Python标准库之functools/itertools/operator

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引言

functools , itertools , operator 是Python标准库为我们提供的支持函数式编程的三大模块，合理的使用这三个模块，我们可以写出更加简洁可读的Pythonic代码，接下来我们通过一些example来了解三大模块的使用。

functools的使用

partial

>>> from functools import partial

>>> basetwo = partial(int, base=2)

>>> basetwo('10010')
18

上述过程实际上等价于调用int(‘10010’, base=2)，而partial内部实际上就是通过一个简单的闭包来实现的

defpartial(func, *args, **keywords):
    defnewfunc(*fargs, **fkeywords):
        newkeywords = keywords.copy()
        newkeywords.update(fkeywords)
        return func(*args, *fargs, **newkeywords)
    newfunc.func = func
    newfunc.args = args
    newfunc.keywords = keywords
    return newfunc

partialmethod

partialmethod和partial类似，但是对于 绑定一个非对象自身的方法 的时候，这个时候就只能使用partialmethod了，我们通过下面这个例子来看一下两者的差异

>>> from functools import partial, partialmethod

>>> defstandalone(self, a=1, b=2):
...     "Standalone function"
...     print(' called standalone with:', (self, a, b))
...     if self is not None:
...         print(' self.attr =', self.attr)

>>> classMyClass:
...     "Demonstration class for functools"
...     def__init__(self):
...         self.attr = 'instance attribute'
...     method1 = functools.partialmethod(standalone)  # 使用partialmethod
...     method2 = functools.partial(standalone)  # 使用partial

>>> o = MyClass()

>>> o.method1()
  called standalone with: (<__main__.MyClass object at 0x7f46d40cc550>, 1, 2)
  self.attr = instance attribute

# 不能使用partial
>>> o.method2()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: standalone() missing 1 required positional argument: 'self'

singledispatch

虽然Python不支持同名方法允许有不同的参数类型，但是我们可以借用singledispatch来 动态指定相应的方法所接收的参数类型 ，而不用把参数判断放到方法内部去判断从而降低代码的可读性

from functools import singledispatch


classTestClass(object):
 @singledispatch
    deftest_method(arg, verbose=False):
        if verbose:
            print("Let me just say,", end=" ")
        print(arg)

 @test_method.register(int)
    def_(arg):
        print("Strength in numbers, eh?", end=" ")
        print(arg)

 @test_method.register(list)
    def_(arg):
        print("Enumerate this:")

        for i, elem in enumerate(arg):
            print(i, elem)


if __name__ == '__main__':
    TestClass.test_method(55555)  # call @test_method.register(int)
    TestClass.test_method([33, 22, 11])   # call @test_method.register(list)
    TestClass.test_method('hello world', verbose=True)  # call default

根据运行结果来解释一下上面这段代码，上面通过@test_method.register(int)和@test_method.register(list)指定当test_method的第一个参数为int或者list的时候，分别调用不同的方法来进行处理

Enumerate this:
0 33
1 22
2 11
Let me just say, hello world

wraps

装饰器会遗失被装饰函数的__name__和__doc__等属性，可以使用@wraps来恢复

>>> from functools import wraps

>>> defmy_decorator(f):
...     @wraps(f)
...     defwrapper():
...         """wrapper_doc"""
...         print('Calling decorated function')
...         return f()
...     return wrapper
...

>>> @my_decorator
... defexample():
...     """example_doc"""
...     print('Called example function')
...

>>> example.__name__
'example'
>>> example.__doc__
'example_doc'

# 尝试去掉@wraps(f)来看一下运行结果，example自身的__name__和__doc__都已经丧失了
>>> example.__name__
'wrapper'
>>> example.__doc__
'wrapper_doc'

我们也可以使用update_wrapper来改写

defg():
    ...
g = update_wrapper(g, f)

# 等价于
@wraps(f)
defg():
     ...

@wraps内部实际上就是基于update_wrapper来实现的

defwraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES):
    defdecorator(wrapper):
        return update_wrapper(wrapper, wrapped=wrapped...)
    return decorator

total_ordering

Python2中可以通过自定义__cmp__的返回值0/-1/1来比较对象的大小，在Python3中废弃了__cmp__，但是我们可以通过total ordering然后修改/ _lt__() , __le__() , __eq__() , __ne__() , __gt__(), __ge__()等魔术方法来自定义类的比较规则

import functools


@functools.total_ordering
classMyObject:
    def__init__(self, val):
        self.val = val

    def__eq__(self, other):
        print(' testing __eq__({}, {})'.format(
            self.val, other.val))
        return self.val == other.val

    def__gt__(self, other):
        print(' testing __gt__({}, {})'.format(
            self.val, other.val))
        return self.val > other.val


a = MyObject(1)
b = MyObject(2)

for expr in ['a < b', 'a <= b', 'a == b', 'a >= b', 'a > b']:
    print('/n{:<6}:'.format(expr))
    result = eval(expr)
    print(' result of {}: {}'.format(expr, result))

