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computationalthinking-week6

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Deep study for Quiz 2 week 6. Tables organize records in rows; dictionaries map keys to values for fast lookup and counting.

Week map

Row/column table → record as dict → key-value lookup → frequency tally → table vs dict tradeoff.

Table notation

Row → one record → e.g. student name + score + section.
Column → one field across rows → all scores, all names.
Cell → intersection of row and column → single value.
Header → column labels → keys when converting to dict.

Mini-table:

Name	Score
Ada	88
Ben	92

Row 1 cell (Ada, Score) = 88. Column Score = {88, 92}.

List of records (table as data)


text
table ← [
  {Name: Ada, Score: 88},
  {Name: Ben, Score: 92}
]

Access row 0 field Score → 88.

Dictionary notation

Key → unique identifier → string, number, tuple.
Value → data stored → any type.
Lookup → given key, retrieve value in $O(1)$ average time (conceptually constant).
map[key] ← value → insert or update.

Mini-example: frequency of letters in "aba":


text
freq ← empty map
for each character c in string:
    if c in freq:
        freq[c] ← freq[c] + 1
    else:
        freq[c] ← 1

Result: {a: 2, b: 1}.

Pattern families

Easy — Read table cell

From small grid or list-of-dicts, fetch one value. Identify row vs column.

Medium — Build dict from table

Convert parallel columns to records. Count occurrences. Lookup by key; handle missing key.

Hard — Aggregate with dict

Group rows by category key. Sum or average values per group. Merge two tables on shared key.

Worked mini-examples

Example 1 — Row access.

Three rows, columns (ID, Qty): row 2 ID cell = value in column ID at index 1 (0-based).

Example 2 — Dict lookup.

phone["Ana"] = "555-0100". Lookup "Ana" → number. Lookup missing key → error unless default policy stated.

Example 3 — Frequency.

Items [red, blue, red, green]: freq red=2, blue=1, green=1.

Example 4 — Dict of lists (column store).


text
data.Name ← [Ada, Ben]
data.Score ← [88, 92]

Column Name row index 1 → Ben.

Example 5 — Update vs insert.

If key exists, overwrite value; dict size unchanged. New key increases size by 1.

Traps

Row index vs ID column value — row 3 ≠ ID 3 unless stated.
Missing key in dict — define behavior (0, null, error).
Counting rows vs counting distinct keys.
Assuming table sorted unless specified.
Confusing list index with dict key.

Diagnostic (try yourself)

In a 4-row table with columns A and B, what does cell (row 3, B) mean?
Map {x: 10, y: 20}. What is value at x? What happens at z without default?
Count how many times "the" appears in word list [the, cat, the, sat].
Represent the mini-table above as one dict mapping Name → Score.
When is dict better than scanning entire table for each lookup?