将Pandas DataFrame转换为嵌套JSON而不更改数据结构 [英] Pandas DataFrame to Nested JSON Without Changing Data Structure

问题描述

我有pandas.DataFrame:

import pandas as pd
import json
df = pd.DataFrame([['2016-04-30T20:02:25.693Z', 'vmPowerOn', 'vmName'],['2016-04-07T22:35:41.145Z','vmPowerOff','hostName']], 
                  columns=['date', 'event', 'object'])

    date                        event       object
0   2016-04-30T20:02:25.693Z    vmPowerOn   vmName
1   2016-04-07T22:35:41.145Z    vmPowerOff  hostName

我想将该数据框转换为以下格式:

I want to convert that dataframe into the following format:

  {
    "name":"Alarm/Error",
    "data":[
      {"date": "2016-04-30T20:02:25.693Z", "details": {"event": "vmPowerOn", "object": "vmName"}},
      {"date": "2016-04-07T22:35:41.145Z", "details": {"event": "vmPowerOff", "object": "hostName"}}
    ]
  }

到目前为止，我已经尝试过:

So far, I've tried this:

df = df.to_dict(orient='records')
j = {"name":"Alarm/Error", "data":df}
json.dumps(j)

'{"name": "Alarm/Error", 
  "data": [{"date": "2016-04-30T20:02:25.693Z", "event": "vmPowerOn", "object": "vmName"}, 
           {"date": "2016-04-07T22:35:41.145Z", "event": "vmPowerOff", "object": "hostName"}
          ]
 }'

但是，这显然不会将详细信息列放在其自己的字典中.

However, this obviously does not put the detail columns in their own dictionary.

如何有效地将df date列和所有其他列拆分为JSON的单独部分?

How would I efficiently split the df date column and all other columns into separate parts of the JSON?

推荐答案

使用

测试代码:

df = pd.DataFrame([['2016-04-30T20:02:25.693Z', 'vmPowerOn', 'vmName'],
                   ['2016-04-07T22:35:41.145Z', 'vmPowerOff', 'hostName']],
                  columns=['date', 'event', 'object'])

print([{'date': x['date'],
        'details': {k: v for k, v in x.items() if k != 'date'}}
       for x in df.to_dict('records')])

结果:

[{'date': '2016-04-30T20:02:25.693Z', 'details': {'event': 'vmPowerOn', 'object': 'vmName'}}, 
 {'date': '2016-04-07T22:35:41.145Z', 'details': {'event': 'vmPowerOff', 'object': 'hostName'}}
]

这篇关于将Pandas DataFrame转换为嵌套JSON而不更改数据结构的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！