下面是运行结果

a < b :
  testing __gt__(1, 2)
  testing __eq__(1, 2)
  result of a < b: True

a <= b:
  testing __gt__(1, 2)
  result of a <= b: True

a == b:
  testing __eq__(1, 2)
  result of a == b: False

a >= b:
  testing __gt__(1, 2)
  testing __eq__(1, 2)
  result of a >= b: False

a > b :
  testing __gt__(1, 2)
  result of a > b: False

itertools的使用

Infinite Iterators

count

count(start=0, step=1) 返回一个无限的整数流，每次增加1。你可以选择提供起始编号，默认为0

>>> from itertools import count

>>> for i in zip(count(1), ['a', 'b', 'c']):
...     print(i, end=' ')
...
(1, 'a') (2, 'b') (3, 'c')

cycle

cycle(iterable) saves a copy of the contents of a provided iterable and returns a new iterator that returns its elements from first to last. The new iterator will repeat these elements infinitely.

>>> from itertools import cycle

>>> for i in zip(range(6), cycle(['a', 'b', 'c'])):
...     print(i, end=' ')
...
(0, 'a') (1, 'b') (2, 'c') (3, 'a') (4, 'b') (5, 'c')

repeat

repeat(object[, times]) 返回提供的元素n次，如果未提供n则无限次返回

>>> from itertools import repeat

>>> for i, s in zip(count(1), repeat('over-and-over', 5)):
...     print(i, s)
...
1 over-and-over
2 over-and-over
3 over-and-over
4 over-and-over
5 over-and-over

Iterators terminating on the shortest input sequence

accumulate

accumulate(iterable[, func])

>>> from itertools import accumulate
>>> import operator

>>> list(accumulate([1, 2, 3, 4, 5], operator.add))
[1, 3, 6, 10, 15]

>>> list(accumulate([1, 2, 3, 4, 5], operator.mul))
[1, 2, 6, 24, 120]

chain

itertools.chain(*iterables)可以将多个iterable组合成一个iterator

>>> from itertools import chain

>>> list(chain([1, 2, 3], ['a', 'b', 'c']))
[1, 2, 3, 'a', 'b', 'c']

chain的实现原理如下

defchain(*iterables):
    # chain('ABC', 'DEF') --> A B C D E F
    for it in iterables:
        for element in it:
            yield element

chain.from_iterable

chain.from_iterable(iterable)和chain类似，但是只是接收单个iterable，然后将这个iterable中的元素组合成一个iterator

>>> from itertools import chain

>>> list(chain.from_iterable(['ABC', 'DEF']))
['A', 'B', 'C', 'D', 'E', 'F']

实现原理也和chain类似

deffrom_iterable(iterables):
    # chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
    for it in iterables:
        for element in it:
            yield element

compress

compress(data, selectors)接收两个iterable作为参数，只返回selectors中对应的元素为True的data，当data/selectors之一用尽时停止

>>> list(compress([1, 2, 3, 4, 5], [True, True, False, False, True]))
[1, 2, 5]

zip_longest

zip_longest(*iterables, fillvalue=None)和zip类似，但是zip的缺陷是iterable中的某一个元素被遍历完，整个遍历都会停止，具体差异请看下面这个例子

from itertools import zip_longest

r1 = range(3)
r2 = range(2)

print('zip stops early:')
print(list(zip(r1, r2)))

r1 = range(3)
r2 = range(2)

print('/nzip_longest processes all of the values:')
print(list(zip_longest(r1, r2)))

下面是输出结果

zip stops early:
[(0, 0), (1, 1)]

zip_longest processes all of the values:
[(0, 0), (1, 1), (2, None)]

islice

islice(iterable, stop) or islice(iterable, start, stop[, step]) 与Python的字符串和列表切片有一些，只是你不能对start、start和step使用负值

