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<doc-anchor-target id="heapq" class="break-words">
<h1>
<doc-anchor-trigger class="header-anchor-trigger" to="#heapq">#</doc-anchor-trigger>
<span>heapq</span>
</h1>
</doc-anchor-target>
<doc-anchor-target id="heapqheappushheap-item">
<h3>
<doc-anchor-trigger class="header-anchor-trigger" to="#heapqheappushheap-item">#</doc-anchor-trigger>
<span><code translate="no" v-pre>heapq.heappush(heap, item)</code></span>
</h3>
</doc-anchor-target>
<p>Push the value <code translate="no" v-pre>item</code> onto the heap, maintaining the heap invariant.</p>
<doc-anchor-target id="heapqheappopheap">
<h3>
<doc-anchor-trigger class="header-anchor-trigger" to="#heapqheappopheap">#</doc-anchor-trigger>
<span><code translate="no" v-pre>heapq.heappop(heap)</code></span>
</h3>
</doc-anchor-target>
<p>Pop and return the smallest item from the heap, maintaining the heap invariant. If the heap is empty, IndexError is raised. To access the smallest item without popping it, use <code translate="no" v-pre>heap[0]</code>.</p>
<doc-anchor-target id="heapqheapifyx">
<h3>
<doc-anchor-trigger class="header-anchor-trigger" to="#heapqheapifyx">#</doc-anchor-trigger>
<span><code translate="no" v-pre>heapq.heapify(x)</code></span>
</h3>
</doc-anchor-target>
<p>Transform list <code translate="no" v-pre>x</code> into a heap, in-place, in linear time.</p>
<doc-anchor-target id="heapqheappushpopheap-item">
<h3>
<doc-anchor-trigger class="header-anchor-trigger" to="#heapqheappushpopheap-item">#</doc-anchor-trigger>
<span><code translate="no" v-pre>heapq.heappushpop(heap, item)</code></span>
</h3>
</doc-anchor-target>
<p>Push <code translate="no" v-pre>item</code> on the heap, then pop and return the smallest item from the heap. The combined action runs more efficiently than <code translate="no" v-pre>heappush()</code> followed by a separate <code translate="no" v-pre>heappop()</code>.</p>
<doc-anchor-target id="heapqheapreplaceheap-item">
<h3>
<doc-anchor-trigger class="header-anchor-trigger" to="#heapqheapreplaceheap-item">#</doc-anchor-trigger>
<span><code translate="no" v-pre>heapq.heapreplace(heap, item)</code></span>
</h3>
</doc-anchor-target>
<p>Pop and return the smallest item from the heap, and also push the new item. The heap size doesnt change. If the heap is empty, IndexError is raised.</p>
<doc-anchor-target id="source-code">
<h4>
<doc-anchor-trigger class="header-anchor-trigger" to="#source-code">#</doc-anchor-trigger>
<span>Source code</span>
</h4>
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<div class="codeblock-wrapper"><doc-codeblock>
<pre translate="no" class="language-python"><code v-pre class="language-python"># Heap queue algorithm (a.k.a. priority queue)
def heappush(heap, item):
&quot;&quot;&quot;Push item onto heap, maintaining the heap invariant.&quot;&quot;&quot;
heap.append(item)
_siftdown(heap, 0, len(heap)-1)
def heappop(heap):
&quot;&quot;&quot;Pop the smallest item off the heap, maintaining the heap invariant.&quot;&quot;&quot;
lastelt = heap.pop() # raises appropriate IndexError if heap is empty
if heap:
returnitem = heap[0]
heap[0] = lastelt
_siftup(heap, 0)
return returnitem
return lastelt
def heapreplace(heap, item):
&quot;&quot;&quot;Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap. Note that the value
returned may be larger than item! That constrains reasonable uses of
this routine unless written as part of a conditional replacement:
if item &gt; heap[0]:
item = heapreplace(heap, item)
&quot;&quot;&quot;
returnitem = heap[0] # raises appropriate IndexError if heap is empty
heap[0] = item
_siftup(heap, 0)
return returnitem
def heappushpop(heap, item):
&quot;&quot;&quot;Fast version of a heappush followed by a heappop.&quot;&quot;&quot;
if heap and heap[0] &lt; item:
item, heap[0] = heap[0], item
_siftup(heap, 0)
return item
def heapify(x):
&quot;&quot;&quot;Transform list into a heap, in-place, in O(len(x)) time.&quot;&quot;&quot;
n = len(x)
# Transform bottom-up. The largest index there's any point to looking at
# is the largest with a child index in-range, so must have 2*i + 1 &lt; n,
# or i &lt; (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so
# j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is
# (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.
for i in reversed(range(n//2)):
_siftup(x, i)
# 'heap' is a heap at all indices &gt;= startpos, except possibly for pos. pos
# is the index of a leaf with a possibly out-of-order value. Restore the
# heap invariant.
def _siftdown(heap, startpos, pos):
newitem = heap[pos]
# Follow the path to the root, moving parents down until finding a place
# newitem fits.
while pos &gt; startpos:
parentpos = (pos - 1) &gt;&gt; 1
parent = heap[parentpos]
if newitem &lt; parent:
heap[pos] = parent
pos = parentpos
continue
break
heap[pos] = newitem
def _siftup(heap, pos):
endpos = len(heap)
startpos = pos
newitem = heap[pos]
# Bubble up the smaller child until hitting a leaf.
childpos = 2*pos + 1 # leftmost child position
while childpos &lt; endpos:
# Set childpos to index of smaller child.
rightpos = childpos + 1
if rightpos &lt; endpos and not heap[childpos] &lt; heap[rightpos]:
childpos = rightpos
# Move the smaller child up.
heap[pos] = heap[childpos]
pos = childpos
childpos = 2*pos + 1
# The leaf at pos is empty now. Put newitem there, and bubble it up
# to its final resting place (by sifting its parents down).
heap[pos] = newitem
_siftdown(heap, startpos, pos)</code></pre>
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