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Counter and defaultdict

The collections module has tools that solve common data tasks with less code and fewer edge cases. Two of the most useful are Counter for frequency counting and defaultdict for grouping or accumulating values.

Why this matters​

Many counting and grouping problems start with manual dictionary setup:

counts = {}

for word in words:
    if word not in counts:
        counts[word] = 0
    counts[word] += 1

This works, but Python already has tools built for exactly this pattern.

Use Counter for counting​

from collections import Counter

counts = Counter(words)
print(counts["python"])

Counter makes it obvious that the real task is frequency counting.

It also gives you helpful operations such as most common items:

from collections import Counter

counts = Counter(["a", "b", "a", "c", "a"])
print(counts.most_common(2))

Output:

[('a', 3), ('b', 1)]

Use defaultdict for grouping​

When the real task is "collect several values under the same key," defaultdict removes repeated setup:

from collections import defaultdict

groups = defaultdict(list)

for user, team in memberships:
    groups[team].append(user)

This is often clearer than checking whether each key already exists.

Prefer the tool that matches the task​

These types help because they make your intent visible:

  • Counter means counting
  • defaultdict(list) means grouping
  • defaultdict(int) means accumulating numeric totals

That clarity is usually as valuable as the shorter code.

Rules of thumb​

  • Reach for Counter when the task is frequency counting.
  • Reach for defaultdict when you keep initializing missing keys.
  • Let the data structure describe the job.
  • Prefer these tools over hand-rolled dictionary setup for common patterns.