>>> from itertools import islice

>>> for i in islice(range(100), 0, 100, 10):
...     print(i, end=' ')
...
0 10 20 30 40 50 60 70 80 90

tee

tee(iterable, n=2) 返回n个独立的iterator，n默认为2

from itertools import islice, tee

r = islice(count(), 5)
i1, i2 = tee(r)

print('i1:', list(i1))
print('i2:', list(i2))

for i in r:
    print(i, end=' ')
    if i > 1:
        break

下面是输出结果，注意tee(r)后，r作为iterator已经失效，所以for循环没有输出值

i1: [0, 1, 2, 3, 4]
i2: [0, 1, 2, 3, 4]

starmap

starmap(func, iterable)假设iterable将返回一个元组流，并使用这些元组作为参数调用func：

>>> from itertools import starmap
>>> import os

>>> iterator = starmap(os.path.join,
...                    [('/bin', 'python'), ('/usr', 'bin', 'java'),
...                    ('/usr', 'bin', 'perl'), ('/usr', 'bin', 'ruby')])

>>> list(iterator)
['/bin/python', '/usr/bin/java', '/usr/bin/perl', '/usr/bin/ruby']

filterfalse

filterfalse(predicate, iterable) 与filter()相反，返回所有predicate返回False的元素

itertools.filterfalse(is_even, itertools.count()) =>
1, 3, 5, 7, 9, 11, 13, 15, ...

takewhile

takewhile(predicate, iterable) 只要predicate返回True，不停地返回iterable中的元素。一旦predicate返回False，iteration将结束

def less_than_10(x):
    return x < 10

itertools.takewhile(less_than_10, itertools.count())
=> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9

itertools.takewhile(is_even, itertools.count())
=> 0

dropwhile

dropwhile(predicate, iterable) 在predicate返回True时舍弃元素，然后返回其余迭代结果

itertools.dropwhile(less_than_10, itertools.count())
=> 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...

itertools.dropwhile(is_even, itertools.count())
=> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...

groupby

groupby(iterable, key=None) collects all the consecutive elements from the underlying iterable that have the same key value, and returns a stream of 2-tuples containing a key value and an iterator for the elements with that key. If you don’t supply a key function, the key is simply each element itself.

• [k for k, g in groupby(‘AAAABBBCCDAABBB’)] –> A B C D A B
• [list(g) for k, g in groupby(‘AAAABBBCCD’)] –> AAAA BBB CC D

例子很清晰，直接看例子，上面的定义我就不翻译了

city_list = [('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL'),
             ('Anchorage', 'AK'), ('Nome', 'AK'),
             ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ'),
             ...
            ]

def get_state(city_state):
    return city_state[1]

itertools.groupby(city_list, get_state) =>
  ('AL', iterator-1),
  ('AK', iterator-2),
  ('AZ', iterator-3), ...

iterator-1 =>  ('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL')
iterator-2 => ('Anchorage', 'AK'), ('Nome', 'AK')
iterator-3 => ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ')

groupby() assumes that the underlying iterable’s contents will already be sorted based on the key.

Combinatoric generators

product

product(*iterables, repeat=1)

• product(A, B) returns the same as ((x,y) for x in A for y in B)
• product(A, repeat=4) means the same as product(A, A, A, A)

from itertools import product


defshow(iterable):
    for i, item in enumerate(iterable, 1):
        print(item, end=' ')
        if (i % 3) == 0:
            print()
    print()


print('Repeat 2:/n')
show(product(range(3), repeat=2))

print('Repeat 3:/n')
show(product(range(3), repeat=3))

Repeat 2:

(0, 0) (0, 1) (0, 2)
(1, 0) (1, 1) (1, 2)
(2, 0) (2, 1) (2, 2)

Repeat 3:

(0, 0, 0) (0, 0, 1) (0, 0, 2)
(0, 1, 0) (0, 1, 1) (0, 1, 2)
(0, 2, 0) (0, 2, 1) (0, 2, 2)
(1, 0, 0) (1, 0, 1) (1, 0, 2)
(1, 1, 0) (1, 1, 1) (1, 1, 2)
(1, 2, 0) (1, 2, 1) (1, 2, 2)
(2, 0, 0) (2, 0, 1) (2, 0, 2)
(2, 1, 0) (2, 1, 1) (2, 1, 2)
(2, 2, 0) (2, 2, 1) (2, 2, 2)

permutations

permutations(iterable, r=None)返回长度为r的所有可能的组合

from itertools import permutations


defshow(iterable):
    first = None
    for i, item in enumerate(iterable, 1):
        if first != item[0]:
            if first is not None:
                print()
            first = item[0]
        print(''.join(item), end=' ')
    print()


print('All permutations:/n')
show(permutations('abcd'))

print('/nPairs:/n')
show(permutations('abcd', r=2))

下面是输出结果

All permutations:

abcd abdc acbd acdb adbc adcb
bacd badc bcad bcda bdac bdca
cabd cadb cbad cbda cdab cdba
dabc dacb dbac dbca dcab dcba

