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Python find largest N (top N) or smallest N items

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✅ When to Use heapq.nlargest() or nsmallest()​

  • You need the top N largest or smallest elements without sorting the entire dataset.
  • Works with both simple iterables and complex data structures (using key).
  • Efficient for large data, especially when N is much smaller than the total number of elements.

🔍 Extra Tip: Use min() and max() when N = 1​

nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
print(max(nums))  # 42
print(min(nums))  # -4

#Also with another example
import heapq

company = [
   {'name': 'IBM', 'shares': 100, 'price': 91.1},
   {'name': 'AAPL', 'shares': 50, 'price': 543.22},
   {'name': 'FB', 'shares': 200, 'price': 21.09},
   {'name': 'HPQ', 'shares': 35, 'price': 31.75},
   {'name': 'YHOO', 'shares': 45, 'price': 16.35},
   {'name': 'ACME', 'shares': 75, 'price': 115.65}
]

cheap = heapq.nsmallest(1, company, key= lambda s: s['price'])
print("smallest", cheap)

cheap = heapq.nsmallest(3, company, key= lambda s: s['price'])
print("3 smallest", cheap)

cheapest = min(company, key= lambda s: s['price'])
print("Cheapest Company",cheapest)

expensive = max(company, key= lambda s: s['price'])
print("Expensive Company", expensive)

This is more efficient than calling nlargest(1, nums)[0].

⚙️ If you ever need to extract by multiple conditions, you can chain key logic​

# Highest value per share (price per share)
expensive_by_value = heapq.nlargest(3, portfolio, key=lambda s: s['price'] * s['shares'])

Let me know if you’d like to see:

  • A custom comparator version
  • How heapq is implemented under the hood
  • Performance comparison with sorting

Happy Pythoning! 🐍