嵌套Json到pandas DataFrame具有特定格式

时间：2022-04-08 00:17:25

i need to format the contents of a Json file in a certain format in a pandas DataFrame so that i can run pandassql to transform the data and run it through a scoring model.

我需要在pandas DataFrame中以特定格式格式化Json文件的内容，以便我可以运行pandassql来转换数据并通过评分模型运行它。

file = C:\scoring_model\json.js (contents of 'file' are below)

file = C：\ scoring_model \ json.js（'file'的内容如下）

{
"response":{
  "version":"1.1",
  "token":"dsfgf",
   "body":{
     "customer":{
         "customer_id":"1234567",
         "verified":"true"
       },
     "contact":{
         "email":"mr@abc.com",
         "mobile_number":"0123456789"
      },
     "personal":{
         "gender": "m",
         "title":"Dr.",
         "last_name":"Muster",
         "first_name":"Max",
         "family_status":"single",
         "dob":"1985-12-23",
     }
   }
 }

I need the dataframe to look like this (obviously all values on same row, tried to format it best as possible for this question):

我需要数据框看起来像这样（显然在同一行上的所有值，尝试尽可能地格式化这个问题）：

version | token | customer_id | verified | email      | mobile_number | gender |
1.1     | dsfgf | 1234567     | true     | mr@abc.com | 0123456789    | m      |

title | last_name | first_name |family_status | dob
Dr.   | Muster    | Max        | single       | 23.12.1985

I have looked at all the other questions on this topic, have tried various ways to load Json file into pandas

我已经查看了有关此主题的所有其他问题，尝试了各种方法将Json文件加载到pandas中

`with open(r'C:\scoring_model\json.js', 'r') as f:`
    c = pd.read_json(f.read())

 `with open(r'C:\scoring_model\json.js', 'r') as f:`
    c = f.readlines()

tried pd.Panel() in this solution Python Pandas: How to split a sorted dictionary in a column of a dataframe

在此解决方案中尝试了pd.Panel（）Python Pandas：如何在数据帧的列中拆分排序的字典

with dataframe results from [yo = f.readlines()] thought about trying to split contents of each cell based on ("") and find a way to put the split contents into different columns but no luck so far. Your expertise is greatly appreciated. Thank you in advance.

来自[yo = f.readlines（）]的数据帧结果考虑尝试基于（“”）拆分每个单元格的内容，并找到一种方法将拆分内容放入不同的列但到目前为止没有运气。非常感谢您的专业知识。先谢谢你。

1 个解决方案

#1

31  

If you load in the entire json as a dict (or list) e.g. using json.load, you can use json_normalize:

如果您将整个json作为dict（或列表）加载，例如使用json.load，你可以使用json_normalize：

In [11]: d = {"response": {"body": {"contact": {"email": "mr@abc.com", "mobile_number": "0123456789"}, "personal": {"last_name": "Muster", "gender": "m", "first_name": "Max", "dob": "1985-12-23", "family_status": "single", "title": "Dr."}, "customer": {"verified": "true", "customer_id": "1234567"}}, "token": "dsfgf", "version": "1.1"}}

In [12]: df = pd.io.json.json_normalize(d)

In [13]: df.columns = df.columns.map(lambda x: x.split(".")[-1])

In [14]: df
Out[14]:
        email mobile_number customer_id verified         dob family_status first_name gender last_name title  token version
0  mr@abc.com    0123456789     1234567     true  1985-12-23        single        Max      m    Muster   Dr.  dsfgf     1.1

#1

31  

If you load in the entire json as a dict (or list) e.g. using json.load, you can use json_normalize:

如果您将整个json作为dict（或列表）加载，例如使用json.load，你可以使用json_normalize：

In [11]: d = {"response": {"body": {"contact": {"email": "mr@abc.com", "mobile_number": "0123456789"}, "personal": {"last_name": "Muster", "gender": "m", "first_name": "Max", "dob": "1985-12-23", "family_status": "single", "title": "Dr."}, "customer": {"verified": "true", "customer_id": "1234567"}}, "token": "dsfgf", "version": "1.1"}}

In [12]: df = pd.io.json.json_normalize(d)

In [13]: df.columns = df.columns.map(lambda x: x.split(".")[-1])

In [14]: df
Out[14]:
        email mobile_number customer_id verified         dob family_status first_name gender last_name title  token version
0  mr@abc.com    0123456789     1234567     true  1985-12-23        single        Max      m    Muster   Dr.  dsfgf     1.1
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