听说你会 Python (2):Python 高阶数据结构解析

前言

之前写过一篇《听说你会 Python ?》的文章,大家反响都还不错,那么我想干脆把这个文章做成一个系列,继续讲解一下 Python 当中那些不为人知的细节吧。然后之前在和师父川爷讨论面试的时候,川爷说了一句“要是我,我就考考你们怎么去实现一个 namedtuple ,好用,方便,又能区分人”,说者无心,听者有意,我于是决定在这次的文章中,和大家聊一聊 Python 中一个特殊的高阶数据结构, namedtuple 的实现。

Let’s begin

namedtuple

介绍

tuple 是 Python 中 build-in 的一种特殊的数据结构,它是一种 immutable 的数据集合,我们经常会这样使用它

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def test():
a = (1, 2)
print(a)
return a


if __name__ == '__main__':
b, c = test()
print(a)

Right,很多时候我们会直接使用 tuple 来进行一些数据的 packing/unpacking 的操作。OK,关于 tuple 的科普就到这里。那么什么是 namedtuple 呢,恩,前面不是说了 tuple 是一种特殊的数据集合么,那么 namedtuple 是其一个进阶(这不是废话么)。它将会基础的 tuple 抽象成一个类,我们将自行定义变量的名称和类的名称,这样我们可以很方便的将其复用并管理。具体的用法我们可以看看下面这个例子

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if __name__ == '__main__':
fuck=namedtuple("fuck", ['x', 'y'])
a=fuck(1,2)
print(a.x)
print(a.y)

恩,这样看起来貌似更直观了点,但是,但是,但是,我猜你肯定想知道 namedtuple 是怎么实现的,那么我们先来看看代码吧

详解

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_class_template = '''\
class {typename}(tuple):
'{typename}({arg_list})'

__slots__ = ()

_fields = {field_names!r}

def __new__(_cls, {arg_list}):
'Create new instance of {typename}({arg_list})'
return _tuple.__new__(_cls, ({arg_list}))

@classmethod
def _make(cls, iterable, new=tuple.__new__, len=len):
'Make a new {typename} object from a sequence or iterable'
result = new(cls, iterable)
if len(result) != {num_fields:d}:
raise TypeError('Expected {num_fields:d} arguments, got %d' % len(result))
return result

def __repr__(self):
'Return a nicely formatted representation string'
return '{typename}({repr_fmt})' % self

def _asdict(self):
'Return a new OrderedDict which maps field names to their values'
return OrderedDict(zip(self._fields, self))

def _replace(_self, **kwds):
'Return a new {typename} object replacing specified fields with new values'
result = _self._make(map(kwds.pop, {field_names!r}, _self))
if kwds:
raise ValueError('Got unexpected field names: %r' % kwds.keys())
return result

def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return tuple(self)

__dict__ = _property(_asdict)

def __getstate__(self):
'Exclude the OrderedDict from pickling'
pass

{field_defs}
'''

_repr_template = '{name}=%r'

_field_template = '''\
{name} = _property(_itemgetter({index:d}), doc='Alias for field number {index:d}')
'''

def namedtuple(typename, field_names, verbose=False, rename=False):
"""Returns a new subclass of tuple with named fields.

>>> Point = namedtuple('Point', ['x', 'y'])
>>> Point.__doc__ # docstring for the new class
'Point(x, y)'
>>> p = Point(11, y=22) # instantiate with positional args or keywords
>>> p[0] + p[1] # indexable like a plain tuple
33
>>> x, y = p # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessible by name
33
>>> d = p._asdict() # convert to a dictionary
>>> d['x']
11
>>> Point(**d) # convert from a dictionary
Point(x=11, y=22)
>>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
Point(x=100, y=22)

"""

