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Python判断列表是否已排序的各种方法及其性能分析

时间：2020-09-12 06:46 编辑：来源：阅读：
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摘要：Python判断列表是否已排序的各种方法及其性能分析

[b]声明[/b] 本文基于Python2.7语言，给出判断列表是否已排序的多种方法，并在作者的Windows XP主机(Pentium G630 2.7GHz主频2GB内存)上对比和分析其性能表现。 [b]一. 问题提出[/b] Haskell培训老师提出一个问题：如何判断列表是否已经排序？排序与否实际只是相邻元素间的某种二元关系，即a->a->Bool。所以第一步可以把二元组列表找出来；第二步是把这个函数作用于每个元组，然后用and操作。老师给出的实现代码如下：

pair lst = zip lst ( tail lst )
sorted lst predict = and [ predict x y | (x,y) <- pair lst]

Haskell中，等号前面是函数的名称和参数，后面是函数的定义体。pair函数将列表lst错位一下(tail除去列表的第一个元素)后，和原列表在zip的作用下形成前后相邻元素二元组列表。predict函数接受两个数字，根据大小返回True或False。and对类型为[Bool]的列表中所有元素求与，其语义类似Python的all()函数。随后，老师请大家思考性能问题。作者提出，若准确性要求不高，可生成三组随机数排序后作为下标，提取原列表相应的三组元素组成三个新的子列表("采样")。若判断三个子列表遵循同样的排序规则时，则认为原列表已排序。当列表很大且前段已排序时，选取适当数目的随机数，可在保障一定准确率的同时显著地降低运算规模。此外，实际应用中还应考虑数据来源。例如，Python语言的os.listdir()方法在Windows系统中返回的列表条目满足字母序，在Linux系统中则返回乱序条目。因此，若判断系统平台(os.platform)为win32，则条目必然已经排序。为对比验证随机采样方式的可行性，作者先在网上搜集判断列表排序的现有方法，主要参考Stack Overflow网站上"Pythonic way to check if a list is sorted or not"问题的答案，并在本文第二节给出相关的代码示例。注意，本文所述的"排序"不要求严格排序，即相邻元素允许相等。例如，[1,2,2,3]视为升序，[3,3,2,2]视为降序。 [b]二. 代码实现 [/b] 本节判断列表排序的函数名格式为IsListSorted_XXX()。为简洁起见，除代码片段及其输出外，一律以_XXX()指代。 [b]2.1 guess[/b]

def IsListSorted_guess(lst):
listLen = len(lst)
if listLen <= 1:
return True
#由首个元素和末尾元素猜测可能的排序规则
if lst[0] == lst[-1]: #列表元素相同
for elem in lst:
if elem != lst[0]: return False
elif lst[0] < lst[-1]: #列表元素升序
for i, elem in enumerate(lst[1:]):
if elem < lst[i]: return False
else: #列表元素降序
for i, elem in enumerate(lst[1:]):
if elem > lst[i]: return False
return True

_guess()是最通用的实现，几乎与语言无关。值得注意的是，该函数内会猜测给定列表可能的排序规则，因此无需外部调用者指明排序规则。 [b]2.2 sorted[/b]

def IsListSorted_sorted(lst):
return sorted(lst) == lst or sorted(lst, reverse=True) == lst

_sorted()使用Python内置函数sorted()。由于sorted()会对未排序的列表排序，_sorted()函数主要适用于已排序列表。若想判断列表未排序后再对其排序，不如直接调用列表的sort()方法，因为该方法内部会判断列表是否排序。对于已排序列表，该方法的时间复杂度为线性阶O(n)——判断为O(n)而排序为O(nlgn)。 [b]2.3 for-loop[/b]

def IsListSorted_forloop(lst, key=lambda x, y: x <= y):
for i, elem in enumerate(lst[1:]): #注意，enumerate默认迭代下标从0开始
if not key(lst[i], elem): #if elem > lst[i]更快，但通用性差
return False
return True

