Python heapq Usage Details and instance code, pythonheapq

Python heapq Usage Details and instance code, pythonheapq

Python heapq

Python has a built-in module. The heapq standard encapsulates the minimum heap algorithm implementation. Let's take a look at two good applications.

Small top heap (top K)

The requirement is as follows: a sequence with a fixed length is used to obtain top K big data.

import heapqimport randomclass TopkHeap(object):  def __init__(self, k):    self.k = k    self.data = []  def Push(self, elem):    if len(self.data) < self.k:      heapq.heappush(self.data, elem)    else:      topk_small = self.data[0]      if elem > topk_small:        heapq.heapreplace(self.data, elem)  def TopK(self):    return [x for x in reversed([heapq.heappop(self.data) for x in xrange(len(self.data))])]if __name__ == "__main__":  print "Hello"  list_rand = random.sample(xrange(1000000), 100)  th = TopkHeap(3)  for i in list_rand:    th.Push(i)  print th.TopK()  print sorted(list_rand, reverse=True)[0:3]

Big Top heap (small BtmK)

This demand has become more difficult: give N long sequences and find small BtmK elements, that is, use a large top heap.

The core idea of Algorithm Implementation is to change push (e) to push (-e) and pop (e) to-pop (e ).

class BtmkHeap(object):  def __init__(self, k):    self.k = k    self.data = []  def Push(self, elem):    # Reverse elem to convert to max-heap    elem = -elem    # Using heap algorighem    if len(self.data) < self.k:      heapq.heappush(self.data, elem)    else:      topk_small = self.data[0]      if elem > topk_small:        heapq.heapreplace(self.data, elem)  def BtmK(self):    return sorted([-x for x in self.data])

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