python - 如何将深度嵌套的 JSON 文件转换为 CSV？

Question

我对编码非常陌生，我想将深层嵌套的 JSON 文件转换为 CSV。我知道这可以通过 pandas 模块轻松实现。

我尝试使用 pandas 包并将标准化部分输出到 cvs 中。

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"2019-02-07T14:00:00.000Z", "v_alc": 0.3852}, {"t": "2019-02-07T15:00:00.000Z", "v_alc": 0.3438297872}, {"t": "2019-02-07T16:00:00.000Z", "v_alc": 0.4885714286}, {"t": "2019-02-07T17:00:00.000Z", "v_alc": 0.5139583333}, {"t": "2019-02-07T18:00:00.000Z", "v_alc": 0.4481818182}, {"t": "2019-02-07T19:00:00.000Z", "v_alc": 0.3231111111}, {"t": "2019-02-07T20:00:00.000Z", "v_alc": 0.305}, {"t": "2019-02-07T21:00:00.000Z", "v_alc": 0.3018367347}, {"t": "2019-02-07T22:00:00.000Z", "v_alc": 0.3054}, {"t": "2019-02-07T23:00:00.000Z", "v_alc": 0.326}, {"t": "2019-02-08T00:00:00.000Z", "v_alc": 0.3595833333}, {"t": "2019-02-08T01:00:00.000Z", "v_alc": 0.4104255319}, {"t": "2019-02-08T02:00:00.000Z", "v_alc": 0.3588}, {"t": "2019-02-08T03:00:00.000Z", "v_alc": 0.3382}, {"t": "2019-02-08T04:00:00.000Z", "v_alc": 0.305625}, {"t": "2019-02-08T05:00:00.000Z", "v_alc": 0.34325}, {"t": "2019-02-08T06:00:00.000Z", "v_alc": 0.3891666667}, {"t": "2019-02-08T07:00:00.000Z", "v_alc": 0.3381081081}, {"t": "2019-02-08T08:00:00.000Z", "v_alc": 0.5335897436}, {"t": "2019-02-08T09:00:00.000Z", "v_alc": 0.4008163265}, {"t": "2019-02-08T10:00:00.000Z", "v_alc": 0.3123404255}, {"t": "2019-02-08T11:00:00.000Z", "v_alc": 0.3280851064}, {"t": "2019-02-08T12:00:00.000Z", "v_alc": 0.2973333333}, {"t": "2019-02-08T13:00:00.000Z", "v_alc": 0.2947916667}, {"t": "2019-02-08T14:00:00.000Z", "v_alc": 0.3066}, {"t": "2019-02-08T15:00:00.000Z", "v_alc": 0.3938636364}, {"t": "2019-02-08T16:00:00.000Z", "v_alc": 0.5452380952}, {"t": "2019-02-08T17:00:00.000Z", "v_alc": 0.3494871795}, {"t": "2019-02-08T18:00:00.000Z", "v_alc": 0.325106383}, {"t": "2019-02-08T19:00:00.000Z", "v_alc": 0.3283333333}, {"t": "2019-02-08T20:00:00.000Z", "v_alc": 0.31375}, {"t": "2019-02-08T21:00:00.000Z", "v_alc": 0.3391836735}, {"t": "2019-02-08T22:00:00.000Z", "v_alc": 0.3617391304}, {"t": "2019-02-08T23:00:00.000Z", "v_alc": 0.3286}, {"t": "2019-02-09T00:00:00.000Z", "v_alc": 0.3425}, {"t": "2019-02-09T01:00:00.000Z", "v_alc": 0.3659090909}, {"t": "2019-02-09T02:00:00.000Z", "v_alc": 0.3580769231}, {"t": "2019-02-09T03:00:00.000Z", "v_alc": 0.3397826087}, {"t": "2019-02-09T04:00:00.000Z", "v_alc": 0.3319512195}, {"t": "2019-02-09T05:00:00.000Z", "v_alc": 0.3107843137}, {"t": "2019-02-09T06:00:00.000Z", "v_alc": 0.3089361702}, {"t": "2019-02-09T07:00:00.000Z", "v_alc": 0.3552173913}, {"t": "2019-02-09T08:00:00.000Z", "v_alc": 0.5072}, {"t": "2019-02-09T09:00:00.000Z", "v_alc": 0.4972}, {"t": "2019-02-09T10:00:00.000Z", "v_alc": 0.64175}, {"t": "2019-02-09T11:00:00.000Z", "v_alc": 0.4969565217}, {"t": "2019-02-09T12:00:00.000Z", "v_alc": 0.3991666667}, {"t": "2019-02-09T13:00:00.000Z", "v_alc": 0.4159183673}, {"t": "2019-02-09T14:00:00.000Z", "v_alc": 0.4604255319}, {"t": "2019-02-09T15:00:00.000Z", "v_alc": 0.6008333333}, {"t": "2019-02-09T16:00:00.000Z", "v_alc": 0.5222727273}, {"t": "2019-02-09T17:00:00.000Z", "v_alc": 0.3736956522}, {"t": "2019-02-09T18:00:00.000Z", "v_alc": 0.3779591837}, {"t": "2019-02-09T19:00:00.000Z", "v_alc": 0.3551020408}, {"t": "2019-02-09T20:00:00.000Z", "v_alc": 0.3625}, {"t": "2019-02-09T21:00:00.000Z", "v_alc": 0.3319047619}, {"t": "2019-02-09T22:00:00.000Z", "v_alc": 0.3476470588}, {"t": "2019-02-09T23:00:00.000Z", "v_alc": 0.3747916667}, {"t": "2019-02-10T00:00:00.000Z", "v_alc": 0.3697916667}, {"t": "2019-02-10T01:00:00.000Z", "v_alc": 0.3229545455}, {"t": "2019-02-10T02:00:00.000Z", "v_alc": 0.3221276596}, {"t": "2019-02-10T03:00:00.000Z", "v_alc": 0.3158}, {"t": "2019-02-10T04:00:00.000Z", "v_alc": 0.32125}, {"t": "2019-02-10T05:00:00.000Z", "v_alc": 0.3241860465}, {"t": "2019-02-10T06:00:00.000Z", "v_alc": 0.303125}, {"t": "2019-02-10T07:00:00.000Z", "v_alc": 0.3140425532}, {"t": "2019-02-10T08:00:00.000Z", "v_alc": 0.5367346939}, {"t": "2019-02-10T09:00:00.000Z", "v_alc": 0.3625581395}, {"t": "2019-02-10T10:00:00.000Z", "v_alc": 0.4341176471}, {"t": "2019-02-10T11:00:00.000Z", "v_alc": 0.411627907}, {"t": "2019-02-10T12:00:00.000Z", "v_alc": 0.466875}, {"t": "2019-02-10T13:00:00.000Z", "v_alc": 0.4318181818}, {"t": "2019-02-10T14:00:00.000Z", "v_alc": 0.3642222222}, {"t": "2019-02-10T15:00:00.000Z", "v_alc": 0.3268085106}, {"t": "2019-02-10T16:00:00.000Z", "v_alc": 0.393877551}, {"t": "2019-02-10T17:00:00.000Z", "v_alc": 0.4158695652}, {"t": "2019-02-10T18:00:00.000Z", "v_alc": 0.5480851064}, {"t": "2019-02-10T19:00:00.000Z", "v_alc": 0.5466}, {"t": "2019-02-10T20:00:00.000Z", "v_alc": 0.4887234043}, {"t": "2019-02-10T21:00:00.000Z", "v_alc": 0.4388461538}, {"t": "2019-02-10T22:00:00.000Z", "v_alc": 0.4172}, {"t": "2019-02-10T23:00:00.000Z", "v_alc": 0.3665306122}, {"t": "2019-02-11T00:00:00.000Z", "v_alc": 0.3511111111}, {"t": "2019-02-11T01:00:00.000Z", "v_alc": 0.3243589744}, {"t": "2019-02-11T02:00:00.000Z", "v_alc": null}

