collections.md

Python by Example: Collections

The collections module provides specialized data structures for common patterns. Counter counts occurrences; defaultdict supplies a default value for missing keys; deque is a fast double-ended queue. All three solve problems that plain lists and dicts handle awkwardly.

What you'll learn:

• Counter for tallying items
• defaultdict for grouping without KeyError
• deque for fast appends and pops from both ends

from collections import Counter, defaultdict, deque


# Counter tallies occurrences
words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
counts = Counter(words)
print(counts)
print(counts["apple"])
print(counts.most_common(2))


# defaultdict avoids KeyError for missing keys
groups = defaultdict(list)
for name, dept in [("Alice", "eng"), ("Bob", "hr"), ("Carol", "eng")]:
    groups[dept].append(name)
print(dict(groups))


# deque supports fast O(1) appends and pops from both ends
d = deque([1, 2, 3])
d.appendleft(0)
d.append(4)
print(d)
d.popleft()
print(d)

Counter also supports arithmetic—add two counters to combine counts. defaultdict(list) is the idiomatic way to group items. deque is better than a list when you frequently add or remove from the front.

To run this program:

$ python source/collections-example.py
Counter({'apple': 3, 'banana': 2, 'cherry': 1})
3
[('apple', 3), ('banana', 2)]
{'eng': ['Alice', 'Carol'], 'hr': ['Bob']}
deque([0, 1, 2, 3, 4])
deque([1, 2, 3, 4])

Tip: Counter("hello") counts characters; Counter(a) + Counter(b) merges two tallies.

Try it: Use Counter to find the three most common words in a paragraph of text.

Source: collections-example.py

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