Advanced Python Data Structures for Interviews & Code
Level up your Python with the structures interviewers love — and that clean code relies on.
In this guide
Python's built-ins cover 90% of cases, but the collections and heapq modules hold the structures that make hard problems easy — and make your code read like you've been doing this a while.
defaultdict — no more key checks
python
from collections import defaultdict
# count words without checking for missing keys
counts = defaultdict(int)
for word in "the quick brown fox the fox".split():
counts[word] += 1
# group items by key
groups = defaultdict(list)
for name in ["ann", "bob", "amy", "ben"]:
groups[name[0]].append(name)
print(counts["the"]) # 2 (no KeyError)
print(dict(groups)) # {'a': ['ann','amy'], 'b': ['bob','ben']}Counter — counts and top-N
python
from collections import Counter
c = Counter("abracadabra")
print(c.most_common(3)) # [('a', 5), ('b', 2), ('r', 2)]
print(c["z"]) # 0, not KeyErrordeque — fast both ends
python
from collections import deque
d = deque(["a", "b", "c"])
d.appendleft("z") # O(1)
d.pop() # O(1) from the right
d.append("y")
print(list(d)) # ['z', 'a', 'b', 'y']
# a fixed-size sliding buffer
buf = deque(maxlen=3)
for x in range(5):
buf.append(x)
print(list(buf)) # [2, 3, 4]heapq — always the smallest
python
import heapq
h = []
for x in [5, 2, 9, 1]:
heapq.heappush(h, x)
print(heapq.heappop(h)) # 1 (always the minimum)
# top-3 largest without sorting everything
nums = [5, 2, 9, 1, 7, 3]
print(heapq.nlargest(3, nums)) # [9, 7, 5]namedtuple — self-documenting tuples
python
from collections import namedtuple
Point = namedtuple("Point", "x y")
p = Point(3, 4)
print(p.x, p.y) # readable, and still hashable/immutableHeaps are the answer to any "k largest / k closest / merge k streams" problem — heapq keeps them a one-liner instead of a page of code.
These aren't exotic — they're the difference between a loop full of bookkeeping and a clean two-line solution.
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