Explain the two-heaps technique.
01
01
Understand the problem
heapdesign
02
02
Attempt it yourself
Sketch your approach before reading the solution — that's what interviews test.
Stuck? AI Nudge Available
Get a conceptual hint to guide your logic without spoiling the final implementation.
03
03
Study the solution
The solution is waiting
Give it an honest attempt first — then compare your thinking with the full walkthrough.
04
04
Read the code
Two balanced heaps
Run Playgroundimport heapq
class MedianFinder:
def __init__(self):
self.lo = [] # max-heap (stored as negatives)
self.hi = [] # min-heap
def add_num(self, num):
heapq.heappush(self.lo, -num)
heapq.heappush(self.hi, -heapq.heappop(self.lo)) # move max of lo to hi
if len(self.hi) > len(self.lo): # rebalance
heapq.heappush(self.lo, -heapq.heappop(self.hi))
def find_median(self):
if len(self.lo) > len(self.hi):
return float(-self.lo[0])
return (-self.lo[0] + self.hi[0]) / 2.0
# --- demo ---
mf = MedianFinder()
for x in [1, 2, 3]:
mf.add_num(x)
print(mf.find_median()) # 1.0, then 1.5, then 2.005
05
Join the discussion
Discussion (0)
Sign in to join the discussion.
No responses yet. Be the first to share what you think.