无论列表是否已排序，本函数的时间复杂度均为线性阶O(n)。注意，参数key表明缺省的排序规则为升序。 [b]2.4 all [/b]

def IsListSorted_allenumk(lst, key=lambda x, y: x <= y):
return all(key(lst[i], elem) for i, elem in enumerate(lst[1:]))
import operator
def IsListSorted_allenumo(lst, oCmp=operator.le):
return all(oCmp(lst[i], elem) for i, elem in enumerate(lst[1:]))
def IsListSorted_allenumd(lst):
return all((lst[i] <= elem) for i, elem in enumerate(lst[1:]))
def IsListSorted_allxran(lst, key=lambda x,y: x <= y):
return all(key(lst[i],lst[i+1]) for i in xrange(len(lst)-1))
def IsListSorted_allzip(lst, key=lambda x,y: x <= y):
from itertools import izip #Python 3中zip返回生成器(generator)，而izip被废弃
return all(key(a, b) for (a, b) in izip(lst[:-1],lst[1:]))

lambda表达式与operator运算符速度相当，前者简单灵活，后者略为高效(实测并不一定)。但两者速度均不如列表元素直接比较(可能存在调用开销)。亦即，_allenumd()快于_allenumo()快于_allenumk()。若使用lambda表达式指示排序规则，更改规则时只需要改变x和y之间的比较运算符；若使用operator模块指示排序规则，更改规则时需要改变对象比较方法。具体地，lt(x, y)等效于x < y，le(x, y)等效于x <= y，eq(x, y)等效于x == y，ne(x, y)等效于x != y，gt(x, y)等效于x > y，ge(x, y)等效于x >= y。例如，_allenumo()函数若要严格升序可设置oCmp=operator.lt。此外，由all()函数的帮助信息可知，_allenumk()其实是_forloop()的等效形式。 [b]2.5 numpy[/b]

def IsListSorted_numpy(arr, key=lambda dif: dif >= 0):
import numpy
try:
if arr.dtype.kind == 'u': #无符号整数数组执行np.diff时存在underflow风险
arr = numpy.int64(lst)
except AttributeError:
pass #无dtype属性，非数组
return (key(numpy.diff(arr))).all() #numpy.diff(x)返回相邻数组元素的差值构成的数组

NumPy是用于科学计算的Python基础包，可存储和处理大型矩阵。它包含一个强大的N维数组对象，比Python自身的嵌套列表结构(nested list structure)高效得多。第三节的实测数据表明，_numpy()处理大型列表时性能非常出色。在Windows系统中可通过pip install numpy命令安装NumPy包，不建议登录官网下载文件自行安装。 [b]2.6 reduce[/b]

def IsListSorted_reduce(iterable, key=lambda x, y: x <= y):
cmpFunc = lambda x, y: y if key(x, y) else float('inf')
return reduce(cmpFunc, iterable, .0) < float('inf')

reduce实现是all实现的变体。累加器(accumulator)中仅存储最后一个检查的列表元素，或者Infinity(若任一元素小于前个元素值)。前面2.1~2.5小节涉及下标操作的函数适用于列表等可迭代对象(Iterable)。对于通用迭代器(Iterator)对象，即可以作用于next()函数或方法的对象，可使用_reduce()及后面除_rand()外各小节的函数。迭代器的计算是惰性的，只有在需要返回下一个数据时才会计算，以避免不必要的计算。而且，迭代器方式无需像列表那样切片为两个迭代对象。 [b]2.7 imap [/b]

def IsListSorted_itermap(iterable, key=lambda x, y: x <= y):
from itertools import imap, tee
a, b = tee(iterable) #为单个iterable创建两个独立的iterator
next(b, None)
return all(imap(key, a, b))

[b]2.8 izip[/b]

def IsListSorted_iterzip(iterable, key=lambda x, y: x <= y):
from itertools import tee, izip
a, b = tee(iterable)
next(b, None)
return all(key(x, y) for x, y in izip(a, b))
def pairwise(iterable):
from itertools import tee, izip
a, b = tee(iterable)
next(b, None)
return izip(a, b) #"s -> (s0,s1), (s1,s2), (s2, s3), ..."
def IsListSorted_iterzipf(iterable, key=lambda x, y: x <= y):
return all(key(a, b) for a, b in pairwise(iterable))

