这段代码应该适用于您，假设您的JSON数据位于名为data.json的文件中。

import json
import csv


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


with open("data.csv", "w") as file:
csv_file = csv.writer(file)
for item in data:
fields = list(item['fields'].values())
csv_file.writerow([item['pk'], item['model']] + fields)

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)。

因此，在一般情况下，您不能将任意的JSON结构转换为CSV。在一些特殊情况下，你可以(没有进一步嵌套的数组的数组;对象的数组，它们都有完全相同的键)。如果有的话，哪个特殊情况适用于您的问题?解决方案的细节取决于您所遇到的特殊情况。考虑到你甚至没有提到哪一个适用，我怀疑你可能没有考虑到约束，两个可用的情况实际上都不适用，你的问题是不可能解决的。但请务必澄清!

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

我在丹提出的解决方案上遇到了麻烦，但这对我有用:

import json
import csv


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


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


for item in data:
f.writerow([item['pk'], item['model']] + item['fields'].values())

“测试的地方。Json”包含以下内容:

[
{"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 } }
]

一个通用的解决方案，将任何平对象的json列表转换为csv。

传递输入。Json文件作为命令行的第一个参数。

import csv, json, sys


input = open(sys.argv[1])
data = json.load(input)
input.close()


output = csv.writer(sys.stdout)


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


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

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())

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。

正如在前面的回答中提到的，将json转换为csv的困难在于json文件可以包含嵌套字典，因此是多维数据结构，而csv是2D数据结构。但是，将多维结构转换为csv的一个好方法是使用多个主键连接在一起的csv。

在你的例子中，第一个csv输出的列是“pk”，“model”，“fields”。“pk”和“model”的值很容易获得，但因为“fields”列包含一个字典，它应该是它自己的csv，因为“codename”似乎是主键，你可以使用作为“fields”的输入来完成第一个csv。第二个csv包含来自“fields”列的字典，以codename作为主键，可用于将两个csv绑定在一起。

这是一个解决方案，为您的json文件转换嵌套字典2 csv。

import csv
import json


def readAndWrite(inputFileName, primaryKey=""):
input = open(inputFileName+".json")
data = json.load(input)
input.close()


header = set()


if primaryKey != "":
outputFileName = inputFileName+"-"+primaryKey
if inputFileName == "data":
for i in data:
for j in i["fields"].keys():
if j not in header:
header.add(j)
else:
outputFileName = inputFileName
for i in data:
for j in i.keys():
if j not in header:
header.add(j)


with open(outputFileName+".csv", 'wb') as output_file:
fieldnames = list(header)
writer = csv.DictWriter(output_file, fieldnames, delimiter=',', quotechar='"')
writer.writeheader()
for x in data:
row_value = {}
if primaryKey == "":
for y in x.keys():
yValue = x.get(y)
if type(yValue) == int or type(yValue) == bool or type(yValue) == float or type(yValue) == list:
row_value[y] = str(yValue).encode('utf8')
elif type(yValue) != dict:
row_value[y] = yValue.encode('utf8')
else:
if inputFileName == "data":
row_value[y] = yValue["codename"].encode('utf8')
readAndWrite(inputFileName, primaryKey="codename")
writer.writerow(row_value)
elif primaryKey == "codename":
for y in x["fields"].keys():
yValue = x["fields"].get(y)
if type(yValue) == int or type(yValue) == bool or type(yValue) == float or type(yValue) == list:
row_value[y] = str(yValue).encode('utf8')
elif type(yValue) != dict:
row_value[y] = yValue.encode('utf8')
writer.writerow(row_value)


readAndWrite("data")

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
}
}, "__")

是

{
"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))

我希望这能有所帮助!

这是python 3的

import json


o = json.loads('your json string') # Be careful, o must be a list, each of its objects will make a line of the csv.


def flatten(o, k='/'):
global l, c_line
if isinstance(o, dict):
for key, value in o.items():
flatten(value, k + '/' + key)
elif isinstance(o, list):
for ov in o:
flatten(ov, '')
elif isinstance(o, str):
o = o.replace('\r',' ').replace('\n',' ').replace(';', ',')
if not k in l:
l[k]={}
l[k][c_line]=o


def render_csv(l):
ftime = True


for i in range(100): #len(l[list(l.keys())[0]])
for k in l:
if ftime :
print('%s;' % k, end='')
continue
v = l[k]
try:
print('%s;' % v[i], end='')
except:
print(';', end='')
print()
ftime = False
i = 0


def json_to_csv(object_list):
global l, c_line
l = {}
c_line = 0
for ov in object_list : # Assumes json is a list of objects
flatten(ov)
c_line += 1
render_csv(l)


json_to_csv(o)

享受。

用pandas 图书馆， 这就像使用两个命令一样简单!

df = pd.read_json()

read_json将一个JSON字符串转换为一个pandas对象(一个序列或数据帧)。然后:

df.to_csv()

