我知道这个问题已经被问到很长时间了，但我想我可以在其他人的答案上加上一篇博客文章，以一种非常简洁的方式解释解决方案。

这是链接

打开文件进行写入

employ_data = open('/tmp/EmployData.csv', 'w')

创建csv writer对象

csvwriter = csv.writer(employ_data)
count = 0
for emp in emp_data:
      if count == 0:
             header = emp.keys()
             csvwriter.writerow(header)
             count += 1
      csvwriter.writerow(emp.values())

为了保存内容，请确保关闭文件

employ_data.close()

由于数据看起来是字典格式，因此似乎应该实际使用csv.DictWriter()来实际输出带有适当标题信息的行。这将使转换更容易处理。然后fieldnames参数将正确地设置顺序，而第一行的输出作为标题将允许稍后由csv.DictReader()读取和处理。

例如，Mike Repass使用

output = csv.writer(sys.stdout)

output.writerow(data[0].keys())  # header row

for row in data:
  output.writerow(row.values())

不过，只需将初始设置更改为 输出= csv。DictWriter数据(文件集,字段名= [0]. keys ())

注意，由于字典中元素的顺序没有定义，您可能必须显式地创建字段名条目。一旦你这样做了，writerow就可以工作了。然后写操作就像最初显示的那样工作。

首先，JSON包含嵌套对象，因此通常不能直接转换为CSV。你需要把它改成这样:

{
    "pk": 22,
    "model": "auth.permission",
    "codename": "add_logentry",
    "content_type": 8,
    "name": "Can add log entry"
},
......]

下面是我的代码来生成CSV:

import csv
import json

x = """[
    {
        "pk": 22,
        "model": "auth.permission",
        "fields": {
            "codename": "add_logentry",
            "name": "Can add log entry",
            "content_type": 8
        }
    },
    {
        "pk": 23,
        "model": "auth.permission",
        "fields": {
            "codename": "change_logentry",
            "name": "Can change log entry",
            "content_type": 8
        }
    },
    {
        "pk": 24,
        "model": "auth.permission",
        "fields": {
            "codename": "delete_logentry",
            "name": "Can delete log entry",
            "content_type": 8
        }
    }
]"""

x = json.loads(x)

f = csv.writer(open("test.csv", "wb+"))

# Write CSV Header, If you dont need that, remove this line
f.writerow(["pk", "model", "codename", "name", "content_type"])

for x in x:
    f.writerow([x["pk"],
                x["model"],
                x["fields"]["codename"],
                x["fields"]["name"],
                x["fields"]["content_type"]])

你会得到如下输出:

pk,model,codename,name,content_type
22,auth.permission,add_logentry,Can add log entry,8
23,auth.permission,change_logentry,Can change log entry,8
24,auth.permission,delete_logentry,Can delete log entry,8

这不是一个很聪明的方法，但我也遇到过同样的问题，这对我来说很有效:

import csv

f = open('data.json')
data = json.load(f)
f.close()

new_data = []

for i in data:
   flat = {}
   names = i.keys()
   for n in names:
      try:
         if len(i[n].keys()) > 0:
            for ii in i[n].keys():
               flat[n+"_"+ii] = i[n][ii]
      except:
         flat[n] = i[n]
   new_data.append(flat)  

f = open(filename, "r")
writer = csv.DictWriter(f, new_data[0].keys())
writer.writeheader()
for row in new_data:
   writer.writerow(row)
f.close()

JSON可以表示各种各样的数据结构——JS的“对象”大致类似于Python的dict(带有字符串键)，JS的“数组”大致类似于Python列表，只要最后的“叶子”元素是数字或字符串，你就可以嵌套它们。

CSV本质上只能表示一个2-D表——可选的第一行是“标题”，即“列名”，这可以使表可解释为字典列表，而不是正常的解释，一个列表的列表(同样，“叶子”元素可以是数字或字符串)。

So, in the general case, you can't translate an arbitrary JSON structure to a CSV. In a few special cases you can (array of arrays with no further nesting; arrays of objects which all have exactly the same keys). Which special case, if any, applies to your problem? The details of the solution depend on which special case you do have. Given the astonishing fact that you don't even mention which one applies, I suspect you may not have considered the constraint, neither usable case in fact applies, and your problem is impossible to solve. But please do clarify!

我已经尝试了很多建议的解决方案(也熊猫没有正确地规范化我的JSON)，但真正好的是正确解析JSON数据来自Max Berman。

我写了一个改进，以避免每一行都有新列 在解析期间将其放置到现有列。 如果只有一个数据存在，则将值存储为字符串，如果该列有更多值，则将值存储为列表。

它有一个输入。Json文件作为输入，并输出一个output.csv。

import json
import pandas as pd

def flatten_json(json):
    def process_value(keys, value, flattened):
        if isinstance(value, dict):
            for key in value.keys():
                process_value(keys + [key], value[key], flattened)
        elif isinstance(value, list):
            for idx, v in enumerate(value):
                process_value(keys, v, flattened)
                # process_value(keys + [str(idx)], v, flattened)
        else:
            key1 = '__'.join(keys)
            if not flattened.get(key1) is None:
                if isinstance(flattened[key1], list):
                    flattened[key1] = flattened[key1] + [value]
                else:
                    flattened[key1] = [flattened[key1]] + [value]
            else:
                flattened[key1] = value

    flattened = {}
    for key in json.keys():
        k = key
        # print("Key: " + k)
        process_value([key], json[key], flattened)
    return flattened

try:
    f = open("input.json", "r")
except:
    pass
y = json.loads(f.read())
flat = flatten_json(y)
text = json.dumps(flat)
df = pd.read_json(text)
df.to_csv('output.csv', index=False, encoding='utf-8')