第三节的实测数据表明，虽然存在外部函数调用，_iterzipf()却比_iterzip()略为高效。 [b]2.9 fast[/b]

def IsListSorted_fastd(lst):
it = iter(lst)
try:
prev = it.next()
except StopIteration:
return True
for cur in it:
if prev > cur:
return False
prev = cur
return True
def IsListSorted_fastk(lst, key=lambda x, y: x <= y):
it = iter(lst)
try:
prev = it.next()
except StopIteration:
return True
for cur in it:
if not key(prev, cur):
return False
prev = cur
return True

_fastd()和_fastk()是Stack Overflow网站回答里据称执行最快的。实测数据表明，在列表未排序时，它们的性能表现确实优异。 [b]2.10 random[/b]

import random
def IsListSorted_rand(lst, randNum=3, randLen=100):
listLen = len(lst)
if listLen <= 1:
return True

#由首个元素和末尾元素猜测可能的排序规则
if lst[0] < lst[-1]: #列表元素升序
key = lambda dif: dif >= 0
else: #列表元素降序或相等
key = lambda dif: dif <= 0

threshold, sortedFlag = 10000, True
import numpy
if listLen <= threshold or listLen <= randLen*2 or not randNum:
return (key(numpy.diff(numpy.array(lst)))).all()
from random import sample
for i in range(randNum):
sortedRandList = sorted(sample(xrange(listLen), randLen))
flag = (key(numpy.diff(numpy.array([lst[x] for x in sortedRandList])))).all()
sortedFlag = sortedFlag and flag
return sortedFlag

_rand()借助随机采样降低运算规模，并融入其他判断函数的优点。例如，猜测列表可能的排序规则，并在随机采样不适合时使用相对快速的判断方式，如NumPy。通过line_profiler分析可知，第20行和第21行与randLen有关，但两者耗时接近。因此randLen应小于listLen的一半，以抵消sorted开销。除内部限制外，用户可以调节随机序列个数和长度，如定制单个但较长的序列。注意，_rand()不适用于存在微量异常数据的长列表。因为这些数据很可能被随机采样遗漏，从而影响判断结果的准确性。 [b]三. 性能分析[/b] 本节借助Python标准库random模块，生成各种随机列表，以对比和分析上节列表排序判断函数的性能。可通过sample()、randint()等方法生成随机列表。例如：

>>>import random
>>> random.sample(range(10), 10); random.sample(range(10), 5)
[9, 1, 6, 3, 0, 8, 4, 2, 7, 5]
[1, 2, 5, 6, 0]
>>> rand = [random.randint(1,10) for i in range(10)]; rand
[7, 3, 7, 5, 7, 2, 4, 4, 9, 8]
>>> random.sample(rand, 5); random.sample(rand, 5)
[4, 7, 7, 9, 8]
[3, 9, 2, 5, 7]
>>> randGen = (random.randint(1,10) for i in range(10)); randGen
<generator object <genexpr> at 0x0192C878>

sample()方法从列表中随机选择数字，结合range()函数可生产不含重复元素的随机列表；而randint()方法结合列表解析生成的随机列表可能包含重复元素。Python文档规定，首次导入random模块时使用当前系统时间作为种子初始化随机数生成器。因此，本文并未显式地调用seed()方法设置种子。为度量性能表现，定义如下计时装饰器：

from time import clock
TimeList = []
def FuncTimer(repeats=1000):
def decorator(func):
def wrapper(*args, **kwargs):
try:
startTime = clock()
for i in xrange(repeats):
ret = func(*args, **kwargs)
except Exception, e:
print '%s() Error: %s!' %(func.__name__, e)
ret = None
finally: #当目标函数发生异常时，仍旧输出计时信息
endTime = clock()
timeElasped = (endTime-startTime)*1000.0
msg = '%21s(): %s =>Time Elasped: %.3f msec, repeated %d time(s).' \
%(func.__name__, ret, timeElasped, repeats)
global TimeList; TimeList.append([timeElasped, msg])
return ret
return wrapper
return decorator
def ReportTimer():
global TimeList; TimeList.sort(key=lambda x:x[0])
for entry in TimeList:
print entry[1]
TimeList = [] #Flush