它既可以返回字符串，也可以直接写入csv文件。请参阅to_csv的文档。

根据之前的冗长回答，我们都应该感谢熊猫提供的这条捷径。

非结构化JSON参见这个答案。

import pandas as pd


with open('jsonfile.json', encoding='utf-8') as inputfile:
df = pd.read_json(inputfile)


df.to_csv('csvfile.csv', encoding='utf-8', index=False)

修改了Alec McGail的答案，以支持包含列表的JSON

    def flattenjson(self, mp, delim="|"):
ret = []
if isinstance(mp, dict):
for k in mp.keys():
csvs = self.flattenjson(mp[k], delim)
for csv in csvs:
ret.append(k + delim + csv)
elif isinstance(mp, list):
for k in mp:
csvs = self.flattenjson(k, delim)
for csv in csvs:
ret.append(csv)
else:
ret.append(mp)


return ret

谢谢!

csv.DictWriter()很容易使用，详细的实现可以像这样:

def read_json(filename):
return json.loads(open(filename).read())
def write_csv(data,filename):
with open(filename, 'w+') as outf:
writer = csv.DictWriter(outf, data[0].keys())
writer.writeheader()
for row in data:
writer.writerow(row)
# implement
write_csv(read_json('test.json'), 'output.csv')

注意，这假设所有JSON对象都具有相同的字段。

下面是参考，它可以帮助你。

解决这个问题的简单方法是:

创建一个新的Python文件，如:json_to_csv.py

添加以下代码:

import csv, json, sys
#if you are not using utf-8 files, remove the next line
sys.setdefaultencoding("UTF-8")
#check if you pass the input file and output file
if sys.argv[1] is not None and sys.argv[2] is not None:


fileInput = sys.argv[1]
fileOutput = sys.argv[2]


inputFile = open(fileInput)
outputFile = open(fileOutput, 'w')
data = json.load(inputFile)
inputFile.close()


output = csv.writer(outputFile)


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


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

添加代码后，保存文件并在终端上运行:

Python json_to_csv.py input.txt output.csv

我希望这对你有帮助。

韩国歌手组合!

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。

这里是链接

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()

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()

您只需将lineterminator='\n'参数添加到csv.writer。它将是:csv_w = csv.writer( out_file, lineterminator='\n' )

令人惊讶的是，我发现到目前为止贴在这里的答案都没有正确处理所有可能的场景(例如，嵌套字典，嵌套列表，无值等)。

这个解决方案应该适用于所有场景:

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 + [str(idx)], v, flattened)
else:
flattened['__'.join(keys)] = value


flattened = {}
for key in json.keys():
process_value([key], json[key], flattened)
return flattened

import os
import pandas as pd
import json
import numpy as np


data = []
os.chdir('D:\\Your_directory\\folder')
with open('file_name.json', encoding="utf8") as data_file:
for line in data_file:
data.append(json.loads(line))


dataframe = pd.DataFrame(data)
## Saving the dataframe to a csv file
dataframe.to_csv("filename.csv", encoding='utf-8',index= False)

import json,csv
t=''
t=(type('a'))
json_data = []
data = None
write_header = True
item_keys = []
try:
with open('kk.json') as json_file:
json_data = json_file.read()


data = json.loads(json_data)
except Exception as e:
print( e)


with open('bar.csv', 'at') as csv_file:
writer = csv.writer(csv_file)#, quoting=csv.QUOTE_MINIMAL)
for item in data:
item_values = []
for key in item:
if write_header:
item_keys.append(key)
value = item.get(key, '')
if (type(value)==t):
item_values.append(value.encode('utf-8'))
else:
item_values.append(value)
if write_header:
writer.writerow(item_keys)
write_header = False
writer.writerow(item_values)

试试这个

import csv, json, sys


input = open(sys.argv[1])
data = json.load(input)
input.close()


output = csv.writer(sys.stdout)


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


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

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()

亚历克的回答很好，但它不能在有多层嵌套的情况下工作。下面是一个支持多层嵌套的修改版本。如果嵌套对象已经指定了自己的键(例如Firebase Analytics / BigTable / BigQuery数据)，它也会使头名称更好一些:

"""Converts JSON with nested fields into a flattened CSV file.
"""


import sys
import json
import csv
import os


import jsonlines


from orderedset import OrderedSet


# from https://stackoverflow.com/a/28246154/473201
def flattenjson( b, prefix='', delim='/', val=None ):
if val is None:
val = {}


if isinstance( b, dict ):
for j in b.keys():
flattenjson(b[j], prefix + delim + j, delim, val)
elif isinstance( b, list ):
get = b
for j in range(len(get)):
key = str(j)


