我使用了以下函数（详细信息可以在here中找到）：

def flatten_data(y):
    out = {}

    def flatten(x, name=''):
        if type(x) is dict:
            for a in x:
                flatten(x[a], name + a + '_')
        elif type(x) is list:
            i = 0
            for a in x:
                flatten(a, name + str(i) + '_')
                i += 1
        else:
            out[name[:-1]] = x

    flatten(y)
    return out

不幸的是，这会使整个JSON完全扁平化，这意味着如果您有多层JSON（许多嵌套字典），它可能会将所有内容扁平化为一行，其中包含大量列。
最后，我使用的是json_normalize()和我所需要的指定结构。这里有一个很好的例子来说明如何用这种方法。

从https://stackoverflow.com/a/62186053/4355695交叉发布（但随后进一步调整）：在本回购协议中：https://github.com/ScriptSmith/socialreaper/blob/master/socialreaper/tools.py#L8，我找到了一个由@roneo对answer posted by @Imran执行的列表包含注解的实现。
我在函数中添加了捕捉空列表和空指令的检查，还添加了打印行，这将帮助人们准确理解函数是如何工作的，您可以通过设置crumbs=False来关闭这些打印语句

from collections import MutableMapping
crumbs = True
def flatten(dictionary, parent_key=False, separator='.'):
    """
    Turn a nested dictionary into a flattened dictionary
    :param dictionary: The dictionary to flatten
    :param parent_key: The string to prepend to dictionary's keys
    :param separator: The string used to separate flattened keys
    :return: A flattened dictionary
    """

    items = []
    for key, value in dictionary.items():
        if crumbs: print('checking:',key)
        new_key = str(parent_key) + separator + key if parent_key else key
        if isinstance(value, MutableMapping):
            if crumbs: print(new_key,': dict found')
            if not value.items():
                if crumbs: print('Adding key-value pair:',new_key,None)
                items.append((new_key,None))
            else:
                items.extend(flatten(value, new_key, separator).items())
        elif isinstance(value, list):
            if crumbs: print(new_key,': list found')
            if len(value):
                for k, v in enumerate(value):
                    items.extend(flatten({str(k): v}, new_key, separator).items())
            else:
                if crumbs: print('Adding key-value pair:',new_key,None)
                items.append((new_key,None))
        else:
            if crumbs: print('Adding key-value pair:',new_key,value)
            items.append((new_key, value))
    return dict(items)

测试：

ans = flatten({'a': 1, 'c': {'a': 2, 'b': {'x': 5, 'y' : 10}}, 'd': [1, 2, 3], 'e':{'f':[], 'g':{}} })
print('\nflattened:',ans)

输出：

checking: a
Adding key-value pair: a 1
checking: c
c : dict found
checking: a
Adding key-value pair: c.a 2
checking: b
c.b : dict found
checking: x
Adding key-value pair: c.b.x 5
checking: y
Adding key-value pair: c.b.y 10
checking: d
d : list found
checking: 0
Adding key-value pair: d.0 1
checking: 1
Adding key-value pair: d.1 2
checking: 2
Adding key-value pair: d.2 3
checking: e
e : dict found
checking: f
e.f : list found
Adding key-value pair: e.f None
checking: g
e.g : dict found
Adding key-value pair: e.g None

flattened: {'a': 1, 'c.a': 2, 'c.b.x': 5, 'c.b.y': 10, 'd.0': 1, 'd.1': 2, 'd.2': 3, 'e.f': None, 'e.g': None}

这就是我要做的事我把任何复杂的json都扔到这里，它会把它展平。我在原始代码中添加了一个检查来处理空列表
归功于https://github.com/ScriptSmith，我在它的repo中找到了初始扁平化函数。
测试OP的示例json，输出如下：