# Validate the field names. At the user's option, either generate an error
# message or automatically replace the field name with a valid name.
if isinstance(field_names, basestring):
field_names = field_names.replace(',', ' ').split()
field_names = map(str, field_names)
typename = str(typename)
if rename:
seen = set()
for index, name in enumerate(field_names):
if (not all(c.isalnum() or c=='_' for c in name)
or _iskeyword(name)
or not name
or name[0].isdigit()
or name.startswith('_')
or name in seen):
field_names[index] = '_%d' % index
seen.add(name)
for name in [typename] + field_names:
if type(name) != str:
raise TypeError('Type names and field names must be strings')
if not all(c.isalnum() or c=='_' for c in name):
raise ValueError('Type names and field names can only contain '
'alphanumeric characters and underscores: %r' % name)
if _iskeyword(name):
raise ValueError('Type names and field names cannot be a '
'keyword: %r' % name)
if name[0].isdigit():
raise ValueError('Type names and field names cannot start with '
'a number: %r' % name)
seen = set()
for name in field_names:
if name.startswith('_') and not rename:
raise ValueError('Field names cannot start with an underscore: '
'%r' % name)
if name in seen:
raise ValueError('Encountered duplicate field name: %r' % name)
seen.add(name)

# Fill-in the class template
class_definition = _class_template.format(
typename = typename,
field_names = tuple(field_names),
num_fields = len(field_names),
arg_list = repr(tuple(field_names)).replace("'", "")[1:-1],
repr_fmt = ', '.join(_repr_template.format(name=name)
for name in field_names),
field_defs = '\n'.join(_field_template.format(index=index, name=name)
for index, name in enumerate(field_names))
)
if verbose:
print class_definition

# Execute the template string in a temporary namespace and support
# tracing utilities by setting a value for frame.f_globals['__name__']
namespace = dict(_itemgetter=_itemgetter, __name__='namedtuple_%s' % typename,
OrderedDict=OrderedDict, _property=property, _tuple=tuple)
try:
exec class_definition in namespace
except SyntaxError as e:
raise SyntaxError(e.message + ':\n' + class_definition)
result = namespace[typename]

# For pickling to work, the __module__ variable needs to be set to the frame
# where the named tuple is created. Bypass this step in environments where
# sys._getframe is not defined (Jython for example) or sys._getframe is not
# defined for arguments greater than 0 (IronPython).
try:
result.__module__ = _sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
pass

return result

这,这,这,这特么什么玩意儿啊!没事,我们慢慢来看。
首先,下面这一部分代码,将会校验我们传入的数据是否符合要求

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if isinstance(field_names, basestring):
field_names = field_names.replace(',', ' ').split()
field_names = map(str, field_names)
typename = str(typename)
if rename:
seen = set()
for index, name in enumerate(field_names):
if (not all(c.isalnum() or c=='_' for c in name)
or _iskeyword(name)
or not name
or name[0].isdigit()
or name.startswith('_')
or name in seen):
field_names[index] = '_%d' % index
seen.add(name)
for name in [typename] + field_names:
if type(name) != str:
raise TypeError('Type names and field names must be strings')
if not all(c.isalnum() or c=='_' for c in name):
raise ValueError('Type names and field names can only contain '
'alphanumeric characters and underscores: %r' % name)
if _iskeyword(name):
raise ValueError('Type names and field names cannot be a '
'keyword: %r' % name)
if name[0].isdigit():
raise ValueError('Type names and field names cannot start with '
'a number: %r' % name)
seen = set()
for name in field_names:
if name.startswith('_') and not rename:
raise ValueError('Field names cannot start with an underscore: '
'%r' % name)
if name in seen:
raise ValueError('Encountered duplicate field name: %r' % name)
seen.add(name)

接着,便是我们 namedtuple 的核心代码

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class_definition = _class_template.format(
typename = typename,
field_names = tuple(field_names),
num_fields = len(field_names),
arg_list = repr(tuple(field_names)).replace("'", "")[1:-1],
repr_fmt = ', '.join(_repr_template.format(name=name)
for name in field_names),
field_defs = '\n'.join(_field_template.format(index=index, name=name)
for index, name in enumerate(field_names))
)
if verbose:
print class_definition