这是我尝试过的。我不确定如何深入了解嵌套标题。

import pandas as pd
from pandas.io.json import json_normalize
import json

with open('AQ_T1_Feb19.json') as file:
    data = json.load(file)

df = json_normalize(data, ['status'],['data'])

df.to_csv('AQ_T1_Feb19.csv', encoding='utf-8', index=False)

csv 文件应将 t 和 v_amm 作为标题，并在列中包含相应的值。我无法上传图片，但希望您明白我的意思。

Answer 1

使用json_normalize:

• 在最初的尝试中设置了不正确的record_path
• 以下是正确的路径，没有额外的键

{'data': {'metrics': {'mid1': {'did1': {'data': [{'t': <class 'str'>,
                                                  'v_amm': <class 'float'>}]},
                               'did2': {'data': [{'t': <class 'str'>,
                                                  'v_alc': <class 'float'>}]}}}}}

• 此外，JSON 中有两个 null 值，必须替换为 None
  • How can JSON data with null value be converted to a dictionary

from pandas.io.json import json_normalize
import pandas as pd

data = {your json}
df_v_alc = json_normalize(data, ['data', 'metrics', 'mid1', 'did2', 'data'])

                         t     v_alc
 2019-02-07T08:00:00.000Z  0.344000
 2019-02-07T09:00:00.000Z  0.391778
 2019-02-07T10:00:00.000Z  0.325208
 2019-02-07T11:00:00.000Z  0.293800
 2019-02-07T12:00:00.000Z  0.309302

df_v_amm = json_normalize(data, ['data', 'metrics', 'mid1', 'did1', 'data'])

                        t     v_amm
 2019-02-07T08:00:00.000Z  0.320000
 2019-02-07T09:00:00.000Z  0.322889
 2019-02-07T10:00:00.000Z  0.209375
 2019-02-07T11:00:00.000Z  0.167200
 2019-02-07T12:00:00.000Z  0.196279