{'count': 13,
 'virtualmachine.0.id': '1082e2ed-ff66-40b1-a41b-26061afd4a0b',
 'virtualmachine.0.name': 'test-2',
 'virtualmachine.0.displayname': 'test-2',
 'virtualmachine.0.securitygroup.0.id': '9e649fbc-3e64-4395-9629-5e1215b34e58',
 'virtualmachine.0.securitygroup.0.name': 'test',
 'virtualmachine.0.securitygroup.0.tags': None,
 'virtualmachine.0.nic.0.id': '79568b14-b377-4d4f-b024-87dc22492b8e',
 'virtualmachine.0.nic.0.networkid': '05c0e278-7ab4-4a6d-aa9c-3158620b6471',
 'virtualmachine.0.nic.1.id': '3d7f2818-1f19-46e7-aa98-956526c5b1ad',
 'virtualmachine.0.nic.1.networkid': 'b4648cfd-0795-43fc-9e50-6ee9ddefc5bd',
 'virtualmachine.0.nic.1.traffictype': 'Guest',
 'virtualmachine.0.hypervisor': 'KVM',
 'virtualmachine.0.affinitygroup': None,
 'virtualmachine.0.isdynamicallyscalable': False}

所以你会看到“tags”和“affinitygroup”键也被处理并添加到输出中，原始代码省略了它们。
2021年5月30日更新：集合。可变Map更改为集合。abc。可变Map
2023年1月11日：已编辑，在第二个项目中添加了分隔符参数。根据@MHebes的建议调用extend（）

IMO accepted answer无法正确处理JSON数组。
如果JSON对象以数组作为值，则应将其扁平化为对象数组，如

{'a': [1, 2]} -> [{'a': 1}, {'a': 2}]

而不是向键添加索引。
嵌套对象应该通过连接键（例如，用点作为分隔符）来展平，如

{'a': {'b': 1}} -> {'a.b': 1}

(and这在接受的一个中被正确地完成）。
考虑到所有这些要求，我最终得到了以下内容（在CPython3.5.3中开发和使用）：

from functools import (partial,
                       singledispatch)
from itertools import chain
from typing import (Dict,
                    List,
                    TypeVar)

Serializable = TypeVar('Serializable', None, int, bool, float, str,
                       dict, list, tuple)
Array = List[Serializable]
Object = Dict[str, Serializable]

def flatten(object_: Object,
            *,
            path_separator: str = '.') -> Array[Object]:
    """
    Flattens given JSON object into list of objects with non-nested values.

    >>> flatten({'a': 1})
    [{'a': 1}]
    >>> flatten({'a': [1, 2]})
    [{'a': 1}, {'a': 2}]
    >>> flatten({'a': {'b': None}})
    [{'a.b': None}]
    >>> flatten({'a': [1, 2], 'b': []})
    [{'a': 1}, {'a': 2}]
    """
    keys = set(object_)
    result = [dict(object_)]
    while keys:
        key = keys.pop()
        new_result = []
        for index, record in enumerate(result):
            try:
                value = record[key]
            except KeyError:
                new_result.append(record)
            else:
                if isinstance(value, dict):
                    del record[key]
                    new_value = flatten_nested_objects(
                            value,
                            prefix=key + path_separator,
                            path_separator=path_separator
                    )
                    keys.update(new_value.keys())
                    new_result.append({**new_value, **record})
                elif isinstance(value, list):
                    del record[key]
                    new_records = [
                        flatten_nested_objects(sub_value,
                                               prefix=key + path_separator,
                                               path_separator=path_separator)
                        for sub_value in value
                    ]
                    keys.update(chain.from_iterable(map(dict.keys,
                                                        new_records)))
                    if new_records:
                        new_result.extend({**new_record, **record}
                                          for new_record in new_records)
                    else:
                        new_result.append(record)
                else:
                    new_result.append(record)
        result = new_result
    return result

@singledispatch
def flatten_nested_objects(object_: Serializable,
                           *,
                           prefix: str = '',
                           path_separator: str) -> Object:
    return {prefix[:-len(path_separator)]: object_}

@flatten_nested_objects.register(dict)
def _(object_: Object,
      *,
      prefix: str = '',
      path_separator: str) -> Object:
    result = dict(object_)
    for key in list(result):
        result.update(flatten_nested_objects(result.pop(key),
                                             prefix=(prefix + key
                                                     + path_separator),
                                             path_separator=path_separator))
    return result

@flatten_nested_objects.register(list)
def _(object_: Array,
      *,
      prefix: str = '',
      path_separator: str) -> Object:
    return {prefix[:-len(path_separator)]: list(map(partial(
            flatten_nested_objects,
            path_separator=path_separator),
            object_))}

