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Data Structures with `collections`

Standard Library

collections Module Tour

`collections` upgrades built-in data structures with specialized versions tuned for real workloads.

Learn the five workhorsesMix with itertools/functools

Workhorses​

collections recipes
TypeUse case
CounterCount occurrences or run frequency analysis
defaultdictGroup items without manual key checks
dequeFast appends/popleft for queues
namedtupleLightweight, immutable records
OrderedDictEquality comparison considers order (regular dicts maintain insertion order since 3.7)
ChainMapView multiple dicts as one

Sample snippet​

from collections import Counter, defaultdict

counts = Counter(["ok", "fail", "ok"])
print(counts.most_common(1))  # [('ok', 2)]

grouped = defaultdict(list)
orders = [{"customer": "A", "item": "x"}, {"customer": "A", "item": "y"}]
for order in orders:
    grouped[order["customer"]].append(order["item"])

Ecosystem tips​

  • Combine deque with itertools sliding windows.
  • Convert namedtuple to dataclasses when you need methods or defaults.
  • Use Counter.most_common() for leaderboard-style logic.

Next up in your learning path

itertools

Compose iterables efficiently.

Companion

functools

Memoize and transform functions around these structures.

Functional

Data structures pillar

Revisit built-in lists/sets/dicts for fundamentals.

Foundation