该装饰器允许对输出进行排序，以便更直观地观察性能。基于该装饰器，下文将分别测试不同的排序场景。注意，第二节各函数头部需添加FuncTimer()装饰。 [b]3.1 列表前段乱序[/b] 测试代码如下：

def TestHeadUnorderedList():
TEST_NAME = 'HeadUnorderedList'; scale = int(1e5)
List = random.sample(xrange(scale), scale) + range(scale)
print 'Test 1: %s, list len: %d' %(TEST_NAME, len(List))
IsListSorted_guess(List)
IsListSorted_sorted(List)
IsListSorted_allenumk(List)
IsListSorted_allenumo(List)
IsListSorted_allenumd(List)
IsListSorted_allxran(List)
IsListSorted_allzip(List)
IsListSorted_forloop(List)
IsListSorted_itermap(List)
IsListSorted_iterzipf(List)
IsListSorted_iterzip(List)
IsListSorted_reduce(List)
IsListSorted_numpy(numpy.array(List)) #若不先转换为数组，则耗时骤增
IsListSorted_fastd(List)
IsListSorted_fastk(List)
ReportTimer()

运行输出如下：

Test 1: HeadUnorderedList, list len: 200
IsListSorted_fastd(): False =>Time Elasped: 0.757 msec, repeated 1000 time(s).
IsListSorted_fastk(): False =>Time Elasped: 1.091 msec, repeated 1000 time(s).
IsListSorted_forloop(): False =>Time Elasped: 2.080 msec, repeated 1000 time(s).
IsListSorted_guess(): False =>Time Elasped: 2.123 msec, repeated 1000 time(s).
IsListSorted_allxran(): False =>Time Elasped: 2.255 msec, repeated 1000 time(s).
IsListSorted_allenumd(): False =>Time Elasped: 2.672 msec, repeated 1000 time(s).
IsListSorted_allenumo(): False =>Time Elasped: 3.021 msec, repeated 1000 time(s).
IsListSorted_allenumk(): False =>Time Elasped: 3.207 msec, repeated 1000 time(s).
IsListSorted_itermap(): False =>Time Elasped: 5.845 msec, repeated 1000 time(s).
IsListSorted_allzip(): False =>Time Elasped: 7.793 msec, repeated 1000 time(s).
IsListSorted_iterzip(): False =>Time Elasped: 9.667 msec, repeated 1000 time(s).
IsListSorted_iterzipf(): False =>Time Elasped: 9.969 msec, repeated 1000 time(s).
IsListSorted_numpy(): False =>Time Elasped: 16.203 msec, repeated 1000 time(s).
IsListSorted_sorted(): False =>Time Elasped: 45.742 msec, repeated 1000 time(s).
IsListSorted_reduce(): False =>Time Elasped: 145.447 msec, repeated 1000 time(s).
Test 1: HeadUnorderedList, list len: 200000
IsListSorted_fastd(): False =>Time Elasped: 0.717 msec, repeated 1000 time(s).
IsListSorted_fastk(): False =>Time Elasped: 0.876 msec, repeated 1000 time(s).
IsListSorted_allxran(): False =>Time Elasped: 2.104 msec, repeated 1000 time(s).
IsListSorted_itermap(): False =>Time Elasped: 6.062 msec, repeated 1000 time(s).
IsListSorted_iterzip(): False =>Time Elasped: 7.244 msec, repeated 1000 time(s).
IsListSorted_iterzipf(): False =>Time Elasped: 8.491 msec, repeated 1000 time(s).
IsListSorted_numpy(): False =>Time Elasped: 801.916 msec, repeated 1000 time(s).
IsListSorted_forloop(): False =>Time Elasped: 2924.755 msec, repeated 1000 time(s).
IsListSorted_guess(): False =>Time Elasped: 2991.756 msec, repeated 1000 time(s).
IsListSorted_allenumo(): False =>Time Elasped: 3025.864 msec, repeated 1000 time(s).
IsListSorted_allenumk(): False =>Time Elasped: 3062.792 msec, repeated 1000 time(s).
IsListSorted_allenumd(): False =>Time Elasped: 3190.896 msec, repeated 1000 time(s).
IsListSorted_allzip(): False =>Time Elasped: 6586.183 msec, repeated 1000 time(s).
IsListSorted_sorted(): False =>Time Elasped: 119974.955 msec, repeated 1000 time(s).
IsListSorted_reduce(): False =>Time Elasped: 154747.659 msec, repeated 1000 time(s).

