collections for better data structures

collections contains data structures that often fit real problems better than plain lists or dictionaries. A few tools in this module can make code both shorter and clearer.

Why it is useful

The most practical tools here are:

• Counter for frequency counting
• defaultdict for grouping and accumulating
• deque for efficient append and pop operations at both ends
• namedtuple when a lightweight structured record is enough

Each one makes the intended data shape more obvious.

A few high-value patterns

Counting:

from collections import Counter

counts = Counter(["a", "b", "a"])
print(counts["a"])

Grouping:

from collections import defaultdict

groups = defaultdict(list)
groups["python"].append("tips")

Queue-like behavior:

from collections import deque

items = deque([1, 2, 3])
items.appendleft(0)
print(items.popleft())

When not to use it

Do not reach for these types just because they exist. Use them when they match the actual job better than a normal list or dictionary.

Rules of thumb

• Use Counter for counts, not hand-rolled dictionaries.
• Use defaultdict when missing-key setup keeps repeating.
• Use deque when operations at the left side matter.
• Let the data structure communicate the task.