# Execute the template string in a temporary namespace and support
# tracing utilities by setting a value for frame.f_globals['__name__']
namespace = dict(_itemgetter=_itemgetter, __name__='namedtuple_%s' % typename,
OrderedDict=OrderedDict, _property=property, _tuple=tuple)
try:
exec class_definition in namespace
except SyntaxError as e:
raise SyntaxError(e.message + ':\n' + class_definition)
result = namespace[typename]

你是不是想说,what the fuck!我知道,class_definition_repr_template_field_template 是前面所定义的字符串模板

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_class_template = '''\
class {typename}(tuple):
'{typename}({arg_list})'

__slots__ = ()

_fields = {field_names!r}

def __new__(_cls, {arg_list}):
'Create new instance of {typename}({arg_list})'
return _tuple.__new__(_cls, ({arg_list}))

@classmethod
def _make(cls, iterable, new=tuple.__new__, len=len):
'Make a new {typename} object from a sequence or iterable'
result = new(cls, iterable)
if len(result) != {num_fields:d}:
raise TypeError('Expected {num_fields:d} arguments, got %d' % len(result))
return result

def __repr__(self):
'Return a nicely formatted representation string'
return '{typename}({repr_fmt})' % self

def _asdict(self):
'Return a new OrderedDict which maps field names to their values'
return OrderedDict(zip(self._fields, self))

def _replace(_self, **kwds):
'Return a new {typename} object replacing specified fields with new values'
result = _self._make(map(kwds.pop, {field_names!r}, _self))
if kwds:
raise ValueError('Got unexpected field names: %r' % kwds.keys())
return result

def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return tuple(self)

__dict__ = _property(_asdict)

def __getstate__(self):
'Exclude the OrderedDict from pickling'
pass

{field_defs}
'''

_repr_template = '{name}=%r'

_field_template = '''\
{name} = _property(_itemgetter({index:d}), doc='Alias for field number {index:d}')
'''

但是其余的是什么鬼啊!别急,字符串模板我们先放在一边,我们先来看看后面的一段代码

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namespace = dict(_itemgetter=_itemgetter, __name__='namedtuple_%s' % typename,
OrderedDict=OrderedDict, _property=property, _tuple=tuple)
try:
exec class_definition in namespace
except SyntaxError as e:
raise SyntaxError(e.message + ':\n' + class_definition)
result = namespace[typename]

在这段代码中,首先 namespace 变量是一个字典,里面设置了一些变量的存在,紧接就是 exec class_definition in namespace 。众所周知,Python 是一门动态语言,在 Python 中,解释器允许我们在运行时,生成一些包含了符合 Python 语法语句的字符串,并用 exec 将其作为 Python 代码进行执行。同时在我们生成一些语句字符串的时候,我们可能会使用一些自定义的变量,于是,我们需要提供一个 dict 供其进行变量的查找。知道前面这些知识点后,exec class_definition in namespace 的作用是不是就很清楚了捏。
好了,我们再回过头去看 class_definition 定义。不过我们直接看未格式化之前的模板未免的太过于枯燥和难懂了,我们干脆以前面举过的一个例子来看看格式化后的 class_definition 吧~

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class fuck(tuple):
'fuck(x, y)'

__slots__ = ()

_fields = ('x', 'y')

def __new__(_cls, x, y):
'Create new instance of fuck(x, y)'
return _tuple.__new__(_cls, (x, y))

@classmethod
def _make(cls, iterable, new=tuple.__new__, len=len):
'Make a new fuck object from a sequence or iterable'
result = new(cls, iterable)
if len(result) != 2:
raise TypeError('Expected 2 arguments, got %d' % len(result))
return result

def __repr__(self):
'Return a nicely formatted representation string'
return 'fuck(x=%r, y=%r)' % self

def _asdict(self):
'Return a new OrderedDict which maps field names to their values'
return OrderedDict(zip(self._fields, self))

def _replace(_self, **kwds):
'Return a new fuck object replacing specified fields with new values'
result = _self._make(map(kwds.pop, ('x', 'y'), _self))
if kwds:
raise ValueError('Got unexpected field names: %r' % kwds.keys())
return result

def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return tuple(self)