可见，对于前段乱序的列表，无论其长短_fastd()和_fastk()的表现均为最佳。对于未排序列表，_sorted()需要进行排序，故性能非常差。然而，_reduce()性能最差。实际上除_guess()和_sorted()外，其他函数都按升序检查列表。为安全起见，可仿照_guess()实现，先猜测排序方式，再进一步检查。因为短列表耗时差异大多可以忽略，后续测试将不再包含短列表(但作者确实测试过)，仅关注长列表。除非特别说明，列表长度为10万级，重复计时1000次。 [b]3.2 列表中段乱序 [/b] 测试代码及结果如下：

def TestMiddUnorderedList():
TEST_NAME = 'MiddUnorderedList'; scale = int(1e5)
List = range(scale) + random.sample(xrange(scale), scale) + range(scale)
print 'Test 2: %s, list len: %d' %(TEST_NAME, len(List))
IsListSorted_numpy(numpy.array(List)) # 1572.295 msec 
IsListSorted_guess(List) # 14540.637 msec
IsListSorted_itermap(List) # 21013.096 msec
IsListSorted_fastk(List) # 23840.582 msec
IsListSorted_allxran(List) # 31014.215 msec
IsListSorted_iterzip(List) # 33386.059 msec
IsListSorted_forloop(List) # 34228.006 msec
IsListSorted_allenumk(List) # 34416.802 msec
IsListSorted_allzip(List) # 42370.528 msec
IsListSorted_sorted(List) # 142592.756 msec
IsListSorted_reduce(List) # 187514.967 msec
ReportTimer()

为节省篇幅，已根据运行输出调整函数的调用顺序。下文也作如此处理。 [b]3.3 列表后段乱序[/b] 测试代码及结果如下：

def TestTailUnorderedList():
TEST_NAME = 'TailUnorderedList'; scale = int(1e5)
List = range(scale, 0, -1) + random.sample(xrange(scale), scale)
print 'Test 3: %s, list len: %d' %(TEST_NAME, len(List))
IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0) # 980.789 msec 
IsListSorted_guess(List) # 13273.862 msec
IsListSorted_itermap(List, key=lambda x, y: x >= y) # 21603.315 msec
IsListSorted_fastk(List, key=lambda x, y: x >= y) # 24183.548 msec
IsListSorted_allxran(List, key=lambda x, y: x >= y) # 32850.254 msec
IsListSorted_forloop(List, key=lambda x, y: x >= y) # 33918.848 msec
IsListSorted_iterzip(List, key=lambda x, y: x >= y) # 34505.809 msec
IsListSorted_allenumk(List, key=lambda x, y: x >= y) # 35631.625 msec
IsListSorted_allzip(List, key=lambda x, y: x >= y) # 40076.330 msec
ReportTimer()

[b]3.4 列表完全乱序[/b] 测试代码及结果如下：

def TestUnorderedList():
TEST_NAME = 'UnorderedList'; scale = int(1e5)
List = random.sample(xrange(scale), scale)
print 'Test 4: %s, list len: %d' %(TEST_NAME, len(List))
IsListSorted_fastk(List) # 0.856 msec
IsListSorted_allxran(List) # 3.438 msec
IsListSorted_iterzip(List) # 7.233 msec
IsListSorted_itermap(List) # 7.595 msec
IsListSorted_numpy(numpy.array(List)) # 207.222 msec
IsListSorted_allenumk(List) # 953.423 msec
IsListSorted_guess(List) # 1023.575 msec
IsListSorted_forloop(List) # 1076.986 msec
IsListSorted_allzip(List) # 2062.821 msec
ReportTimer()

