我仍然在解决嵌套JSON文件的问题。嵌套的项目是List或Dict：
这是我想要展平的文件（与我以前的帖子不同，我保持它的长度不错，但它只包含输入[0]而不是任何后续项目，因为它会很长）：
input = [{'states': ['USED'], 'niceName': '1-series', 'id': 'BMW_1_Series',
'years': [{'styles':
[{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '128i 2dr Convertible (3.0L 6cyl 6M)', 'id': 100994560},
{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '128i 2dr Coupe (3.0L 6cyl 6M)', 'id': 100974974},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '135i 2dr Coupe (3.0L 6cyl Turbo 6M)', 'id': 100974975},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '135i 2dr Convertible (3.0L 6cyl Turbo 6M)', 'id': 100994561}
],
'states': ['USED'], 'id': 100524709, 'year': 2008},
{'styles':
[{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '135i 2dr Coupe (3.0L 6cyl Turbo 6M)', 'id': 101082656},
{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '128i 2dr Coupe (3.0L 6cyl 6M)', 'id': 101082655},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '135i 2dr Convertible (3.0L 6cyl Turbo 6M)', 'id': 101082663},
{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '128i 2dr Convertible (3.0L 6cyl 6M)', 'id': 101082662}
],
'states': ['USED'], 'id': 100503222, 'year': 2009},
{'styles':
[{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '128i 2dr Coupe (3.0L 6cyl 6M)', 'id': 101200599},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '135i 2dr Coupe (3.0L 6cyl Turbo 6M)', 'id': 101200600},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '135i 2dr Convertible (3.0L 6cyl Turbo 6M)', 'id': 101200607},
{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '128i 2dr Convertible (3.0L 6cyl 6M)', 'id': 101200601}
],
'states': ['USED'], 'id': 100529091, 'year': 2010},
{'styles':
[{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '128i 2dr Coupe (3.0L 6cyl 6M)', 'id': 101288165},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '135i 2dr Coupe (3.0L 6cyl Turbo 6M)', 'id': 101288166},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '135i 2dr Convertible (3.0L 6cyl Turbo 6M)', 'id': 101288298},
{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '128i 2dr Convertible (3.0L 6cyl 6M)', 'id': 101288297}
],
'states': ['USED'], 'id': 100531309, 'year': 2011},
{'styles':
[{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '128i 2dr Convertible (3.0L 6cyl 6M)', 'id': 101381667},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '135i 2dr Convertible (3.0L 6cyl Turbo 6M)', 'id': 101381668},
{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '128i 2dr Coupe (3.0L 6cyl 6M)', 'id': 101381665},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '135i 2dr Coupe (3.0L 6cyl Turbo 6M)', 'id': 101381666}
],
'states': ['USED'], 'id': 100534729, 'year': 2012},
{'styles':
[{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '128i 2dr Coupe (3.0L 6cyl 6M)', 'id': 200428722},
{'trim': '128i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '128i 2dr Convertible (3.0L 6cyl 6M)', 'id': 200428721},
{'trim': '135is', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '135is 2dr Coupe (3.0L 6cyl Turbo 6M)', 'id': 200421701},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '135i 2dr Coupe (3.0L 6cyl Turbo 6M)', 'id': 200428724},
{'trim': '135i', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '135i 2dr Convertible (3.0L 6cyl Turbo 6M)', 'id': 200428723},
{'trim': '128i SULEV', 'states': ['USED'], 'submodel': {'body': 'Coupe', 'niceName': 'coupe', 'modelName': '1 Series Coupe'},
'name': '128i SULEV 2dr Coupe (3.0L 6cyl 6M)', 'id': 200428726},
{'trim': '128i SULEV', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '128i SULEV 2dr Convertible (3.0L 6cyl 6M)', 'id': 200428725},
{'trim': '135is', 'states': ['USED'], 'submodel': {'body': 'Convertible', 'niceName': 'convertible', 'modelName': '1 Series Convertible'},
'name': '135is 2dr Convertible (3.0L 6cyl Turbo 6M)', 'id': 200428727}
],
'states': ['USED'], 'id': 200421700, 'year': 2013}
],
'name': '1 Series', 'make': {'niceName': 'bmw', 'name': 'BMW', 'id': 200000081}
}, #here is more to come, but I needed to crop it
]
我失败了之后到目前为止使用的代码是由@poke编写的：Flattening Generic JSON List of Dicts or Lists in Python
def splitObj (obj, prefix = None):
'''
Split the object, returning a 3-tuple with the flat object, optionally
followed by the key for the subobjects and a list of those subobjects.