__dict__ = _property(_asdict)

def __getstate__(self):
'Exclude the OrderedDict from pickling'
pass

x = _property(_itemgetter(0), doc='Alias for field number 0')

y = _property(_itemgetter(1), doc='Alias for field number 1')

好了,让我们一点点来分析,首先 class fuck(tuple) 指明我们创建的 fuck 类是继承自 tuple 。紧接着 __new__ 是 Python 对象系统中的一个特殊方法,用于我们的实例化的操作,其在 __init__ 之前便被触发,其是一个特殊的静态方法,我们可以将其用于实例缓存等特殊的功能。在这里,__new__ 将会返回一个 tuple 的实例。
接下来的是是一些特殊的私有方法,代码很好懂,我们就不细讲了,接着我们来看看这样一段代码

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x = _property(_itemgetter(0), doc='Alias for field number 0')

y = _property(_itemgetter(1), doc='Alias for field number 1')

你可能还不知道这两段代码用来是干什么的233,没事儿,我们慢慢来。
还记得前面我们举过的一个例子么

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if __name__ == '__main__':
fuck=namedtuple("fuck", ['x', 'y'])
a=fuck(1,2)
print(a.x)
print(a.y)

你可能会突发奇想,要是我们执行 a.x=1 这样的操作会怎样呢?OK,你会发现,Python 会抛出一个异常叫做 AttributeError: can't set attribute ,嗯哼,讲到这里,你可能就知道前面提到的包含 property 的两行代码作用就是保证 namedtupleimmutable 的特性。那么你可能还是不知道这是为什么。这和 Python 增加的描述符机制有关

扩展(1):Python 中的描述符

首先我们要明确一点,描述符指的是实现了描述符协议的特殊的类,三个描述符协议指的是 __get__ , ‘set‘ , __delete__ 以及 Python 3.6 中新增的 __set_name__ 方法,其中实现了 __get__ 以及 __set__ / __delete__ / __set_name__ 的是 Data descriptors ,而只实现了 __get__ 的是 Non-Data descriptor 。那么有什么区别呢,前面说了, 我们如果调用一个属性,那么其顺序是优先从实例的 __dict__ 里查找,然后如果没有查找到的话,那么一次查询类字典,父类字典,直到彻底查不到为止。 但是,这里没有考虑描述符的因素进去,如果将描述符因素考虑进去,那么正确的表述应该是我们如果调用一个属性,那么其顺序是优先从实例的 __dict__ 里查找,然后如果没有查找到的话,那么一次查询类字典,父类字典,直到彻底查不到为止。其中如果在类实例字典中的该属性是一个 Data descriptors ,那么无论实例字典中存在该属性与否,无条件走描述符协议进行调用,在类实例字典中的该属性是一个 Non-Data descriptors ,那么优先调用实例字典中的属性值而不触发描述符协议,如果实例字典中不存在该属性值,那么触发 Non-Data descriptor 的描述符协议

可能这讲完了,你还是不清楚和前面问题有什么关联,没事儿,我们接下来会讲讲 property 的实现

扩展(2):Property 详解

首先我们来看看关于 Property 的实现

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class Property(object):
"Emulate PyProperty_Type() in Objects/descrobject.c"
def __init__(self, fget=None, fset=None, fdel=None, doc=None):
self.fget = fget
self.fset = fset
self.fdel = fdel
if doc is None and fget is not None:
doc = fget.__doc__
self.__doc__ = doc

def __get__(self, obj, objtype=None):
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
return self.fget(obj)

def __set__(self, obj, value):
if self.fset is None:
raise AttributeError("can't set attribute")
self.fset(obj, value)

def __delete__(self, obj):
if self.fdel is None:
raise AttributeError("can't delete attribute")
self.fdel(obj)

def getter(self, fget):
return type(self)(fget, self.fset, self.fdel, self.__doc__)

def setter(self, fset):
return type(self)(self.fget, fset, self.fdel, self.__doc__)

def deleter(self, fdel):
return type(self)(self.fget, self.fset, fdel, self.__doc__)