# If the nested data contains its own key, use that as the header instead.
if isinstance( get[j], dict ):
if 'key' in get[j]:
key = get[j]['key']


flattenjson(get[j], prefix + delim + key, delim, val)
else:
val[prefix] = b


return val


def main(argv):
if len(argv) < 2:
raise Error('Please specify a JSON file to parse')


print "Loading and Flattening..."
filename = argv[1]
allRows = []
fieldnames = OrderedSet()
with jsonlines.open(filename) as reader:
for obj in reader:
# print 'orig:\n'
# print obj
flattened = flattenjson(obj)
#print 'keys: %s' % flattened.keys()
# print 'flattened:\n'
# print flattened
fieldnames.update(flattened.keys())
allRows.append(flattened)


print "Exporting to CSV..."
outfilename = filename + '.csv'
count = 0
with open(outfilename, 'w') as file:
csvwriter = csv.DictWriter(file, fieldnames=fieldnames)
csvwriter.writeheader()
for obj in allRows:
# print 'allRows:\n'
# print obj
csvwriter.writerow(obj)
count += 1


print "Wrote %d rows" % count






if __name__ == '__main__':
main(sys.argv)

我可能迟到了，但我想，我已经处理过类似的问题。我有一个json文件，看起来像这样

我只想从这些json文件中提取一些键/值。因此，我编写了下面的代码来提取相同的内容。

    """json_to_csv.py
This script reads n numbers of json files present in a folder and then extract certain data from each file and write in a csv file.
The folder contains the python script i.e. json_to_csv.py, output.csv and another folder descriptions containing all the json files.
"""


import os
import json
import csv




def get_list_of_json_files():
"""Returns the list of filenames of all the Json files present in the folder
Parameter
---------
directory : str
'descriptions' in this case
Returns
-------
list_of_files: list
List of the filenames of all the json files
"""


list_of_files = os.listdir('descriptions')  # creates list of all the files in the folder


return list_of_files




def create_list_from_json(jsonfile):
"""Returns a list of the extracted items from json file in the same order we need it.
Parameter
_________
jsonfile : json
The json file containing the data
Returns
-------
one_sample_list : list
The list of the extracted items needed for the final csv
"""


with open(jsonfile) as f:
data = json.load(f)


data_list = []  # create an empty list


# append the items to the list in the same order.
data_list.append(data['_id'])
data_list.append(data['_modelType'])
data_list.append(data['creator']['_id'])
data_list.append(data['creator']['name'])
data_list.append(data['dataset']['_accessLevel'])
data_list.append(data['dataset']['_id'])
data_list.append(data['dataset']['description'])
data_list.append(data['dataset']['name'])
data_list.append(data['meta']['acquisition']['image_type'])
data_list.append(data['meta']['acquisition']['pixelsX'])
data_list.append(data['meta']['acquisition']['pixelsY'])
data_list.append(data['meta']['clinical']['age_approx'])
data_list.append(data['meta']['clinical']['benign_malignant'])
data_list.append(data['meta']['clinical']['diagnosis'])
data_list.append(data['meta']['clinical']['diagnosis_confirm_type'])
data_list.append(data['meta']['clinical']['melanocytic'])
data_list.append(data['meta']['clinical']['sex'])
data_list.append(data['meta']['unstructured']['diagnosis'])
# In few json files, the race was not there so using KeyError exception to add '' at the place
try:
data_list.append(data['meta']['unstructured']['race'])
except KeyError:
data_list.append("")  # will add an empty string in case race is not there.
data_list.append(data['name'])


return data_list




def write_csv():
"""Creates the desired csv file
Parameters
__________
list_of_files : file
The list created by get_list_of_json_files() method
result.csv : csv
The csv file containing the header only
Returns
_______
result.csv : csv
The desired csv file
"""


list_of_files = get_list_of_json_files()
for file in list_of_files:
row = create_list_from_json(f'descriptions/{file}')  # create the row to be added to csv for each file (json-file)
with open('output.csv', 'a') as c:
writer = csv.writer(c)
writer.writerow(row)
c.close()




if __name__ == '__main__':
write_csv()

我希望这能有所帮助。有关此代码如何工作的详细信息，您可以检查在这里

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(...)将数据保存到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:

<李>所以回答:
• 用python平化JSON数组
• 如何递归地平坦嵌套JSON，与平坦JSON
• 如何json_normalize一个带nan的列
• 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。

如果我们考虑下面的例子，将json格式的文件转换为csv格式的文件。

{
"item_data" : [
{
"item": "10023456",
"class": "100",
"subclass": "123"
}
]
}

下面的代码将转换json文件(data3. xml)。Json)转换为CSV文件(data3.csv)。

import json
import csv
with open("/Users/Desktop/json/data3.json") as file:
data = json.load(file)
file.close()
print(data)


fname = "/Users/Desktop/json/data3.csv"


with open(fname, "w", newline='') as file:
csv_file = csv.writer(file)
csv_file.writerow(['dept',
'class',
'subclass'])
for item in data["item_data"]:
csv_file.writerow([item.get('item_data').get('dept'),
item.get('item_data').get('class'),
item.get('item_data').get('subclass')])

上面提到的代码已经在本地安装的pycharm中执行，它已经成功地将json文件转换为csv文件。希望这有助于转换文件。

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数据来自马克斯·伯曼。

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')