我有一个字符串列表,我想执行一个自然的字母排序。
例如,下面的列表是自然排序(我想要的):
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
下面是上面列表的“排序”版本(我使用sorted()得到的):
['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
我在寻找一个排序函数它的行为和第一个一样。
我有一个字符串列表,我想执行一个自然的字母排序。
例如,下面的列表是自然排序(我想要的):
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
下面是上面列表的“排序”版本(我使用sorted()得到的):
['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
我在寻找一个排序函数它的行为和第一个一样。
当前回答
很可能functools.cmp_to_key()与python的sort的底层实现密切相关。此外,cmp参数是遗留的。现代的方法是将输入项转换为支持所需的丰富比较操作的对象。
在CPython 2下。X,即使没有实现各自的富比较操作符,也可以对不同类型的对象排序。在CPython 3下。X,不同类型的对象必须显式地支持比较。参见Python如何比较字符串和int?链接到官方文件。大多数答案都依赖于这种隐含的顺序。切换到Python 3。X将需要一个新的类型来实现和统一数字和字符串之间的比较。
Python 2.7.12 (default, Sep 29 2016, 13:30:34)
>>> (0,"foo") < ("foo",0)
True
Python 3.5.2 (default, Oct 14 2016, 12:54:53)
>>> (0,"foo") < ("foo",0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unorderable types: int() < str()
有三种不同的方法。第一种使用嵌套类来利用Python的Iterable比较算法。第二个函数将这个嵌套展开到单个类中。第三种方法放弃了str的子类化,专注于性能。所有都是有时间的;第二辆快了一倍,第三辆快了近六倍。对str进行子类化并不是必需的,而且首先可能是一个坏主意,但它确实带来了某些便利。
排序字符被复制以强制按大小写排序,并交换大小写以强制小写字母优先排序;这就是“自然排序”的典型定义。我无法决定分组的类型;有些人可能更喜欢以下选项,这也会带来显著的性能优势:
d = lambda s: s.lower()+s.swapcase()
在使用的地方,比较运算符被设置为object的运算符,这样它们就不会被functools. total_orders忽略。
import functools
import itertools
@functools.total_ordering
class NaturalStringA(str):
def __repr__(self):
return "{}({})".format\
( type(self).__name__
, super().__repr__()
)
d = lambda c, s: [ c.NaturalStringPart("".join(v))
for k,v in
itertools.groupby(s, c.isdigit)
]
d = classmethod(d)
@functools.total_ordering
class NaturalStringPart(str):
d = lambda s: "".join(c.lower()+c.swapcase() for c in s)
d = staticmethod(d)
def __lt__(self, other):
if not isinstance(self, type(other)):
return NotImplemented
try:
return int(self) < int(other)
except ValueError:
if self.isdigit():
return True
elif other.isdigit():
return False
else:
return self.d(self) < self.d(other)
def __eq__(self, other):
if not isinstance(self, type(other)):
return NotImplemented
try:
return int(self) == int(other)
except ValueError:
if self.isdigit() or other.isdigit():
return False
else:
return self.d(self) == self.d(other)
__le__ = object.__le__
__ne__ = object.__ne__
__gt__ = object.__gt__
__ge__ = object.__ge__
def __lt__(self, other):
return self.d(self) < self.d(other)
def __eq__(self, other):
return self.d(self) == self.d(other)
__le__ = object.__le__
__ne__ = object.__ne__
__gt__ = object.__gt__
__ge__ = object.__ge__
import functools
import itertools
@functools.total_ordering
class NaturalStringB(str):
def __repr__(self):
return "{}({})".format\
( type(self).__name__
, super().__repr__()
)
d = lambda s: "".join(c.lower()+c.swapcase() for c in s)
d = staticmethod(d)
def __lt__(self, other):
if not isinstance(self, type(other)):
return NotImplemented
groups = map(lambda i: itertools.groupby(i, type(self).isdigit), (self, other))
zipped = itertools.zip_longest(*groups)
for s,o in zipped:
if s is None:
return True
if o is None:
return False
s_k, s_v = s[0], "".join(s[1])
o_k, o_v = o[0], "".join(o[1])
if s_k and o_k:
s_v, o_v = int(s_v), int(o_v)
if s_v == o_v:
continue
return s_v < o_v
elif s_k:
return True
elif o_k:
return False
else:
s_v, o_v = self.d(s_v), self.d(o_v)
if s_v == o_v:
continue
return s_v < o_v
return False
def __eq__(self, other):
if not isinstance(self, type(other)):
return NotImplemented
groups = map(lambda i: itertools.groupby(i, type(self).isdigit), (self, other))
zipped = itertools.zip_longest(*groups)
for s,o in zipped:
if s is None or o is None:
return False
s_k, s_v = s[0], "".join(s[1])
o_k, o_v = o[0], "".join(o[1])
if s_k and o_k:
s_v, o_v = int(s_v), int(o_v)
