嵌套JSON到CSV-通用方法

Question

我是Python的新手，正在努力将嵌套json文件转换为cvs。为此，我首先加载json，然后以json_normalize打印出不错的输出的方式对其进行了转换，然后使用pandas包将标准化的部分输出到中cvs。

我的示例json：

[{
 "_id": {
   "id": "123"
 },
 "device": {
   "browser": "Safari",
   "category": "d",
   "os": "Mac"
 },
 "exID": {
   "$oid": "123"
 },
 "extreme": false,
 "geo": {
   "city": "London",
   "country": "United Kingdom",
   "countryCode": "UK",
   "ip": "00.000.000.0"
 },
 "viewed": {
   "$date": "2011-02-12"
 },
 "attributes": [{
   "name": "gender",
   "numeric": 0,
   "value": 0
 }, {
   "name": "email",
   "value": false
 }],
 "change": [{
   "id": {
     "$id": "1231"
   },
   "seen": [{
     "$date": "2011-02-12"
   }]
 }]
}, {
 "_id": {
   "id": "456"
 },
 "device": {
   "browser": "Chrome 47",
   "category": "d",
   "os": "Windows"
 },
 "exID": {
   "$oid": "345"
 },
 "extreme": false,
 "geo": {
   "city": "Berlin",
   "country": "Germany",
   "countryCode": "DE",
   "ip": "00.000.000.0"
 },
 "viewed": {
   "$date": "2011-05-12"
 },
 "attributes": [{
   "name": "gender",
   "numeric": 1,
   "value": 1
 }, {
   "name": "email",
   "value": true
 }],
 "change": [{
   "id": {
     "$id": "1231"
   },
   "seen": [{
     "$date": "2011-02-12"
   }]
 }]
}]

使用以下代码（此处不包括嵌套部分）：

import json
from pandas.io.json import json_normalize


def loading_file():
    #File path
    file_path = #file path here

    #Loading json file
    json_data = open(file_path)
    data = json.load(json_data)
    return data

#Storing avaliable keys
def data_keys(data):
    keys = {}
    for i in data:
        for k in i.keys():
            keys[k] = 1

    keys = keys.keys()

#Excluding nested arrays from keys - hard coded -> IMPROVE
    new_keys = [x for x in keys if
    x != 'attributes' and
    x != 'change']

    return new_keys

#Excluding nested arrays from json dictionary
def new_data(data, keys):
    new_data = []
    for i in range(0, len(data)):
        x = {k:v for (k,v) in data[i].items() if k in keys }
        new_data.append(x)
    return new_data

 def csv_out(data):
     data.to_csv('out.csv',encoding='utf-8')

def main():
     data_file = loading_file()
     keys = data_keys(data_file)
     table = new_data(data_file, keys)
     csv_out(json_normalize(table))

main()

我当前的输出看起来像这样：

| _id.id | device.browser | device.category | device.os |  ... | viewed.$date |
|--------|----------------|-----------------|-----------|------|--------------|
| 123    | Safari         | d               | Mac       | ...  | 2011-02-12   |
| 456    | Chrome 47      | d               | Windows   | ...  | 2011-05-12   |
|        |                |                 |           |      |              |

我的问题是我想将嵌套数组包括到cvs中，所以我必须将它们展平。我无法弄清楚如何使其通用，因此在创建表时不使用字典keys（numeric, id, name）values。我必须使它普遍意义，因为按键的数量attributes和change。因此，我想要这样的输出：

| _id.id | device.browser | ... | attributes_gender_numeric | attributes_gender_value | attributes_email_value | change_id | change_seen |
|--------|----------------|-----|---------------------------|-------------------------|------------------------|-----------|-------------|
| 123    | Safari         | ... | 0                         | 0                       | false                  | 1231      | 2011-02-12  |
| 456    | Chrome 47      | ... | 1                         | 1                       | true                   | 1231      | 2011-02-12  |
|        |                |     |                           |                         |                        |           |             |

先感谢您！非常欢迎提供任何有关改进我的代码并使之更高效的提示。

Answer 1

感谢Amir Ziai撰写的精彩博客文章，您可以在这里找到我以平板形式输出数据的方法。具有以下功能：

#Function that recursively extracts values out of the object into a flattened dictionary
def flatten_json(data):
    flat = [] #list of flat dictionaries
    def flatten(y):
        out = {}

        def flatten2(x, name=''):
            if type(x) is dict:
                for a in x:
                    if a == "name": 
                            flatten2(x["value"], name + x[a] + '_')
                    else:  
                        flatten2(x[a], name + a + '_')
            elif type(x) is list:
                for a in x:
                    flatten2(a, name + '_')
            else:
                out[name[:-1]] = x

        flatten2(y)
        return out

#Loop needed to flatten multiple objects
    for i in range(len(data)):
        flat.append(flatten(data[i]).copy())

    return json_normalize(flat) 

我知道这样一个事实，即由于name-value if语句的缘故，它不是完全可推广的。但是，如果删除了创建名称-值字典的豁免，则该代码可以与其他嵌入式数组一起使用。