'''
# copy the object, optionally add the prefix before each key
new = obj.copy() if prefix is None else { '{}_{}'.format(prefix, k): v for k, v in obj.items() }
# try to find the key holding the subobject or a list of subobjects
for k, v in new.items():
# list of subobjects
if isinstance(v, list):
del new[k]
return new, k, v
# or just one subobject
elif isinstance(v, dict):
del new[k]
return new, k, [v]
return new, None, None
def flatten (data, prefix = None):
'''
Flatten the data, optionally with each key prefixed.
'''
# iterate all items
for item in data:
# split the object
flat, key, subs = splitObj(item, prefix)
# just return fully flat objects
if key is None:
yield flat
continue
# otherwise recursively flatten the subobjects
for sub in flatten(subs, key):
sub.update(flat)
yield sub
我收到以下错误：
AttributeError: 'str' object has no attribute 'items'
来自'states': ['USED']
我不知道如何处理。关键状态＆＃39;可以保存为列表。
我希望有人可以帮助我。
Ps：这是来自Python: Write Nested JSON as multiple elements in List
的后续帖子答案 0 :(得分：0)
这是我对splitObj的解决方案
def splitObj (obj, prefix = None):
'''
Split the object, returning a 3-tuple with the flat object, optionally
followed by the key for the subobjects and a list of those subobjects.
obj needs to be a Dictonary
'''
# copy the object, optionally add the prefix before each key
new = obj.copy() if prefix is None or prefix=="NotFlat" else { '{}_{}'.format(prefix, k): v for k, v in obj.items() }
cL = 0
cD = 0
# try to find the key holding the subobject or a list of subobjects
for k, v in new.items():
#Determine the number of lists in v
if isinstance(v, list):
cL += 1
#Determine the number of dict in v
elif isinstance(v, dict):
cD += 1
for k, v in new.items():
# list of subobjects
if isinstance(v, list):
if (cD+cL) <=1:
try:
type(v[0])
except IndexError:
v = [""]
if not isinstance(v[0], str):
del new[k]
return new, k, v
elif isinstance(v[0], str):
#handle list when only containing strings, return, the whole thing
#solve other dicts which might be in the line
#use "NotFlat" to run loop again but without adding a prefix
new[k] = ", ".join(v)
return new, None, None
else:
custLog.logger.info("")
elif (cD+cL) >1:
#print("Count List2 CD: "+str(cD))
#print("Count LIST2 CL: "+str(cL))
#if list is empty
try:
type(v[0])
except IndexError:
v = [""]
if not isinstance(v[0], str):
del new[k]
for x in flatten([new]):
newOut = x
break
return newOut, k, v
elif isinstance(v[0], str):
#handle list when only containing strings, return, the whole thing
#solve other dicts which might be in the line
#use "NotFlat" to run loop again but without adding a prefix
new[k] = ", ".join(v)
return None, "NotFlat", [new]
else:
custLog.logger.error("weder noch 2")
# or just one subobject
elif isinstance(v, dict):
if (cD+cL) <=1:
del new[k]
return new, k, [v]
elif (cD+cL) >1:
del new[k]
for x in flatten([new]):
newOut = x
break
return newOut, k, [v]
return new, None, None
此处为flatten
def flatten (data, prefix = None):
'''
Flatten the data, optionally with each key prefixed.
