如何从json文件制作数据框表答案

【问题标题】：How to make dataframe table from json file如何从json文件制作数据框表
【发布时间】：2020-01-07 08:19:34
【问题描述】：

我有 json 文件，我想将其转换为 pandas 数据框，从“标签”中获取一些变量，并从“字段”中获取一些变量

{
    "tags": {
        "ID": "99909",
        "type": "fff",
        "ID2": "565789"
    },
    "timestamp": 1500079519064,
    "tenant": "dxy",
    "tstable": "data",
    "user": "writer",
    "fields": {
        "a": "0.003",
        "b": "0.011",

    }
}

需要的输出：

df_out=pd.DataFrame({'ID':[99909],'type':["fff"],'ID2':[565789],"timestamp": [1500079519064],"tenant": ["dxy"],"tstable": ["data"],"user": ["writer"],"a": ["0.003"],"b": ["0.011"]})

print(df_out)
ID type     ID2      timestamp tenant tstable    user      a      b
0  99909  fff  565789  1577078519064    dxy    data  writer  0.003  0.011

【问题讨论】：

此链接可能会有所帮助：stackoverflow.com/questions/35858611/…

标签： json python-3.x pandas

【解决方案1】：

使用json.json_normalize:

j = {
    "tags": {
        "ID": "99909",
        "type": "fff",
        "ID2": "565789"
    },
    "timestamp": 1500079519064,
    "tenant": "dxy",
    "tstable": "data",
    "user": "writer",
    "fields": {
        "a": "0.003",
        "b": "0.011",

    }
}

from pandas.io.json import json_normalize

df = json_normalize(j)
print (df)
       timestamp tenant tstable    user tags.ID tags.type tags.ID2 fields.a  \
0  1500079519064    dxy    data  writer   99909       fff   565789    0.003   

  fields.b  
0    0.011

如有必要，最后更改列名添加rename：

f = lambda x: x.split('.')[-1]
df = json_normalize(j).rename(columns=f)
print (df)
       timestamp tenant tstable    user     ID type     ID2      a      b
0  1500079519064    dxy    data  writer  99909  fff  565789  0.003  0.011

【讨论】：

【解决方案2】：

如果您有嵌套列，那么您首先需要对数据进行规范化：

import pandas as pd
from pandas.io.json import json_normalize
data = [
  {
    "tags": {
        "ID": "99909",
        "type": "fff",
        "ID2": "565789"
    },
    "timestamp": 1500079519064,
    "tenant": "dxy",
    "tstable": "data",
    "user": "writer",
    "fields": {
        "a": "0.003",
        "b": "0.011",

    }
}]

df = pd.DataFrame.from_dict(json_normalize(data), orient='columns')

print(df)

【讨论】：