如果其他人发现自己在这里，并正在寻找一个更适合随后的方案处理的解决方案：
扁平化列表需要处理列表长度等的标题。我想要一个解决方案，如果有2个列表，例如2个元素，则会生成4行，每个有效的潜在数据行（实际示例见下文）：

class MapFlattener:

    def __init__(self):
        self.headings = []
        self.rows = []

    def add_rows(self, headings, rows):
        self.headings = [*self.headings, *headings]
        if self.rows:
            new_rows = []
            for base_row in self.rows:
                for row in rows:
                    new_rows.append([*base_row, *row])
            self.rows = new_rows
        else:
            self.rows = rows

    def __call__(self, mapping):
        for heading, value in mapping.items():
            if isinstance(value, Mapping):
                sub_headings, sub_rows = MapFlattener()(value)
                sub_headings = [f'{heading}:{sub_heading}' for sub_heading in sub_headings]
                self.add_rows(sub_headings, sub_rows)
                continue

            if isinstance(value, list):
                self.add_rows([heading], [[e] for e in value])
                continue

            self.add_rows([heading], [[value]])

        return self.headings, self.rows

def map_flatten(mapping):
    return MapFlattener()(mapping)

这将创建更符合关系数据的输出：

In [22]: map_flatten({'l': [1,2]})                                                                                                          
Out[22]: (['l'], [[1], [2]])

In [23]: map_flatten({'l': [1,2], 'n': 7})                                                                                                  
Out[23]: (['l', 'n'], [[1, 7], [2, 7]])

In [24]: map_flatten({'l': [1,2], 'n': 7, 'o': {'a': 1, 'b': 2}})                                                                           
Out[24]: (['l', 'n', 'o:a', 'o:b'], [[1, 7, 1, 2], [2, 7, 1, 2]])

In [25]: map_flatten({'top': {'middle': {'bottom': [0, 1]}, 'ml': ['A', 'B']}, 'l': ['a', 'b']})                                             
Out[25]: 
(['top:middle:bottom', 'top:ml', 'l'],
 [[0, 'A', 'a'],
  [0, 'A', 'b'],
  [0, 'B', 'a'],
  [0, 'B', 'b'],
  [1, 'A', 'a'],
  [1, 'A', 'b'],
  [1, 'B', 'a'],
  [1, 'B', 'b']])

如果您在电子表格等中使用csv，并且需要处理展平的数据，则此功能特别有用。

我尝试使用BFS方法，其中只有当val是dict类型时，我才将（parent，瓦尔）存储在队列中。

def flattern_json(d):
    if len(d) == 0:
        return {}
    from collections import deque
    q = deque()
    res = dict()
    for key, val in d.items(): # This loop push the top most keys and values into queue.
        if not isinstance(val, dict):  # If it's not dict
            if isinstance(val, list):  # If it's list then check list values if it contains dict object.
                temp = list()  # Creating temp list for storing the values that we will need which are not dict.
                for v in val:
                    if not isinstance(v, dict):
                        temp.append(v)
                    else:
                        q.append((key, v))  # if it's value is dict type then we push along with parent which is key.
                if len(temp) > 0:
                    res[key] = temp
            else:
                res[key] = val
        else:
            q.append((key, val))
    while q:
        k, v = q.popleft()  # Taking parent and the value out of queue
        for key, val in v.items():
            new_parent = k + "_" + key  # New parent will be old parent_currentval
            if isinstance(val, list):
                temp = list()
                for v in val:
                    if not isinstance(v, dict):
                        temp.append(v)
                    else:
                        q.append((new_parent, v))
                if len(temp) >= 0:
                    res[new_parent] = temp
            elif not isinstance(val, dict):
                res[new_parent] = val
            else:
                q.append((new_parent, val))
    return res

它使用给定的JSON，我附加了_来扁平化JSON，而不是使用01列表索引。

from pprint import pprint

print(pprint.pprint(flattern_json(d)))

