如何将嵌套的dict转换为数据帧

Question

假设我有一个API响应，如下所示：

{
    "fact": {
        "UP": [{
            "SCODE": "CNB",
            "SNAME": "Kanpur Central"
        }, {
            "SCODE": "JHS",
            "SNAME": "Jhansi Junction"
        }],
        "MP": [{
            "SCODE": "BPL",
            "SNAME": "Bhopal Junction"
        }, {
            "SCODE": "JBP",
            "SNAME": "Jabalpur Junction"
        }]
    }
}

我必须将其转换为如下所示的数据帧（预期输出）：

fact    SCODE   SNAME
UP      CNB     Kanpur Central
UP      JHS     Jhansi Junction
MP      BPL     Bhopal Junction
MP      JBP     Jabalpur Junction

我的努力：我尝试使用json_normalize（），但没有达到预期的输出：

pd.json_normalize(response).apply(pd.Series.explode)

Answer 1

• 使用OP中的response
• 您必须创建另一个结构，因为json_normalize可用于词典列表，并且fact必须包含在其中：

new_response = [{"fact": rfact, **r} for rfact in response["fact"] for r in response["fact"][rfact]]

最后，您只需应用以下函数：

final_result = pd.json_normalize(new_response)

  fact SCODE              SNAME
0   UP   CNB     Kanpur Central
1   UP   JHS    Jhansi Junction
2   MP   BPL    Bhopal Junction
3   MP   JBP  Jabalpur Junction

Answer 2

不如直接在字典中工作（选择的答案做得很好）：

    data = {
    "fact": {
        "UP": [{
            "SCODE": "CNB",
            "SNAME": "Kanpur Central"
        }, {
            "SCODE": "JHS",
            "SNAME": "Jhansi Junction"
        }],
        "MP": [{
            "SCODE": "BPL",
            "SNAME": "Bhopal Junction"
        }, {
            "SCODE": "JBP",
            "SNAME": "Jabalpur Junction"
        }]
    }
}

keys = data['fact']

   (pd.concat([jn(data['fact'][key]) for key in keys], 
              keys = keys)
      .droplevel(-1)
      .rename_axis(index='fact')
      .reset_index()
     )



fact SCODE              SNAME
0   UP   CNB     Kanpur Central
1   UP   JHS    Jhansi Junction
2   MP   BPL    Bhopal Junction
3   MP   JBP  Jabalpur Junction

Answer 3

一个选项是使用python重塑形状：

df = pd.DataFrame([{'fact': k, **item}
                   for k, lst in response['fact'].items()
                   for item in lst])

  fact SCODE              SNAME
0   UP   CNB     Kanpur Central
1   UP   JHS    Jhansi Junction
2   MP   BPL    Bhopal Junction
3   MP   JBP  Jabalpur Junction

---

通过^{}+^{}^{}的pandas选项：

df = (
    pd.DataFrame(response)['fact']
        .explode()
        .apply(pd.Series)
        .rename_axis('fact')
        .reset_index()
)

  fact SCODE              SNAME
0   MP   BPL    Bhopal Junction
1   MP   JBP  Jabalpur Junction
2   UP   CNB     Kanpur Central
3   UP   JHS    Jhansi Junction