"""Leetcode 295. Find Median from Data Stream
Hard

URL: https://leetcode.com/problems/find-median-from-data-stream/

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

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

Design a data structure that supports the following two operations:
- void addNum(int num) - Add a integer number from the data stream to 
  the data structure.
- double findMedian() - Return the median of all elements so far.
 
Example:

addNum(1)
addNum(2)
findMedian() -> 1.5
addNum(3) 
findMedian() -> 2
 
Follow up:
- If all integer numbers from the stream are between 0 and 100, 
  how would you optimize it?
- If 99% of all integer numbers from the stream are between 0 and 100, 
  how would you optimize it?
"""

class MedianFinder(object):

    def __init__(self):
        """
        initialize your data structure here.
        """
        pass
        

    def addNum(self, num):
        """
        :type num: int
        :rtype: None
        """
        pass        

    def findMedian(self):
        """
        :rtype: float
        """
        pass


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

if __name__ == '__main__':
    main()

@@ -878,16 +878,31 @@
 t?%0A%22%22%22%0A%0A
+import heapq%0A%0A%0A
 class Me
@@ -921,17 +921,16 @@
 bject):%0A
-%0A
     def 
@@ -1026,21 +1026,63 @@
     
-pass%0A        
+self.small_maxheap = %5B%5D%0A        self.large_minheap = %5B%5D
 %0A%0A  
@@ -1186,106 +1186,980 @@
     
-pass        %0A%0A    def findMedian(self):%0A        %22%22%22%0A        :rtype: float%0A        %22%22%22%0A        pass
+if not self.small_maxheap or num %3C -self.small_maxheap%5B0%5D:%0A            heapq.heappush(self.small_maxheap, -num)%0A        else:%0A            heapq.heappush(self.large_minheap, num)%0A%0A        if len(self.small_maxheap) - len(self.large_minheap) == 2:%0A            pop_item = -heapq.heappop(self.small_maxheap)%0A            heapq.heappush(self.large_minheap, pop_item)%0A        elif len(self.small_maxheap) - len(self.large_minheap) == -2:%0A            pop_item = heapq.heappop(self.large_minheap)%0A            heapq.heappush(self.small_maxheap, -pop_item)%0A%0A    def findMedian(self):%0A        %22%22%22%0A        :rtype: float%0A        %22%22%22%0A        if len(self.small_maxheap) == len(self.large_minheap):%0A            return ((-1 * self.small_maxheap%5B0%5D) + self.large_minheap%5B0%5D) / 2.0%0A        elif len(self.small_maxheap) %3E len(self.large_minheap):%0A            return -self.small_maxheap%5B0%5D%0A        elif len(self.small_maxheap) %3C len(self.large_minheap):%0A            return self.large_minheap%5B0%5D
 %0A%0A%0Ad
@@ -2331,16 +2331,137 @@
     
-pass%0A   
+obj = MedianFinder()%0A    obj.addNum(1)%0A    obj.addNum(2)%0A    print obj.findMedian()%0A    obj.addNum(3)%0A    print obj.findMedian()%0A
 %0A%0Aif

Complete running median