[b]3.5 列表元素相同 [/b] 测试代码及结果如下：

```python def TestSameElemList(): TEST_NAME = 'SameElemList'; scale = int(1e5) List = [5]*scale print 'Test 5: %s, list len: %d' %(TEST_NAME, len(List)) IsListSorted_numpy(numpy.array(List)) # 209.324 msec IsListSorted_sorted(List) # 2760.139 msec IsListSorted_guess(List) # 5843.942 msec IsListSorted_itermap(List) # 20609.704 msec IsListSorted_fastk(List) # 23035.760 msec IsListSorted_forloop(List) # 29043.206 msec IsListSorted_allenumk(List) # 29553.716 msec IsListSorted_allxran(List) # 30348.549 msec IsListSorted_iterzip(List) # 32806.217 msec ReportTimer()

[b]3.6 列表升序[/b] 测试代码及结果如下：

def TestAscendingList():
TEST_NAME = 'AscendingList'; scale = int(1e5)
List = range(scale)
print 'Test 6: %s, list len: %d' %(TEST_NAME, len(List))
IsListSorted_numpy(numpy.array(List)) # 209.217 msec
IsListSorted_sorted(List) # 2845.166 msec
IsListSorted_fastd(List) # 5977.520 msec
IsListSorted_guess(List) # 10408.204 msec
IsListSorted_allenumd(List) # 15812.754 msec
IsListSorted_itermap(List) # 21244.476 msec
IsListSorted_fastk(List) # 23900.196 msec
IsListSorted_allenumo(List) # 28607.724 msec
IsListSorted_forloop(List) # 30075.538 msec
IsListSorted_allenumk(List) # 30274.017 msec
IsListSorted_allxran(List) # 31126.404 msec
IsListSorted_reduce(List) # 32940.108 msec
IsListSorted_iterzip(List) # 34188.312 msec
IsListSorted_iterzipf(List) # 34425.097 msec
IsListSorted_allzip(List) # 37967.447 msec
ReportTimer()

可见，列表已排序时，_sorted()的性能较占优势。 [b]3.7 列表降序[/b] 剔除不支持降序的函数，测试代码及结果如下：

def TestDescendingList():
TEST_NAME = 'DescendingList'; scale = int(1e2)
List = range(scale, 0, -1)
print 'Test 7: %s, list len: %d' %(TEST_NAME, len(List))
IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0) # 209.318 msec
IsListSorted_sorted(List) # 5707.067 msec
IsListSorted_guess(List) # 10549.928 msec
IsListSorted_itermap(List, key=lambda x, y: x >= y) # 21529.547 msec
IsListSorted_fastk(List, key=lambda x, y: x >= y) # 24264.465 msec
import operator; IsListSorted_allenumo(List, oCmp=operator.ge) # 28093.035 msec
IsListSorted_forloop(List, key=lambda x, y: x >= y) # 30745.943 msec
IsListSorted_allenumk(List, key=lambda x, y: x >= y) # 32696.205 msec
IsListSorted_allxran(List, key=lambda x, y: x >= y) # 33431.473 msec
IsListSorted_allzip(List, key=lambda x, y: x >= y) # 34837.019 msec
IsListSorted_iterzip(List, key=lambda x, y: x >= y) # 35237.475 msec
IsListSorted_reduce(List, key=lambda x, y: x >= y) # 37035.270 msec
ReportTimer()

[b]3.8 迭代器测试[/b] 参数为列表的函数，需要先将列表��过iter()函数转换为迭代器。注意，当iterable参数为iterator时，只能计时一次，因为该次执行将用尽迭代器。测试代码如下：

def TestIter():
TEST_NAME = 'Iter'; scale = int(1e7)
List = range(scale) #升序
# List = random.sample(xrange(scale), scale) #乱序
print 'Test 8: %s, list len: %d' %(TEST_NAME, len(List))
iterL = iter(List); IsListSorted_guess(list(iterL))
iterL = iter(List); IsListSorted_sorted(iterL)
iterL = iter(List); IsListSorted_itermap(iterL)
iterL = iter(List); IsListSorted_iterzipf(iterL)
iterL = iter(List); IsListSorted_iterzip(iterL)
iterL = iter(List); IsListSorted_reduce(iterL)
