Dictionaries as Tables

Frequency Count

Count how many times each label appears in a sequence, building a frequency table as a dict. The replay shows counts seeding a new key at 1 on the first occurrence of each label and incrementing it on every repeat.

By hand

The Pythonic way

Counter builds the frequency table in one call and sorts by frequency in .most_common(), making it easy to see the most frequent labels at a glance.

naive.py 
labels = ['b', 'r', 'g', 'b', 'r', 'b', 'g', 'r', 'b']
counts = {}
for x in labels:
    if x in counts:
        counts[x] = counts[x] + 1
    else:
        counts[x] = 1
print('RESULT:', {k: counts[k] for k in sorted(counts)})
library.py 
from collections import Counter
labels = ['b', 'r', 'g', 'b', 'r', 'b', 'g', 'r', 'b']
counts = Counter(labels)
top = counts.most_common()
print('most common:', top)
print('RESULT:', {k: counts[k] for k in sorted(counts)})

most common: [('b', 4), ('r', 3), ('g', 2)]
RESULT: {'b': 4, 'g': 2, 'r': 3}

Implementation notes

  • This lesson shares its mechanism with python-data-basics/group-count (ch04). The distinction is framing: group-count aggregates a value column per category; frequency-count builds a frequency table over a flat label sequence. The hand-written pattern is identical in both cases.
  • Counter is a subclass of dict, so counts[k] works just like a regular dict lookup after construction.
  • .most_common() with no argument returns all entries sorted by count descending; .most_common(n) returns only the top n.
  • The if x in counts / else pattern is equivalent to counts[x] = counts.get(x, 0) + 1 — both are common; the explicit branch makes the seed-then-increment logic visible in the trace.