Day 6 - Introduction to tables

TODO: lengthen class exercise

Skills: 2

Pre-reading: 4.1.1 & 4.1.2

Intro (15 min)

Goal Learn about tabular data, creating tables literally, importing data, extracting rows and cell values.

• Many everyday pieces of data -- like a workout journal, recipe index, or library catalog — are naturally represented as tables, a type of data where there are many rows where each row has the same set of attributes, called columns.
• Tables are values, just like numbers, strings, images, and booleans, and small ones can be directly typed into Pyret as:

  workouts = table: date :: String, activity :: String, duration :: Number
    row: "2025-04-01", "Running", 30
    row: "2025-04-02", "Yoga", 45
    row: "2025-04-03", "Cycling", 60
  end
• Note that after the table: comes a list of columns, with optional type annotations. This is then followed by a sequence of rows, that each must have exactly the columns mentioned at the beginning.
• Since tables are values, they can be the input and output of functions, and can be used in examples. An important detail: when comparing tables for equality (like in test cases) the order of rows matters!
• We can use check: ... end to write a set of tests not associated with a function, and use that to see:

  check:
    table: date :: String, activity :: String, duration :: Number
      row: "2025-04-01", "Running", 30
      row: "2025-04-02", "Yoga", 45
      row: "2025-04-03", "Cycling", 60
    end
    is-not
    table: date :: String, activity :: String, duration :: Number
      row: "2025-04-03", "Cycling", 60
      row: "2025-04-01", "Running", 30
      row: "2025-04-02", "Yoga", 45
    end
  end
• To deal with external files, we need to first include a Pyret piece of functionality that is not enabled by default to handle tables represented as "comma separated values" (CSV) files.
• Then we can use load-table:, rather than table:, and rather than listing the rows, specifying that they come from a csv file (in this case, from a URL, but in HW and lab, often it will be a file in the same project, using csv-table-file).

  include csv
  recipes = load-table:
    title :: String,
    servings :: Number,
    prep-time :: Number
    source: csv-table-url("https://pdi.run/f25-2000-recipes.csv", default-options)
  end
• In addition to printing the whole table (or a prefix, if the table is long), you can extract a row from it by writing table-identifier.row-n(N) for some N. The first row is numbered 0, the last is one minus the number in the table.

  second-workout = workouts.row-n(1)
  # -> Row: date = "2025-04-02", activity = "Yoga", duration = 45
• From a row, you can extract a column values using row-identifier["column-name"], e.g.,

  second-workout["activity"]  # -> "Yoga"
  # or all at once:
  workouts.row-n(1)["duration"]  # -> 45

Class Exercise (40 mins)

• Find a CSV on https://data.boston.gov/, copy the URL, and create a table from it.
• First, make up some columns, but the error should tell you how many there should be (NOTE: This isn't currently the case, but issue tracking it is https://github.com/brownplt/pyret-lang/issues/1779).
• Use the interactions window, .row-n, and the column extraction to explore the data a little bit.
• Check the total number of rows with table-identifier.length(). What happens if you try to do table-identifier.row-n(M) for M that is greater than the total number of rows?
• Similarly, try extracting a column that doesn't exist.

Wrap-up (5 mins)

• Tables are an extremely common and powerful form of data. Today we just learned how to look at them; in the upcoming days we will see how to program with them!