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
        }
    }
]

当前回答

您可以使用此代码将json文件转换为csv文件 读取文件后,我将对象转换为熊猫数据框架,然后将其保存为CSV文件

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)

其他回答

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')

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()

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库,这就像使用两个命令一样简单!

df = pd.read_json()

read_json将JSON字符串转换为pandas对象(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。

关于非结构化JSON,请参阅这个答案。

编辑: 有人问我一个最小的例子:

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)

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!