它给出了以下输出：

{'count': 13,
 'virtualmachine_affinitygroup': [],
 'virtualmachine_displayname': 'test-2',
 'virtualmachine_hypervisor': 'KVM',
 'virtualmachine_id': '1082e2ed-ff66-40b1-a41b-26061afd4a0b',
 'virtualmachine_isdynamicallyscalable': False,
 'virtualmachine_name': 'test-2',
 'virtualmachine_nic': [],
 'virtualmachine_nic_id': '3d7f2818-1f19-46e7-aa98-956526c5b1ad',
 'virtualmachine_nic_networkid': 'b4648cfd-0795-43fc-9e50-6ee9ddefc5bd',
 'virtualmachine_nic_traffictype': 'Guest',
 'virtualmachine_securitygroup': [],
 'virtualmachine_securitygroup_id': '9e649fbc-3e64-4395-9629-5e1215b34e58',
 'virtualmachine_securitygroup_name': 'test',
 'virtualmachine_securitygroup_tags': []}

我使用这个简单的函数将数据标准化和扁平化为json，它接受列表、dict、元组并将其扁平化为json。

def normalize_data_to_json(raw_data: [list, dict, tuple], parent=""):
    from datetime import datetime
    from decimal import Decimal

    result = {}
    # key name normalise to snake case (single underscore)
    parent = parent.lower().replace(" ", "_") if isinstance(parent, str) else parent
    if isinstance(parent, str) and parent.startswith("__"):
        # if parent has no parent remove double underscore and treat as int if digit else as str
        # treating as int is better if passed data is a list so you output is index based dict
        parent = int(parent.lstrip("_")) if parent.lstrip("_").isdigit() else parent.lstrip("_")

    # handle str, int, float, and decimal.
    # you can easily add more data types as er your data
    if type(raw_data) in [str, int, float, Decimal]:
        result[parent] = float(raw_data) if isinstance(raw_data, Decimal) else raw_data

    # normalise datetime object
    elif isinstance(raw_data, datetime):
        result[parent] = raw_data.strftime("%Y-%m-%d %H:%M:%S")

    # normalise dict and all nested dicts.
    # all nests are joined with double underscore to identify parent key name with it's children
    elif isinstance(raw_data, dict):
        for k, v in raw_data.items():
            k = f'{parent}__{k}' if parent else k
            result.update(normalize_data_to_json(v, parent=k))

    # normalise list and tuple
    elif type(raw_data) in [list, tuple]:
        for i, sub_item in enumerate(raw_data, start=1):
            result.update(normalize_data_to_json(sub_item, f"{parent}__{i}"))

    # any data which did not matched above data types, normalise them using it's __str__
    else:
        result[parent] = str(raw_data)

    return result

我读过很多不同的方法。这是唯一一个对我来说适用于复杂嵌套JSON的方法。这个方法是将嵌套JSON扁平化并将其转换为Pandas数据框，这样就更容易过滤掉你想要的任何元素。

import json
import pprint
import pandas as pd
from flatten_json import flatten

with open('sth.json') as json_file:
  nested_json = json.load(json_file)
nested_json = nested_json["_via_img_metadata"]

out = {}
def flatten(x, name=''):
    if type(x) is dict:
        for a in x:
            flatten(x[a], name + a + '_')
    elif type(x) is list:
        i = 0
        for a in x:
            flatten(a, name + str(i) + '_')
            i += 1
    else:
        out[name[:-1]] = x
    return out

df = pd.Series(flatten(nested_json)).to_frame()

以jsonpath格式输出：

def convert(f):
    out = {}
    def flatten(x, name=None):
        if type(x) is dict:
            for a in x:
                val = '.'.join((name, a)) if name else a
                flatten(x[a], val)
        elif type(x) is list:
            for (i, a) in enumerate(x):
                flatten(a, name + f'[{str(i)}]')
        else:
            out[name] = x if x else ""
    flatten(f)
    return out

把字典递过来：

def getKeyValuePair(dic,master_dic = {},master_key = None):
    keys = list(dic.keys())
    for key in keys:
        if type(dic[key]) == dict:
                getKeyValuePair(dic[key],master_dic = master_dic,master_key = key)
        else:
            if master_key == None:
                master_dic[key] = dic[key]
            else:
                master_dic[str(master_key)+'_'+str(key)] = dic[key]

   return master_dic