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Problem solution · Python

Design an Array Statistics Tracker

Design an Array Statistics Tracker: a Python solution using breadth-first search. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Breadth-first search
Source
walkccc LeetCode Solutions
Length
41 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Breadth-first search

For Design an Array Statistics Tracker, the implementation explores reachable states in layers, which is the standard shape for unweighted shortest paths and minimum-step transitions.

  1. Model each valid configuration as a state and each legal move as an edge.
  2. Seed the queue with the starting state and mark it immediately.
  3. Expand each state once, recording distance or reachability for unseen neighbours.

Code notes

  • 41 lines of Python from the credited upstream file 3369.py.
  • The implementation visibly relies on sequence storage, hash lookup, work queue.
  • No explicit loop blocks detected.

Complexity

Verify that each state and transition is processed only a bounded number of times; that determines the traversal cost.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeDesign an Array Statistics Tracker · PythonPython
Use this to learn the idea, then write your own version.
from sortedcontainers import SortedList  class StatisticsTracker:  def __init__(self):    self.q = collections.deque()    self.count = collections.Counter()    self.sortedList = SortedList()    self.modeMaxHeap = []  # (frequency, number)    self.sum = 0   def addNumber(self, number: int) -> None:    self.q.append(number)    self.count[number] += 1    self.sortedList.add(number)    heapq.heappush(self.modeMaxHeap, (-self.count[number], number))    self.sum += number   def removeFirstAddedNumber(self) -> None:    number = self.q.popleft()    self.count[number] -= 1    self.sortedList.remove(number)    # Note: No need to update the heap now; we'll clean up stale entries when    # getting the mode.    self.sum -= number   def getMean(self) -> int:    return self.sum // len(self.q)   def getMedian(self) -> int:    return self.sortedList[len(self.sortedList) // 2]   def getMode(self) -> int:    # Removes stale heap entries where frequency no longer matches.    while self.modeMaxHeap:      frequency = -self.modeMaxHeap[0][0]      number = self.modeMaxHeap[0][1]      if self.count[number] == frequency:        return number      heapq.heappop(self.modeMaxHeap)

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