Python：响应 object 到 Dataframe

Question

我正在尝试将请求响应转换为 pandas dataframe。

到目前为止，我已经把它变成了一个字符串，只是浏览了一下谷歌。 我的第一语言在技术上是 python 但当我学习 python 时，任何 3.X 都是实验性的，所以已经有一段时间了，再加上 pandas 完全是另一种野兽。

无论如何，试图将其变成 dataframe，我应该查看任何想法或链接吗？：

下面是一个字符串，而不是字典。 我什至尝试

from ast import literal_eval
...
ast.literal_eval(data)

这也不起作用..嗯

{
  "instrument": "EUR_USD",
  "granularity": "S5",
  "candles": [
    {
      "complete": true,
      "volume": 7,
      "time": "2020-07-14T19:33:50.000000000Z",
      "mid": {
        "o": "1.13925",
        "h": "1.13926",
        "l": "1.13922",
        "c": "1.13922"
      }
    },
    {
      "complete": true,
      "volume": 1,
      "time": "2020-07-14T19:33:55.000000000Z",
      "mid": {
        "o": "1.13921",
        "h": "1.13921",
        "l": "1.13921",
        "c": "1.13921"
      }
    },
    {
      "complete": true,
      "volume": 1,
      "time": "2020-07-14T19:34:00.000000000Z",
      "mid": {
        "o": "1.13920",
        "h": "1.13920",
        "l": "1.13920",
        "c": "1.13920"
      }
    },
    {
      "complete": false,
      "volume": 10,
      "time": "2020-07-14T19:34:10.000000000Z",
      "mid": {
        "o": "1.13921",
        "h": "1.13921",
        "l": "1.13916",
        "c": "1.13920"
      }
    }
  ]
}

Answer 1

使用pandas模块和其中的DataFrame() function。

a = {
  "instrument": "EUR_USD",
  "granularity": "S5",
  "candles": [
    {
      "complete": True,
      "volume": 7,
      "time": "2020-07-14T19:33:50.000000000Z",
      "mid": {
        "o": "1.13925",
        "h": "1.13926",
        "l": "1.13922",
        "c": "1.13922"
      }
    },
    {
      "complete": True,
      "volume": 1,
      "time": "2020-07-14T19:33:55.000000000Z",
      "mid": {
        "o": "1.13921",
        "h": "1.13921",
        "l": "1.13921",
        "c": "1.13921"
      }
    },
    {
      "complete": True,
      "volume": 1,
      "time": "2020-07-14T19:34:00.000000000Z",
      "mid": {
        "o": "1.13920",
        "h": "1.13920",
        "l": "1.13920",
        "c": "1.13920"
      }
    },
    {
      "complete": False,
      "volume": 10,
      "time": "2020-07-14T19:34:10.000000000Z",
      "mid": {
        "o": "1.13921",
        "h": "1.13921",
        "l": "1.13916",
        "c": "1.13920"
      }
    }
  ]
}

import pandas as pd
df = pd.DataFrame(a)