此代码适用于任何给定的json文件

# -*- coding: utf-8 -*-
"""
Created on Mon Jun 17 20:35:35 2019
author: Ram
"""

import json
import csv

with open("file1.json") as file:
    data = json.load(file)



# create the csv writer object
pt_data1 = open('pt_data1.csv', 'w')
csvwriter = csv.writer(pt_data1)

count = 0

for pt in data:

      if count == 0:

             header = pt.keys()

             csvwriter.writerow(header)

             count += 1

      csvwriter.writerow(pt.values())

pt_data1.close()

我假设您的JSON文件将解码为字典列表。首先，我们需要一个将JSON对象扁平化的函数:

def flattenjson(b, delim):
    val = {}
    for i in b.keys():
        if isinstance(b[i], dict):
            get = flattenjson(b[i], delim)
            for j in get.keys():
                val[i + delim + j] = get[j]
        else:
            val[i] = b[i]
            
    return val

在JSON对象上运行这段代码的结果:

flattenjson({
    "pk": 22, 
    "model": "auth.permission", 
    "fields": {
      "codename": "add_message", 
      "name": "Can add message", 
      "content_type": 8
    }
  }, "__")

is

{
    "pk": 22, 
    "model": "auth.permission", 
    "fields__codename": "add_message", 
    "fields__name": "Can add message", 
    "fields__content_type": 8
}

对JSON对象输入数组中的每个dict应用此函数后:

input = map(lambda x: flattenjson( x, "__" ), input)

并查找相关的列名:

columns = [x for row in input for x in row.keys()]
columns = list(set(columns))

在CSV模块中运行这个并不难:

with open(fname, 'wb') as out_file:
    csv_w = csv.writer(out_file)
    csv_w.writerow(columns)

    for i_r in input:
        csv_w.writerow(map(lambda x: i_r.get(x, ""), columns))

使用pandas中的json_normalize:

在名为test.json的文件中使用来自OP的示例数据。 这里使用了Encoding ='utf-8'，但在其他情况下可能不需要。 下面的代码利用了pathlib库。 .open是pathlib的一个方法。 也适用于非windows路径。 使用pandas.to_csv(…)将数据保存为csv文件。

import pandas as pd
# As of Pandas 1.01, json_normalize as pandas.io.json.json_normalize is deprecated and is now exposed in the top-level namespace.
# from pandas.io.json import json_normalize
from pathlib import Path
import json

# set path to file
p = Path(r'c:\some_path_to_file\test.json')

# read json
with p.open('r', encoding='utf-8') as f:
    data = json.loads(f.read())

# create dataframe
df = pd.json_normalize(data)

# dataframe view
 pk            model  fields.codename           fields.name  fields.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
  4  auth.permission        add_group         Can add group                    2
 10  auth.permission      add_message       Can add message                    4

# save to csv
df.to_csv('test.csv', index=False, encoding='utf-8')

CSV输出:

pk,model,fields.codename,fields.name,fields.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
4,auth.permission,add_group,Can add group,2
10,auth.permission,add_message,Can add message,4

嵌套更重的JSON对象的资源:

所以答案: 用python平化JSON数组 如何平嵌套的JSON递归，与平坦JSON 如何json_normalize一个列与nan 使用pandas将一列字典拆分为单独的列 有关其他相关问题，请参阅json_normalize标记。

这是@MikeRepass回答的修改。此版本将CSV写入文件，适用于Python 2和Python 3。

import csv,json
input_file="data.json"
output_file="data.csv"
with open(input_file) as f:
    content=json.load(f)
try:
    context=open(output_file,'w',newline='') # Python 3
except TypeError:
    context=open(output_file,'wb') # Python 2
with context as file:
    writer=csv.writer(file)
    writer.writerow(content[0].keys()) # header row
    for row in content:
        writer.writerow(row.values())

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()

此代码适用于任何给定的json文件

# -*- coding: utf-8 -*-
"""
Created on Mon Jun 17 20:35:35 2019
author: Ram
"""

import json
import csv

with open("file1.json") as file:
    data = json.load(file)



# create the csv writer object
pt_data1 = open('pt_data1.csv', 'w')
csvwriter = csv.writer(pt_data1)

count = 0

for pt in data:

      if count == 0:

             header = pt.keys()

             csvwriter.writerow(header)

             count += 1

      csvwriter.writerow(pt.values())

pt_data1.close()