使用pd.json_normalize：

>>> pd.json_normalize(test["data"], ['insights', 'data'])

    id person_id  date_start   date_stop impressions  spend
0  123       456  2022-12-31  2023-01-29        4070  36.14

一个选项是将pandas.json_normalize与pandas.Series.explode一起使用：

df = (
        pd.json_normalize(test["data"])
            ['insights.data']
                .explode()
                .pipe(lambda s: pd.DataFrame(s.tolist()))
      )

输出：

print(df)

    id person_id  date_start   date_stop impressions  spend
0  123       456  2022-12-31  2023-01-29        4070  36.14

试试看：

import pandas as pd

test = {
    "data": [
        {
            "name": "john",
            "insights": {
                "data": [
                    {
                        "id": "123",
                        "person_id": "456",
                        "date_start": "2022-12-31",
                        "date_stop": "2023-01-29",
                        "impressions": "4070",
                        "spend": "36.14",
                    }
                ],
                "paging": {"cursors": {"before": "MAZDZD", "after": "MAZDZD"}},
            },
            "id": "978",
        }
    ]
}

df = pd.DataFrame(
    [
        {
            "name": d["name"],
            "date_start": dd["date_start"],
            "date_stop": dd["date_stop"],
            "impressions": dd["impressions"],
        }
        for d in test["data"]
        for dd in d["insights"]["data"]
    ]
)
print(df)

图纸：

name  date_start   date_stop impressions
0  john  2022-12-31  2023-01-29        4070

下面是一个替代方法：

df = pd.DataFrame(test["data"][0]["insights"]["data"], 
        columns=["name", "date_start", "date_stop", "impressions", "spend"])
df["name"] = test["data"][0]["name"]
print(df)

name  date_start   date_stop impressions  spend
0  john  2022-12-31  2023-01-29        4070  36.14

严重混乱的 Dataframe

data['insights'][0]['data'][0]['impressions']
data['insights'][0]['data'][0]['spend']