Algorithms & data structures
Classic patterns — search, sort, stacks, queues, and practical complexity-aware code.
Extract n largest elements from a large list using heapq
Uses heapq.nlargest to efficiently extract the top n largest numbers from a large list, even with millions of elements.
import heapq
import random
def n_largest(numbers, n):
"""Return the n largest numbers from a list using heapq."""
if n <= 0:
return []
return heapq.nlargest(n, numbers)
if __name__ == "__main__":
# Create a large list with 1,000,000 random numbers
large_list = [random.randint(1, 1_000_000…How to Heapify a List into a Min Heap with heapq in Python
Convert any list into a valid min heap in-place using Python's heapq.heapify(), then pop the smallest element to verify heap order.
import heapq
data = [5, 3, 8, 1, 9, 2, 7, 4, 6]
print("Original list:", data)
heapq.heapify(data)
print("Min heap:", data)
popped = heapq.heappop(data)
print("Smallest element popped:", popped)
print("Heap after pop:", data)Browse by section
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Algorithms & data structures — Python code examples
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