if s_v == o_v:
continue
return False
elif s_k or o_k:
return False
else:
s_v, o_v = self.d(s_v), self.d(o_v)
if s_v == o_v:
continue
return False
return True
__le__ = object.__le__
__ne__ = object.__ne__
__gt__ = object.__gt__
__ge__ = object.__ge__
import functools
import itertools
import enum
class OrderingType(enum.Enum):
PerWordSwapCase = lambda s: s.lower()+s.swapcase()
PerCharacterSwapCase = lambda s: "".join(c.lower()+c.swapcase() for c in s)
class NaturalOrdering:
@classmethod
def by(cls, ordering):
def wrapper(string):
return cls(string, ordering)
return wrapper
def __init__(self, string, ordering=OrderingType.PerCharacterSwapCase):
self.string = string
self.groups = [ (k,int("".join(v)))
if k else
(k,ordering("".join(v)))
for k,v in
itertools.groupby(string, str.isdigit)
]
def __repr__(self):
return "{}({})".format\
( type(self).__name__
, self.string
)
def __lesser(self, other, default):
if not isinstance(self, type(other)):
return NotImplemented
for s,o in itertools.zip_longest(self.groups, other.groups):
if s is None:
return True
if o is None:
return False
s_k, s_v = s
o_k, o_v = o
if s_k and o_k:
if s_v == o_v:
continue
return s_v < o_v
elif s_k:
return True
elif o_k:
return False
else:
if s_v == o_v:
continue
return s_v < o_v
return default
def __lt__(self, other):
return self.__lesser(other, default=False)
def __le__(self, other):
return self.__lesser(other, default=True)
def __eq__(self, other):
if not isinstance(self, type(other)):
return NotImplemented
for s,o in itertools.zip_longest(self.groups, other.groups):
if s is None or o is None:
return False
s_k, s_v = s
o_k, o_v = o
if s_k and o_k:
if s_v == o_v:
continue
return False
elif s_k or o_k:
return False
else:
if s_v == o_v:
continue
return False
return True
# functools.total_ordering doesn't create single-call wrappers if both
# __le__ and __lt__ exist, so do it manually.
def __gt__(self, other):
op_result = self.__le__(other)
if op_result is NotImplemented:
return op_result
return not op_result
def __ge__(self, other):
op_result = self.__lt__(other)
if op_result is NotImplemented:
return op_result
return not op_result
# __ne__ is the only implied ordering relationship, it automatically
# delegates to __eq__
>>> import natsort
>>> import timeit
>>> l1 = ['Apple', 'corn', 'apPlE', 'arbour', 'Corn', 'Banana', 'apple', 'banana']
>>> l2 = list(map(str, range(30)))
>>> l3 = ["{} {}".format(x,y) for x in l1 for y in l2]
>>> print(timeit.timeit('sorted(l3+["0"], key=NaturalStringA)', number=10000, globals=globals()))
362.4729259099986
>>> print(timeit.timeit('sorted(l3+["0"], key=NaturalStringB)', number=10000, globals=globals()))
189.7340817489967
>>> print(timeit.timeit('sorted(l3+["0"], key=NaturalOrdering.by(OrderingType.PerCharacterSwapCase))', number=10000, globals=globals()))
69.34636392899847
>>> print(timeit.timeit('natsort.natsorted(l3+["0"], alg=natsort.ns.GROUPLETTERS | natsort.ns.LOWERCASEFIRST)', number=10000, globals=globals()))
98.2531585780016
自然排序既相当复杂,又定义模糊。不要忘记事先运行unicodedata.normalize(…),并考虑使用str.casefold()而不是str.lower()。可能有一些微妙的编码问题我还没有考虑到。因此,我尝试性地推荐natsort库。我快速浏览了一下github存储库;代码维护非常出色。
All the algorithms I've seen depend on tricks such as duplicating and lowering characters, and swapping case. While this doubles the running time, an alternative would require a total natural ordering on the input character set. I don't think this is part of the unicode specification, and since there are many more unicode digits than [0-9], creating such a sorting would be equally daunting. If you want locale-aware comparisons, prepare your strings with locale.strxfrm per Python's Sorting HOW TO.
