LeetCode - Find Median from Data Stream

The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, and the median is the mean of the two middle values.

• For example, for arr = [2,3,4], the median is 3.
• For example, for arr = [2,3], the median is (2 + 3) / 2 = 2.5.

Implement the MedianFinder class:

• MedianFinder() initializes the MedianFinder object.
• void addNum(int num) adds the integer num from the data stream to the data structure.
• double findMedian() returns the median of all elements so far. Answers within 10-5 of the actual answer will be accepted.

 

Example 1:

Input
["MedianFinder", "addNum", "addNum", "findMedian", "addNum", "findMedian"]
[[], [1], [2], [], [3], []]
Output
[null, null, null, 1.5, null, 2.0]

Explanation
MedianFinder medianFinder = new MedianFinder();
medianFinder.addNum(1);    // arr = [1]
medianFinder.addNum(2);    // arr = [1, 2]
medianFinder.findMedian(); // return 1.5 (i.e., (1 + 2) / 2)
medianFinder.addNum(3);    // arr[1, 2, 3]
medianFinder.findMedian(); // return 2.0

 

Constraints:

• -105 <= num <= 105
• There will be at least one element in the data structure before calling findMedian.
• At most 5 * 104 calls will be made to addNum and findMedian.

 

My Solution:

import numpy as np


class MedianFinder:

    def __init__(self):
        self.nums = np.array([], dtype=int)


    def addNum(self, num: int) -> None:
        self.nums = np.append(self.nums, num)
        

    def findMedian(self) -> float:
        return np.median(self.nums)


# Your MedianFinder object will be instantiated and called as such:
# obj = MedianFinder()
# obj.addNum(num)
# param_2 = obj.findMedian()

 

 

ChatGPT's Solution:

import heapq


class MedianFinder:

    def __init__(self):
        # Max heap (store negative values for simulation)
        self.low = []  # Lower half
        # Min heap
        self.high = []  # Upper half


    def addNum(self, num: int) -> None:
        # Insert into max heap (store negative to simulate max heap)
        heapq.heappush(self.low, -num)

        # Ensure max heap root is not greater than min heap root
        if self.low and self.high and (-self.low[0] > self.high[0]):
            heapq.heappush(self.high, -heapq.heappop(self.low))
        
        # Balance the heaps: Ensure low has equal or one more element than high
        if len(self.low) > len(self.high) + 1:
            heapq.heappush(self.high, -heapq.heappop(self.low))
        elif len(self.high) > len(self.low):
            heapq.heappush(self.low, -heapq.heappop(self.high))
        

    def findMedian(self) -> float:
        if len(self.low) > len(self.high):
            return -self.low[0]  # Max heap root (stored as negative)
        return (-self.low[0] + self.high[0]) / 2  # Average of roots


# Your MedianFinder object will be instantiated and called as such:
# obj = MedianFinder()
# obj.addNum(num)
# param_2 = obj.findMedian()