当我们执行完这两句语句时

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x = _property(_itemgetter(0), doc='Alias for field number 0')

y = _property(_itemgetter(1), doc='Alias for field number 1')

我们的 xy 就变成了一个 property 对象的实例,它们也是一个描述符,还记得我们前面讲的么,当一个变量/成员成为一个描述符后,它将改变正常的调用逻辑,现在当我们 a.x=1 的时候,因为我们的x是一个 Data descriptors ,那么不管我们的实例字典中是否有 x 的存在,我们都会触发其 __set__ 方法,由于在我们初始化 xy 两个变量时,没有给予其传入 fset 的方法,因此,我们 __set__ 方法在运行过程中将会抛出 AttributeError("can't set attribute") 的异常,这也保证了 namedtuple 遵循了 tupleimmutable 的特性!是不是很优美!Amazing!

吐槽向

其实很多人不知道我为什么选择 namedtuple 来作为本期的主题,其实很简单呀,namedtuple 中预定义模板,格式化,然后用 exec 函数进行执行这一套方法,是目前 Python 中主流模板引擎的核心原理。某种意义上讲,你在吃透这一点后,你也掌握了怎样去实现一个简易模板引擎的方法,如果大家有兴趣,我们可以下次一起来写一个简单的模板引擎。还有就是在 namedtuple 对于 Python 中的一些高阶特性使用的简直优美无比,这也是我们学习的好例子。

最后的最后,作为另一个写的非常优美的例子,我将 orderdict 的代码贴出来,大家可以下来看看,然后评论区我们讨论一个!

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class OrderedDict(dict):
'Dictionary that remembers insertion order'
# An inherited dict maps keys to values.
# The inherited dict provides __getitem__, __len__, __contains__, and get.
# The remaining methods are order-aware.
# Big-O running times for all methods are the same as regular dictionaries.

# The internal self.__map dict maps keys to links in a doubly linked list.
# The circular doubly linked list starts and ends with a sentinel element.
# The sentinel element never gets deleted (this simplifies the algorithm).
# Each link is stored as a list of length three: [PREV, NEXT, KEY].

def __init__(*args, **kwds):
'''Initialize an ordered dictionary. The signature is the same as
regular dictionaries, but keyword arguments are not recommended because
their insertion order is arbitrary.

'''
if not args:
raise TypeError("descriptor '__init__' of 'OrderedDict' object "
"needs an argument")
self = args[0]
args = args[1:]
if len(args) > 1:
raise TypeError('expected at most 1 arguments, got %d' % len(args))
try:
self.__root
except AttributeError:
self.__root = root = [] # sentinel node
root[:] = [root, root, None]
self.__map = {}
self.__update(*args, **kwds)

def __setitem__(self, key, value, dict_setitem=dict.__setitem__):
'od.__setitem__(i, y) <==> od[i]=y'
# Setting a new item creates a new link at the end of the linked list,
# and the inherited dictionary is updated with the new key/value pair.
if key not in self:
root = self.__root
last = root[0]
last[1] = root[0] = self.__map[key] = [last, root, key]
return dict_setitem(self, key, value)

def __delitem__(self, key, dict_delitem=dict.__delitem__):
'od.__delitem__(y) <==> del od[y]'
# Deleting an existing item uses self.__map to find the link which gets
# removed by updating the links in the predecessor and successor nodes.
dict_delitem(self, key)
link_prev, link_next, _ = self.__map.pop(key)
link_prev[1] = link_next # update link_prev[NEXT]
link_next[0] = link_prev # update link_next[PREV]

