.
  • With no as (like import math), use the full name: statistics.mean.
  • from statistics import * adds the library's functions to the directory under their own names (mean(ticket_counts)). We usually avoid it: you can't tell where a name came from, and a library name can hide one of yours.
  • Try: find the function in the statistics documentation that gives the most common number of tickets.

Recap (25 mins)​

A small cafe records the number of drinks it sold each day, and wants to know which days were busier than average. Read through the program:

import statistics as S

cafe_data = table(
    ["day", "drinks-sold"],
    [
        ["Mon", 45],
        ["Tue", 30],
        ["Wed", 55],
        ["Thu", 40],
        ["Fri", 60],
    ],
)

sales = cafe_data.column("drinks-sold")
average_sales = S.mean(sales)

def busier_than_average(drinks: int) -> bool:
    """whether a day with drinks sold was busier than the average day"""
    return drinks > average_sales

def test_busier_than_average():
    assert busier_than_average(60)
    assert not busier_than_average(30)
    assert not busier_than_average(46)

busy_days = cafe_data.filter(lambda r: busier_than_average(r["drinks-sold"]))
  • What is sales? What does it lack that cafe_data.select_columns(["drinks-sold"]) has?
  • What is average_sales? Why is there a test with 46? Which days are in busy_days?
  • If the first line were import statistics, which line would have to change, and how?

Class Exercise (30 mins)​

  1. Using the cafe data from the intro, extract the "day" column and determine which day appears first alphabetically using min.

  2. Create a list for the number of drinks, and compute the total drinks sold across all days using sum.

  3. Create a new table of student quiz scores:

    quiz_scores = table(
        ["student", "quiz1", "quiz2", "quiz3"],
        [
            ["Alice", 85, 92, 78],
            ["Bob", 90, 88, 95],
            ["Charlie", 78, 85, 82],
            ["Diana", 95, 90, 88],
        ],
    )

    Extract each quiz column and calculate the class average for each quiz using S.mean. Which quiz had the highest average?

  4. Create a list directly using the syntax [12, 8, 15, 22, 5, 18] and use built-in functions to find the minimum, maximum, and sum. What's the range (difference between max and min)?

  5. Load the employees dataset from https://data.boston.gov/dataset/employee-earnings-report using the CSV URL (as in Class 9). Transform the "REGULAR" column to numbers using the earnings_to_number helper from Class 9, extract it as a list and calculate the average regular salary across all employees.

Wrap-Up (5 mins)​

  • Columns can be extracted from tables -- these are lists.
  • Many built in functions work on lists of values.