Pairs:

ab ac ad
ba bc bd
ca cb cd
da db dc

combinations

combinations(iterable, r) 返回一个iterator，提供iterable中所有元素可能组合的r元组。每个元组中的元素保持与iterable返回的顺序相同。下面的实例中，不同于上面的permutations，a总是在bcd之前，b总是在cd之前，c总是在d之前

from itertools import combinations


defshow(iterable):
    first = None
    for i, item in enumerate(iterable, 1):
        if first != item[0]:
            if first is not None:
                print()
            first = item[0]
        print(''.join(item), end=' ')
    print()


print('Unique pairs:/n')
show(combinations('abcd', r=2))

下面是输出结果

Unique pairs:

ab ac ad
bc bd
cd

combinations_with_replacement

combinations_with_replacement(iterable, r)函数放宽了一个不同的约束：元素可以在单个元组中重复，即可以出现aa/bb/cc/dd等组合

from itertools import combinations_with_replacement


defshow(iterable):
    first = None
    for i, item in enumerate(iterable, 1):
        if first != item[0]:
            if first is not None:
                print()
            first = item[0]
        print(''.join(item), end=' ')
    print()


print('Unique pairs:/n')
show(combinations_with_replacement('abcd', r=2))

下面是输出结果

aa ab ac ad
bb bc bd
cc cd
dd

operator的使用

attrgetter

operator.attrgetter( attr )和operator.attrgetter( *attrs )

• After f = attrgetter(‘name’), the call f(b) returns b.name.
• After f = attrgetter(‘name’, ‘date’), the call f(b) returns (b.name, b.date).
• After f = attrgetter(‘name.first’, ‘name.last’), the call f(b) returns (b.name.first, b.name.last).

我们通过下面这个例子来了解一下itergetter的用法

>>> classStudent:
...     def__init__(self, name, grade, age):
...         self.name = name
...         self.grade = grade
...         self.age = age
...     def__repr__(self):
...         return repr((self.name, self.grade, self.age))

>>> student_objects = [
...     Student('john', 'A', 15),
...     Student('jane', 'B', 12),
...     Student('dave', 'B', 10),
... ]

>>> sorted(student_objects, key=lambda student: student.age)   # 传统的lambda做法
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]

>>> from operator import itemgetter, attrgetter

>>> sorted(student_objects, key=attrgetter('age'))
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]

# 但是如果像下面这样接受双重比较，Python脆弱的lambda就不适用了
>>> sorted(student_objects, key=attrgetter('grade', 'age'))
[('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]

attrgetter的实现原理

defattrgetter(*items):
    if any(not isinstance(item, str) for item in items):
        raise TypeError('attribute name must be a string')
    if len(items) == 1:
        attr = items[0]
        defg(obj):
            return resolve_attr(obj, attr)
    else:
        defg(obj):
            return tuple(resolve_attr(obj, attr) for attr in items)
    return g

defresolve_attr(obj, attr):
    for name in attr.split("."):
        obj = getattr(obj, name)
    return obj

itemgetter

operator.itemgetter(item)和operator.itemgetter(*items)

• After f = itemgetter(2), the call f(r) returns r[2].
• After g = itemgetter(2, 5, 3), the call g(r) returns (r[2], r[5], r[3]).

我们通过下面这个例子来了解一下itergetter的用法

>>> student_tuples = [
...     ('john', 'A', 15),
...     ('jane', 'B', 12),
...     ('dave', 'B', 10),
... ]

>>> sorted(student_tuples, key=lambda student: student[2])   # 传统的lambda做法
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]

>>> from operator import attrgetter

>>> sorted(student_tuples, key=itemgetter(2))
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]

# 但是如果像下面这样接受双重比较，Python脆弱的lambda就不适用了
>>> sorted(student_tuples, key=itemgetter(1,2))
[('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]

itemgetter的实现原理

defitemgetter(*items):
    if len(items) == 1:
        item = items[0]
        defg(obj):
            return obj[item]
    else:
        defg(obj):
            return tuple(obj[item] for item in items)
    return g

methodcaller

operator.methodcaller(name[, args…])

• After f = methodcaller(‘name’), the call f(b) returns b.name().
• After f = methodcaller(‘name’, ‘foo’, bar=1), the call f(b) returns b.name(‘foo’, bar=1).

methodcaller的实现原理

defmethodcaller(name, *args, **kwargs):
    defcaller(obj):
        return getattr(obj, name)(*args, **kwargs)
    return caller

References