- 2.1 enum: Enumeration Type
- 2.2 collections: Container Data Types
- 2.3 array: Sequence of Fixed-Type Data
- 2.4 heapq: Heap Sort Algorithm
- 2.5 bisect: Maintain Lists in Sorted Order
- 2.6 queue: Thread-Safe FIFO Implementation
- 2.7 struct: Binary Data Structures
- 2.8 weakref: Impermanent References to Objects
- 2.9 copy: Duplicate Objects
- 2.10 pprint: Pretty-Print Data Structures
This chapter is from the book
This chapter is from the book
This chapter is from the book
2.4 heapq: Heap Sort Algorithm
A heap is a tree-like data structure in which the child nodes have a sort-order relationship with the parents. Binary heaps can be represented using a list or array organized so that the children of element N are at positions 2*N+1 and 2*N+2 (for zero-based indexes). This layout makes it possible to rearrange heaps in place, so it is not necessary to reallocate as much memory when adding or removing items.
A max-heap ensures that the parent is larger than or equal to both of its children. A min-heap requires that the parent be less than or equal to its children. Python’s heapq module implements a min-heap.
2.4.1 Example Data
The examples in this section use the data in heapq_heapdata.py.
Listing 2.46: heapq_heapdata.py
# This data was generated with the random module.
data = [19, 9, 4, 10, 11]
The heap output is printed using heapq_showtree.py.
Listing 2.47: heapq_showtree.py
import math
from io import StringIO
def show_tree(tree, total_width=36, fill=' '):
"""Pretty-print a tree."""
output = StringIO()
last_row = -1
for i, n in enumerate(tree):
if i:
row = int(math.floor(math.log(i + 1, 2)))
else:
row = 0
if row != last_row:
output.write('\n')
columns = 2 ** row
col_width = int(math.floor(total_width / columns))
output.write(str(n).center(col_width, fill))
last_row = row
print(output.getvalue())
print('-' * total_width)
print()
2.4.2 Creating a Heap
There are two basic ways to create a heap: heappush() and heapify().
Listing 2.48: heapq_heappush.py
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
heap = []
print('random :', data)
print()
for n in data:
print('add {:>3}:'.format(n))
heapq.heappush(heap, n)
show_tree(heap)
When heappush() is used, the heap sort order of the elements is maintained as new items are added from a data source.
$ python3 heapq_heappush.py
random : [19, 9, 4, 10, 11]
add 19:
19
------------------------------------
add 9:
9
19
------------------------------------
add 4:
4
19 9
------------------------------------
add 10:
4
10 9
19
------------------------------------
add 11:
4
10 9
19 11
------------------------------------
If the data is already in memory, it is more efficient to use heapify() to rearrange the items of the list in place.
Listing 2.49: heapq_heapify.py
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
print('random :', data)
heapq.heapify(data)
print('heapified :')
show_tree(data)
The result of building a list in heap order one item at a time is the same as building an unordered list and then calling heapify().
$ python3 heapq_heapify.py
random : [19, 9, 4, 10, 11]
heapified :
4
9 19
10 11
------------------------------------
2.4.3 Accessing the Contents of a Heap
Once the heap is organized correctly, use heappop() to remove the element with the lowest value.
Listing 2.50: heapq_heappop.py
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
print('random :', data)
heapq.heapify(data)
print('heapified :')
show_tree(data)
print
for i in range(2):
smallest = heapq.heappop(data)
print('pop {:>3}:'.format(smallest))
show_tree(data)
In this example, adapted from the standard library documentation, heapify() and heappop() are used to sort a list of numbers.
$ python3 heapq_heappop.py
random : [19, 9, 4, 10, 11]
heapified :
4
9 19
10 11
------------------------------------
pop 4:
9
10 19
11
------------------------------------
pop 9:
10
11 19
------------------------------------
To remove existing elements and replace them with new values in a single operation, use heapreplace().
Listing 2.51: heapq_heapreplace.py
import heapq
from heapq_showtree import show_tree
from heapq_heapdata import data
heapq.heapify(data)
print('start:')
show_tree(data)
for n in [0, 13]:
smallest = heapq.heapreplace(data, n)
print('replace {:>2} with {:>2}:'.format(smallest, n))
show_tree(data)
Replacing elements in place makes it possible to maintain a fixed-size heap, such as a queue of jobs ordered by priority.
$ python3 heapq_heapreplace.py
start:
4
9 19
10 11
------------------------------------
replace 4 with 0:
0
9 19
10 11
------------------------------------
replace 0 with 13:
9
10 19
13 11
------------------------------------
2.4.4 Data Extremes from a Heap
heapq also includes two functions to examine an iterable and find a range of the largest or smallest values it contains.
Listing 2.52: heapq_extremes.py
import heapq
from heapq_heapdata import data
print('all :', data)
print('3 largest :', heapq.nlargest(3, data))
print('from sort :', list(reversed(sorted(data)[-3:])))
print('3 smallest:', heapq.nsmallest(3, data))
print('from sort :', sorted(data)[:3])
Using nlargest() and nsmallest() is efficient only for relatively small values of n > 1, but can still come in handy in a few cases.
$ python3 heapq_extremes.py
all : [19, 9, 4, 10, 11]
3 largest : [19, 11, 10]
from sort : [19, 11, 10]
3 smallest: [4, 9, 10]
from sort : [4, 9, 10]
2.4.5 Efficiently Merging Sorted Sequences
Combining several sorted sequences into one new sequence is easy for small data sets.
list(sorted(itertools.chain(*data)))
For larger data sets, this technique can use a considerable amount of memory. Instead of sorting the entire combined sequence, merge() uses a heap to generate a new sequence one item at a time, determining the next item using a fixed amount of memory.
Listing 2.53: heapq_merge.py
import heapq
import random
random.seed(2016)
data = []
for i in range(4):
new_data = list(random.sample(range(1, 101), 5))
new_data.sort()
data.append(new_data)
for i, d in enumerate(data):
print('{}: {}'.format(i, d))
print('\nMerged:')
for i in heapq.merge(*data):
print(i, end=' ')
print()
Because the implementation of merge() uses a heap, it consumes memory based on the number of sequences being merged, rather than the number of items in those sequences.
$ python3 heapq_merge.py
0: [33, 58, 71, 88, 95]
1: [10, 11, 17, 38, 91]
2: [13, 18, 39, 61, 63]
3: [20, 27, 31, 42, 45]
Merged:
10 11 13 17 18 20 27 31 33 38 39 42 45 58 61 63 71 88 91 95