iterL = iter(List); IsListSorted_fastd(iterL)
iterL = iter(List); IsListSorted_fastk(iterL, key=lambda x, y: x >= y)
ReportTimer()

运行结果如下：

Test 8: Iter, list len: 10000000 ---升序
IsListSorted_fastd(): True =>Time Elasped: 611.028 msec, repeated 1 time(s).
IsListSorted_sorted(): False =>Time Elasped: 721.751 msec, repeated 1 time(s).
IsListSorted_guess(): True =>Time Elasped: 1142.065 msec, repeated 1 time(s).
IsListSorted_itermap(): True =>Time Elasped: 2097.696 msec, repeated 1 time(s).
IsListSorted_fastk(): True =>Time Elasped: 2337.233 msec, repeated 1 time(s).
IsListSorted_reduce(): True =>Time Elasped: 3307.361 msec, repeated 1 time(s).
IsListSorted_iterzipf(): True =>Time Elasped: 3354.034 msec, repeated 1 time(s).
IsListSorted_iterzip(): True =>Time Elasped: 3442.520 msec, repeated 1 time(s).
Test 8: Iter, list len: 10000000 ---乱序
IsListSorted_fastk(): False =>Time Elasped: 0.004 msec, repeated 1 time(s).
IsListSorted_fastd(): False =>Time Elasped: 0.010 msec, repeated 1 time(s).
IsListSorted_iterzip(): False =>Time Elasped: 0.015 msec, repeated 1 time(s).
IsListSorted_itermap(): False =>Time Elasped: 0.055 msec, repeated 1 time(s).
IsListSorted_iterzipf(): False =>Time Elasped: 0.062 msec, repeated 1 time(s).
IsListSorted_guess(): False =>Time Elasped: 736.810 msec, repeated 1 time(s).
IsListSorted_reduce(): False =>Time Elasped: 8919.611 msec, repeated 1 time(s).
IsListSorted_sorted(): False =>Time Elasped: 12273.018 msec, repeated 1 time(s).

其中，_itermap()、_iterzip()、_iterzipf()、_reduce()、_fastd()、_fastk()可直接判断迭代器是否已排序。其他函数需将迭代器转换为列表后才能处理。_sorted()虽然接受迭代器参数，但sorted()返回列表，故无法正确判断迭代器顺序。 [b]3.9 随机采样测试 [/b] 综合以上测试，可知_fastk()和_numpy()性能较为突出，而且_rand()内置numpy方式。因此，以_fastk()和_numpy()为参照对象，测试代码如下(计时1次)：

def TestRandList():
scale = int(1e6)
List = random.sample(xrange(scale), scale) + range(scale)
print 'Test 1: %s, list len: %d' %('HeadUnorderedList', len(List))
IsListSorted_fastk(List)
IsListSorted_numpy(numpy.array(List))
IsListSorted_rand(List, randNum=1)
ReportTimer()
List = range(scale) + random.sample(xrange(scale), scale) + range(scale)
print 'Test 2: %s, list len: %d' %('MiddUnorderedList', len(List))
IsListSorted_fastk(List)
IsListSorted_numpy(numpy.array(List))
IsListSorted_rand(List, randNum=1)
ReportTimer()
List = range(scale, 0, -1) + random.sample(xrange(scale), scale)
print 'Test 3: %s, list len: %d' %('TailUnorderedList', len(List))
IsListSorted_fastk(List, key=lambda x, y: x >= y)
IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0)
IsListSorted_rand(List, randNum=1)
ReportTimer()
List = [random.randint(1,scale) for i in xrange(scale)] #random.sample(xrange(scale), scale)
print 'Test 4: %s, list len: %d' %('UnorderedList', len(List))
IsListSorted_fastk(List)
IsListSorted_numpy(numpy.array(List))
IsListSorted_rand(List, randNum=1)
ReportTimer()
List = [5]*scale
print 'Test 5: %s, list len: %d' %('SameElemList', len(List))
IsListSorted_fastk(List)
IsListSorted_numpy(numpy.array(List))
IsListSorted_rand(List, randNum=1)
ReportTimer()
List = range(scale)
print 'Test 6: %s, list len: %d' %('AscendingList', len(List))