其他回答
一个紧凑的解决方案,基于将字符串转换为List[Tuple(str, int)]。
Code
def string_to_pairs(s, pairs=re.compile(r"(\D*)(\d*)").findall):
return [(text.lower(), int(digits or 0)) for (text, digits) in pairs(s)[:-1]]
示范
sorted(['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9'], key=string_to_pairs)
输出:
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
测试
转换
assert string_to_pairs("") == []
assert string_to_pairs("123") == [("", 123)]
assert string_to_pairs("abc") == [("abc", 0)]
assert string_to_pairs("123abc") == [("", 123), ("abc", 0)]
assert string_to_pairs("abc123") == [("abc", 123)]
assert string_to_pairs("123abc456") == [("", 123), ("abc", 456)]
assert string_to_pairs("abc123efg") == [("abc", 123), ("efg", 0)]
排序
# Some extracts from the test suite of the natsort library. Permalink:
# https://github.com/SethMMorton/natsort/blob/e3c32f5638bf3a0e9a23633495269bea0e75d379/tests/test_natsorted.py
sort_data = [
( # same as test_natsorted_can_sort_as_unsigned_ints_which_is_default()
["a50", "a51.", "a50.31", "a-50", "a50.4", "a5.034e1", "a50.300"],
["a5.034e1", "a50", "a50.4", "a50.31", "a50.300", "a51.", "a-50"],
),
( # same as test_natsorted_numbers_in_ascending_order()
["a2", "a5", "a9", "a1", "a4", "a10", "a6"],
["a1", "a2", "a4", "a5", "a6", "a9", "a10"],
),
( # same as test_natsorted_can_sort_as_version_numbers()
["1.9.9a", "1.11", "1.9.9b", "1.11.4", "1.10.1"],
["1.9.9a", "1.9.9b", "1.10.1", "1.11", "1.11.4"],
),
( # different from test_natsorted_handles_filesystem_paths()
[
"/p/Folder (10)/file.tar.gz",
"/p/Folder (1)/file (1).tar.gz",
"/p/Folder/file.x1.9.tar.gz",
"/p/Folder (1)/file.tar.gz",
"/p/Folder/file.x1.10.tar.gz",
],
[
"/p/Folder (1)/file (1).tar.gz",
"/p/Folder (1)/file.tar.gz",
"/p/Folder (10)/file.tar.gz",
"/p/Folder/file.x1.9.tar.gz",
"/p/Folder/file.x1.10.tar.gz",
],
),
( # same as test_natsorted_path_extensions_heuristic()
[
"Try.Me.Bug - 09 - One.Two.Three.[text].mkv",
"Try.Me.Bug - 07 - One.Two.5.[text].mkv",
"Try.Me.Bug - 08 - One.Two.Three[text].mkv",
],
[
"Try.Me.Bug - 07 - One.Two.5.[text].mkv",
"Try.Me.Bug - 08 - One.Two.Three[text].mkv",
"Try.Me.Bug - 09 - One.Two.Three.[text].mkv",
],
),
( # same as ns.IGNORECASE for test_natsorted_supports_case_handling()
["Apple", "corn", "Corn", "Banana", "apple", "banana"],
["Apple", "apple", "Banana", "banana", "corn", "Corn"],
),
]
for (given, expected) in sort_data:
assert sorted(given, key=string_to_pairs) == expected
奖金
如果字符串混合了非ascii文本和数字,您可能会对将string_to_pairs()与我在其他地方给出的函数remove_diacritics()组合感兴趣。
我写了一个基于http://www.codinghorror.com/blog/2007/12/sorting-for-humans-natural-sort-order.html的函数,它增加了传递自己的“键”参数的能力。我需要这样才能执行包含更复杂对象(不仅仅是字符串)的列表的自然排序。
import re
def natural_sort(list, key=lambda s:s):
"""
Sort the list into natural alphanumeric order.
