将字典嵌套到 Pandas df 连接行

如何解决将字典嵌套到 Pandas df 连接行

给定以下字典：

j = {
  "source": "https://example.com","timestamp": "2021-04-12T19:34:24Z","durationInTicks": 1082400000,"duration": "PT1M48.24S","combinedRecognizedPhrases": [
    {
      "channel": 0,"lexical": "aaa","itn": "aaa","maskedITN": "aaa","display": "aaa"
    }
  ],"recognizedPhrases": [
    {
      "recognitionStatus": "Success","channel": 0,"speaker": 1,"offset": "PT2.18S","duration": "PT3.88S","offsetInTicks": 21800000,"durationInTicks": 38800000,"nBest": [
        {
          "confidence": 0.9306252,"lexical": "gracias por llamar","itn": "gracias por llamar","maskedITN": "gracias por llamar","display": "¿Gracias por llamar","words": [
            {
              "word": "gracias","duration": "PT0.37S","durationInTicks": 3700000,"confidence": 0.930625
            },{
              "word": "por","offset": "PT2.55S","duration": "PT0.18S","offsetInTicks": 25500000,"durationInTicks": 1800000,{
              "word": "llamar","offset": "PT2.73S","duration": "PT0.22S","offsetInTicks": 27300000,"durationInTicks": 2200000,"confidence": 0.930625
            }
          ]
        }
      ]
    },{
      "recognitionStatus": "Success","speaker": 2,"offset": "PT6.85S","duration": "PT5.63S","offsetInTicks": 68500000,"durationInTicks": 56300000,"nBest": [
        {
          "confidence": 0.9306253,"lexical": "quiero hacer un pago","itn": "quiero hacer un pago","maskedITN": "quiero hacer un pago","display": "quiero hacer un pago"
        }
      ]
    },"offset": "PT13.29S","duration": "PT3.81S","offsetInTicks": 132900000,"durationInTicks": 38100000,"nBest": [
        {
          "confidence": 0.93062526,"lexical": "no sé bien la cantidad","itn": "no sé bien la cantidad","maskedITN": "no sé bien la cantidad","display": "no sé bien la cantidad"
        }
      ]
    }
  ]
}

目标：在df的单行中获取感兴趣的信息。

到目前为止我做了什么？：

df = pd.json_normalize(j,record_path=['recognizedPhrases','nBest'],Meta=['source','durationInTicks','duration',['recognizedPhrases','speaker']])
df['speech'] = df.groupby(['source','recognizedPhrases.speaker'])['display'].transform(lambda x : ' '.join(x))
df = df.drop_duplicates(subset=['recognizedPhrases.speaker'])

获得的df：

为什么我对获得的输出不满意？：我的输出显示了一个包含两行的 df（每个 recognizedPhrases.speaker 一行），我需要将所有信息合二为一行，一列是说话者 1 所说的话（在 speaker 列中），另一列是 speaker 2 所说的话。

附加信息：性能是一个重要因素，因为我将处理数千个文件。

编辑 1： 我期望的结果看起来像这样：

expected_dict = {'source': {0: 'https://example.com'},'durationInTicks': {0: 1082400000},'duration': {0: 'PT1M48.24S'},'recognizedPhrases.speaker1': {0: '¿Gracias por llamar'},'recognizedPhrases.speaker2': {0: 'quiero hacer un pago no sé bien la cantidad'}}
expected_df = pd.DataFrame(expected_dict)

解决方法

您可以pivot()进入预期的输出：

index = ['source','durationInTicks','duration']
columns = ['recognizedPhrases.speaker']
values= ['speech']

df = df[index+columns+values].pivot(index=index,columns=columns,values=values[0])
df.columns = [f'{df.columns.name}{column}' for column in df.columns]

来源	durationInTicks	持续时间	recognizedPhrases.speaker1	recognizedPhrases.speaker2
https://example.com	1082400000	PT1M48.24S	¿Gracias por llamar	quiero hacer un pago no sé bien la cantidad