IsListSorted_fastk(List)
IsListSorted_numpy(numpy.array(List))
IsListSorted_rand(List, randNum=1)
ReportTimer()
List = range(scale, 0, -1)
print 'Test 7: %s, list len: %d' %('DescendingList', len(List))
IsListSorted_fastk(List, key=lambda x, y: x >= y)
IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0)
IsListSorted_rand(List, randNum=1)
ReportTimer()
List = range(scale, 0, -1); List[scale/2]=0
print 'Test 8: %s, list len: %d' %('MiddleNotchList', len(List))
IsListSorted_fastk(List, key=lambda x, y: x >= y)
IsListSorted_numpy(numpy.array(List), key=lambda dif: dif <= 0)
IsListSorted_rand(List, randNum=1)
IsListSorted_rand(List, randNum=1, randLen=scale/2)
ReportTimer()

运行输出如下：

Test 1: HeadUnorderedList, list len: 2000000
IsListSorted_fastk(): False =>Time Elasped: 0.095 msec, repeated 1 time(s).
IsListSorted_rand(): False =>Time Elasped: 0.347 msec, repeated 1 time(s).
IsListSorted_numpy(): False =>Time Elasped: 7.893 msec, repeated 1 time(s).
Test 2: MiddUnorderedList, list len: 3000000
IsListSorted_rand(): False =>Time Elasped: 0.427 msec, repeated 1 time(s).
IsListSorted_numpy(): False =>Time Elasped: 11.868 msec, repeated 1 time(s).
IsListSorted_fastk(): False =>Time Elasped: 210.842 msec, repeated 1 time(s).
Test 3: TailUnorderedList, list len: 2000000
IsListSorted_rand(): False =>Time Elasped: 0.355 msec, repeated 1 time(s).
IsListSorted_numpy(): False =>Time Elasped: 7.548 msec, repeated 1 time(s).
IsListSorted_fastk(): False =>Time Elasped: 280.416 msec, repeated 1 time(s).
Test 4: UnorderedList, list len: 1000000
IsListSorted_fastk(): False =>Time Elasped: 0.074 msec, repeated 1 time(s).
IsListSorted_rand(): False =>Time Elasped: 0.388 msec, repeated 1 time(s).
IsListSorted_numpy(): False =>Time Elasped: 3.757 msec, repeated 1 time(s).
Test 5: SameElemList, list len: 1000000
IsListSorted_rand(): True =>Time Elasped: 0.304 msec, repeated 1 time(s).
IsListSorted_numpy(): True =>Time Elasped: 3.955 msec, repeated 1 time(s).
IsListSorted_fastk(): True =>Time Elasped: 210.977 msec, repeated 1 time(s).
Test 6: AscendingList, list len: 1000000
IsListSorted_rand(): True =>Time Elasped: 0.299 msec, repeated 1 time(s).
IsListSorted_numpy(): True =>Time Elasped: 4.822 msec, repeated 1 time(s).
IsListSorted_fastk(): True =>Time Elasped: 214.171 msec, repeated 1 time(s).
Test 7: DescendingList, list len: 1000000
IsListSorted_rand(): True =>Time Elasped: 0.336 msec, repeated 1 time(s).
IsListSorted_numpy(): True =>Time Elasped: 3.867 msec, repeated 1 time(s).
IsListSorted_fastk(): True =>Time Elasped: 279.322 msec, repeated 1 time(s).
Test 8: MiddleNotchList, list len: 1000000
IsListSorted_rand(): True =>Time Elasped: 0.387 msec, repeated 1 time(s).
IsListSorted_numpy(): False =>Time Elasped: 4.792 msec, repeated 1 time(s).
IsListSorted_rand(): False =>Time Elasped: 78.903 msec, repeated 1 time(s).
IsListSorted_fastk(): False =>Time Elasped: 110.444 msec, repeated 1 time(s).

可见，在绝大部分测试场景中，_rand()性能均为最佳，且不失正确率。注意测试8，当降序列表中间某个元素被置0(开槽)时，随机采样很容易遗漏该元素，导致误判。然而，这种场景在实际使用中非常罕见。

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