将嵌套的Python dict转换为对象？

有一个名为的集合帮助器namedtuple,可以为您执行此操作:

from collections import namedtuple

d_named = namedtuple('Struct', d.keys())(*d.values())

In [7]: d_named
Out[7]: Struct(a=1, b={'c': 2}, d=['hi', {'foo': 'bar'}])

In [8]: d_named.a
Out[8]: 1

class Struct(object):
    """Comment removed"""
    def __init__(self, data):
        for name, value in data.iteritems():
            setattr(self, name, self._wrap(value))

    def _wrap(self, value):
        if isinstance(value, (tuple, list, set, frozenset)): 
            return type(value)([self._wrap(v) for v in value])
        else:
            return Struct(value) if isinstance(value, dict) else value

可以与任何深度的任何序列/字典/值结构一起使用.

我认为这是前面例子中最好的方面,这就是我想出的:

class Struct:
  '''The recursive class for building and representing objects with.'''
  def __init__(self, obj):
    for k, v in obj.iteritems():
      if isinstance(v, dict):
        setattr(self, k, Struct(v))
      else:
        setattr(self, k, v)
  def __getitem__(self, val):
    return self.__dict__[val]
  def __repr__(self):
    return '{%s}' % str(', '.join('%s : %s' % (k, repr(v)) for
      (k, v) in self.__dict__.iteritems()))

如果你的dict来自json.loads(),你可以把它变成一个对象(而不是一个dict)在一行:

import json
from collections import namedtuple

json.loads(data, object_hook=lambda d: namedtuple('X', d.keys())(*d.values()))

另请参见如何将JSON数据转换为Python对象.

如果你想要将dict键作为一个对象(或者作为一个困难键的dict)访问,那么递归执行它,并且还能够更新原始dict,你可以这样做:

class Dictate(object):
    """Object view of a dict, updating the passed in dict when values are set
    or deleted. "Dictate" the contents of a dict...: """

    def __init__(self, d):
        # since __setattr__ is overridden, self.__dict = d doesn't work
        object.__setattr__(self, '_Dictate__dict', d)

    # Dictionary-like access / updates
    def __getitem__(self, name):
        value = self.__dict[name]
        if isinstance(value, dict):  # recursively view sub-dicts as objects
            value = Dictate(value)
        return value

    def __setitem__(self, name, value):
        self.__dict[name] = value
    def __delitem__(self, name):
        del self.__dict[name]

    # Object-like access / updates
    def __getattr__(self, name):
        return self[name]

    def __setattr__(self, name, value):
        self[name] = value
    def __delattr__(self, name):
        del self[name]

    def __repr__(self):
        return "%s(%r)" % (type(self).__name__, self.__dict)
    def __str__(self):
        return str(self.__dict)

用法示例:

d = {'a': 'b', 1: 2}
dd = Dictate(d)
assert dd.a == 'b'  # Access like an object
assert dd[1] == 2  # Access like a dict
# Updates affect d
dd.c = 'd'
assert d['c'] == 'd'
del dd.a
del dd[1]
# Inner dicts are mapped
dd.e = {}
dd.e.f = 'g'
assert dd['e'].f == 'g'
assert d == {'c': 'd', 'e': {'f': 'g'}}

>>> def dict2obj(d):
        if isinstance(d, list):
            d = [dict2obj(x) for x in d]
        if not isinstance(d, dict):
            return d
        class C(object):
            pass
        o = C()
        for k in d:
            o.__dict__[k] = dict2obj(d[k])
        return o


>>> d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
>>> x = dict2obj(d)
>>> x.a
1
>>> x.b.c
2
>>> x.d[1].foo
'bar'

我最终尝试了AttrDict和束库,并发现它们对我的使用来说太慢了.在我和朋友调查之后,我们发现编写这些库的主要方法导致库通过嵌套对象进行主动递归,并在整个过程中复制字典对象.考虑到这一点,我们做了两个关键的改变.1)我们使属性延迟加载2)而不是创建字典对象的副本,我们创建轻量级代理对象的副本.这是最终的实施.使用此代码的性能提升令人难以置信.当使用AttrDict或Bunch时,这两个库分别占用了我的请求时间的1/2和1/3(什么!？).这段代码将时间缩短到几乎没有(在0.5ms的范围内).这当然取决于您的需求,

class DictProxy(object):
    def __init__(self, obj):
        self.obj = obj

    def __getitem__(self, key):
        return wrap(self.obj[key])

    def __getattr__(self, key):
        try:
            return wrap(getattr(self.obj, key))
        except AttributeError:
            try:
                return self[key]
            except KeyError:
                raise AttributeError(key)

    # you probably also want to proxy important list properties along like
    # items(), iteritems() and __len__

class ListProxy(object):
    def __init__(self, obj):
        self.obj = obj

    def __getitem__(self, key):
        return wrap(self.obj[key])

    # you probably also want to proxy important list properties along like
    # __iter__ and __len__

def wrap(value):
    if isinstance(value, dict):
        return DictProxy(value)
    if isinstance(value, (tuple, list)):
        return ListProxy(value)
    return value

请通过/sf/ask/17360801/查看此处的原始实施.

另外需要注意的是,这个实现非常简单,并没有实现您可能需要的所有方法.您需要在DictProxy或ListProxy对象上根据需要编写它们.

x.__dict__.update(d) 应该没事.

这应该让你开始:

class dict2obj(object):
    def __init__(self, d):
        self.__dict__['d'] = d

    def __getattr__(self, key):
        value = self.__dict__['d'][key]
        if type(value) == type({}):
            return dict2obj(value)

        return value

d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}

x = dict2obj(d)
print x.a
print x.b.c
print x.d[1].foo

它不适用于列表.您必须将列表包装在UserList中并重载__getitem__以包装dicts.

我知道这里已经有很多答案了，我参加聚会很晚，但是这种方法将递归地将“字典”转换成类似对象的结构...在3.xx中有效

def dictToObject(d):
    for k,v in d.items():
        if isinstance(v, dict):
            d[k] = dictToObject(v)
    return namedtuple('object', d.keys())(*d.values())

# Dictionary created from JSON file
d = {
    'primaryKey': 'id', 
    'metadata': 
        {
            'rows': 0, 
            'lastID': 0
        }, 
    'columns': 
        {
            'col2': {
                'dataType': 'string', 
                'name': 'addressLine1'
            }, 
            'col1': {
                'datatype': 'string', 
                'name': 'postcode'
            }, 
            'col3': {
                'dataType': 'string', 
                'name': 'addressLine2'
            }, 
            'col0': {
                'datatype': 'integer', 
                'name': 'id'
            }, 
            'col4': {
                'dataType': 'string', 
                'name': 'contactNumber'
            }
        }, 
        'secondaryKeys': {}
}

d1 = dictToObject(d)
d1.columns.col1 # == object(datatype='string', name='postcode')
d1.metadata.rows # == 0