"""
def get_alphanum_key_func(key):
convert = lambda text: int(text) if text.isdigit() else text
return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
sort_key = get_alphanum_key_func(key)
list.sort(key=sort_key)
例如:
my_list = [{'name':'b'}, {'name':'10'}, {'name':'a'}, {'name':'1'}, {'name':'9'}]
natural_sort(my_list, key=lambda x: x['name'])
print my_list
[{'name': '1'}, {'name': '9'}, {'name': '10'}, {'name': 'a'}, {'name': 'b'}]
在PyPI上有一个名为natsort的第三方库(完全公开,我是包的作者)。对于你的情况,你可以采取以下任何一种方法:
>>> from natsort import natsorted, ns
>>> x = ['Elm11', 'Elm12', 'Elm2', 'elm0', 'elm1', 'elm10', 'elm13', 'elm9']
>>> natsorted(x, key=lambda y: y.lower())
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
>>> natsorted(x, alg=ns.IGNORECASE) # or alg=ns.IC
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
您应该注意到natsort使用通用算法,因此它应该适用于您抛出的任何输入。如果您想了解更多关于为什么选择一个库来执行此操作而不是滚动自己的函数的详细信息,请查看natsort文档的How It Works页面,特别是特殊情况无处不在!部分。
如果需要排序键而不是排序函数,请使用以下公式之一。
>>> from natsort import natsort_keygen, ns
>>> l1 = ['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
>>> l2 = l1[:]
>>> natsort_key1 = natsort_keygen(key=lambda y: y.lower())
>>> l1.sort(key=natsort_key1)
>>> l1
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
>>> natsort_key2 = natsort_keygen(alg=ns.IGNORECASE)
>>> l2.sort(key=natsort_key2)
>>> l2
['elm0', 'elm1', 'Elm2', 'elm9', 'elm10', 'Elm11', 'Elm12', 'elm13']
2020年11月更新
假设一个流行的请求/问题是“如何像Windows资源管理器那样排序?”(或者不管你的操作系统的文件系统浏览器是什么),在natsort 7.1.0版本中,有一个叫做os_sorted的函数可以做到这一点。在Windows上,它将按照与Windows资源管理器相同的顺序进行排序,而在其他操作系统上,它应该像本地文件系统浏览器一样进行排序。
>>> from natsort import os_sorted
>>> os_sorted(list_of_paths)
# your paths sorted like your file system browser
对于那些需要排序键的人,可以使用os_sort_keygen(如果只需要默认值,也可以使用os_sort_key)。
注意:在使用此函数之前,请阅读该函数的API文档,以了解其限制以及如何获得最佳结果。
为了记录,下面是Mark Byers的简单解决方案的另一个变体,类似于Walter Tross建议的解决方案,避免调用isdigit()。这不仅使它更快,而且还避免了可能发生的问题,因为与regex \d+相比,isdigit()将更多的unicode字符视为数字。
import re
from itertools import cycle
_re_digits = re.compile(r"(\d+)")
def natural_comparison_key(key):
return tuple(
int(part) if is_digit else part
for part, is_digit in zip(_re_digits.split(key), cycle((False, True)))
)
a = ['H1', 'H100', 'H10', 'H3', 'H2', 'H6', 'H11', 'H50', 'H5', 'H99', 'H8']
b = ''
c = []
def bubble(bad_list):#bubble sort method
length = len(bad_list) - 1
sorted = False
while not sorted:
sorted = True
for i in range(length):
if bad_list[i] > bad_list[i+1]:
sorted = False
bad_list[i], bad_list[i+1] = bad_list[i+1], bad_list[i] #sort the integer list
a[i], a[i+1] = a[i+1], a[i] #sort the main list based on the integer list index value
for a_string in a: #extract the number in the string character by character
for letter in a_string:
if letter.isdigit():
#print letter
b += letter
c.append(b)
b = ''
print 'Before sorting....'
print a
c = map(int, c) #converting string list into number list
print c
bubble(c)
print 'After sorting....'
print c
print a
应答:
气泡排序作业
如何在python中一次读一个字母的字符串