def __iter__(self):
'od.__iter__() <==> iter(od)'
# Traverse the linked list in order.
root = self.__root
curr = root[1] # start at the first node
while curr is not root:
yield curr[2] # yield the curr[KEY]
curr = curr[1] # move to next node

def __reversed__(self):
'od.__reversed__() <==> reversed(od)'
# Traverse the linked list in reverse order.
root = self.__root
curr = root[0] # start at the last node
while curr is not root:
yield curr[2] # yield the curr[KEY]
curr = curr[0] # move to previous node

def clear(self):
'od.clear() -> None. Remove all items from od.'
root = self.__root
root[:] = [root, root, None]
self.__map.clear()
dict.clear(self)

# -- the following methods do not depend on the internal structure --

def keys(self):
'od.keys() -> list of keys in od'
return list(self)

def values(self):
'od.values() -> list of values in od'
return [self[key] for key in self]

def items(self):
'od.items() -> list of (key, value) pairs in od'
return [(key, self[key]) for key in self]

def iterkeys(self):
'od.iterkeys() -> an iterator over the keys in od'
return iter(self)

def itervalues(self):
'od.itervalues -> an iterator over the values in od'
for k in self:
yield self[k]

def iteritems(self):
'od.iteritems -> an iterator over the (key, value) pairs in od'
for k in self:
yield (k, self[k])

update = MutableMapping.update

__update = update # let subclasses override update without breaking __init__

__marker = object()

def pop(self, key, default=__marker):
'''od.pop(k[,d]) -> v, remove specified key and return the corresponding
value. If key is not found, d is returned if given, otherwise KeyError
is raised.

'''
if key in self:
result = self[key]
del self[key]
return result
if default is self.__marker:
raise KeyError(key)
return default

def setdefault(self, key, default=None):
'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'
if key in self:
return self[key]
self[key] = default
return default

def popitem(self, last=True):
'''od.popitem() -> (k, v), return and remove a (key, value) pair.
Pairs are returned in LIFO order if last is true or FIFO order if false.

'''
if not self:
raise KeyError('dictionary is empty')
key = next(reversed(self) if last else iter(self))
value = self.pop(key)
return key, value

def __repr__(self, _repr_running={}):
'od.__repr__() <==> repr(od)'
call_key = id(self), _get_ident()
if call_key in _repr_running:
return '...'
_repr_running[call_key] = 1
try:
if not self:
return '%s()' % (self.__class__.__name__,)
return '%s(%r)' % (self.__class__.__name__, self.items())
finally:
del _repr_running[call_key]

def __reduce__(self):
'Return state information for pickling'
items = [[k, self[k]] for k in self]
inst_dict = vars(self).copy()
for k in vars(OrderedDict()):
inst_dict.pop(k, None)
if inst_dict:
return (self.__class__, (items,), inst_dict)
return self.__class__, (items,)

def copy(self):
'od.copy() -> a shallow copy of od'
return self.__class__(self)

@classmethod
def fromkeys(cls, iterable, value=None):
'''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S.
If not specified, the value defaults to None.

'''
self = cls()
for key in iterable:
self[key] = value
return self

def __eq__(self, other):
'''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive
while comparison to a regular mapping is order-insensitive.

'''
if isinstance(other, OrderedDict):
return dict.__eq__(self, other) and all(_imap(_eq, self, other))
return dict.__eq__(self, other)

def __ne__(self, other):
'od.__ne__(y) <==> od!=y'
return not self == other

# -- the following methods support python 3.x style dictionary views --

def viewkeys(self):
"od.viewkeys() -> a set-like object providing a view on od's keys"
return KeysView(self)

def viewvalues(self):
"od.viewvalues() -> an object providing a view on od's values"
return ValuesView(self)

def viewitems(self):
"od.viewitems() -> a set-like object providing a view on od's items"
return ItemsView(self)

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