'''
# iterate all items
for item in data:
# split the object
flat, key, subs = splitObj(item, prefix)
if subs is None:
if key is None:
yield flat
continue
# just return fully flat objects
if key is None and flat is not None:
yield flat
continue
# otherwise recursively flatten the subobjects
try:
for sub in flatten(subs, key):
if flat is not None:
sub.update(flat)
yield sub
except TypeError as e:
custLog.logger.error("ERR: TypeError"+str(e))
答案 1 :(得分：0)
虽然不是一般化函数，但考虑遍历每个嵌套元素以获得用于数据库导入或flatfile（csv，txt）导出的平面输出。由于json文件由字典和列表的组合组成，因此在每个级别相应地处理它们：
<ul class="tab-group"><li class="tab active"><a href="#signup">3D</a></li><li class="tab"><a href="#login">REF</a></li></ul>
输出 （其中父项为每个孩子重复）
items = []
for outer in data:
inner = [''] * 15
for outerk, outerv in outer.items():
inner[0] = outer['states'][0]
inner[1] = outer['niceName']
inner[2] = outer['id']
inner[3] = outer['make']['niceName']
inner[4] = outer['make']['name']
inner[5] = outer['make']['id']
if outerk == 'years':
for yri in outer[outerk]:
for yrk, yrv in yri.items():
inner[6] = yri['states'][0]
inner[7] = yri['id']
inner[8] = yri['year']
if yrk == 'styles':
for stylei in yri[yrk]:
inner[9] = stylei['trim']
inner[10] = stylei['name']
inner[11] = stylei['id']
inner[12] = stylei['submodel']['body']
inner[13] = stylei['submodel']['niceName']
inner[14] = stylei['submodel']['modelName']
items.append(inner[0:14])
for i in items:
print(i)
答案 2 :(得分：0)
为更普遍的问题找到解决方案通常更容易。所以，让我们先看看问题。
输入是一些描述一组对象的JSON文件。
对象被重新定义为原子（字符串或数字）或具有对象值的字典。列表用于表示备选方案（即列表的任何元素都可以代替列表）。
例如，{a:[1,2]}表示a可以是1或2。
输出应该是不包含任何选项的对象列表。此外，对象应该是扁平的，即应该是其值为原子且其键描述原始对象中值的路径的dicts。
我的解决方案分别处理替代方案和扁平化。
下面的函数normalise接受json.dumps的输入并产生一系列dicts。请注意，normalise的输入和输出具有相同的语义并描述相同的对象集。输出只是标准化，因为它确实包含仅在顶层的备选方案。数据库人员会称之为非规范化，因为它对于关系数据库是不可取的。
normalise始终返回一系列对象。 normalise被实现为生成器以保持较低的内存使用率。
normalise区分以下案例。
以下是代码：
import itertools
def normalise(x):
if isinstance(x, dict):
keys = x.keys()
values = (normalise(i) for i in x.values())
for i in itertools.product(*values):
yield (dict(zip(keys, i)))
elif isinstance(x, list):
#if not x: # uncomment for "LEFT JOIN" behaviour
# yield None
for i in x:
yield from normalise(i)
else:
yield x
如果该代码包含任何空列表，则此代码不会返回该对象。这是因为没有可能的价值。这就像SQL＆＃34; INNER JOIN＆＃34;。从Bert的回答看起来他想要＆＃34; LEFT JOIN＆＃34;行为（即一些默认值）。为了实现这一点，只需取消注释这两行。
normalise产生的对象仍然具有原始（嵌套）dict结构。可以使用其他讨论中的代码来展平它们。
但是，OP希望将对象插入数据库中。因此，他很可能不需要扁平字典的键列表。他只需要一个返回给定路径值的函数。
这可以通过为具有__getitem__方法的dict创建包装器对象来实现。此包装器还可用于返回不存在路径的默认值。
class DictWrapper:
def __init__(self, d, sep='.'):
self.d = d
self.sep = sep
def __getitem__(self, key):
ret = self.d
try:
for k in key.split(self.sep):
ret = ret[k]
return ret
except KeyError:
return None
sql插件可能看起来如下（使用psycopg2测试）
for i in normalise(input):
cur.execute('insert into mytable (year) VALUES (%(years.year)s)', DictWrapper(i))
为了清晰起见，这个实现明显牺牲了一些运行时性能。
可以使用抽象基类代替list和dict。但是，这可能会有问题，因为str是一个序列，但应该被视为原子。
DictWrapper仅在任何dict键中未包含sep时才能正常工作。
normalise不会过滤掉重复项。这可以通过使用集合和命名元组而不是列表和dicts来完成。但是，这意味着整个结果必须在记忆中。最好在数据库级别过滤掉重复项。
为了将内存使用量保持在最低限度，应该懒惰地阅读JSON。