我有一个JSON文件，我想转换为CSV文件。我如何用Python做到这一点?
我试着:
import json
import csv
f = open('data.json')
data = json.load(f)
f.close()
f = open('data.csv')
csv_file = csv.writer(f)
for item in data:
csv_file.writerow(item)
f.close()
然而，这并没有起作用。我正在使用Django和我收到的错误是:
`file' object has no attribute 'writerow'`
然后我尝试了以下方法:
import json
import csv
f = open('data.json')
data = json.load(f)
f.close()
f = open('data.csv')
csv_file = csv.writer(f)
for item in data:
f.writerow(item) # ← changed
f.close()
然后得到错误:
`sequence expected`
样本json文件:
[{
"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
}
}, {
"pk": 4,
"model": "auth.permission",
"fields": {
"codename": "add_group",
"name": "Can add group",
"content_type": 2
}
}, {
"pk": 10,
"model": "auth.permission",
"fields": {
"codename": "add_message",
"name": "Can add message",
"content_type": 4
}
}
]
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。
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
