首先，您需要使用json_normalize在数据框中读取您的 json 数据pandas

import pandas as pd

df = pd.json_normalize(['your json data'])

您的数据框看起来像这样

  label    id                                            signals
0   bad  0009                          [high_debt_ratio, no_job]
1  good  0002       [high_debt_ratio, great_credit, no_id_match]
2  good  0003                     [low_debt_ratio, great_credit]
3   bad  0001  [high_risk_loc, high_debt_ratio, no_job, no_id...

现在我们需要列的唯一值列表signals并以其可用性循环并基于需要插入特定列的值True或False在特定列的值中......signals在获得最终数据后也删除了列

for i in list(set(df.signals.sum())):
    df[i] = df.signals.apply(lambda x: i in x)
df.drop('signals',axis=1,inplace=True)
print(df)

-:output:-
  label    id  low_debt_ratio  great_credit  high_debt_ratio  no_id_match  high_risk_loc  no_job
0   bad  0009           False         False             True        False          False    True
1  good  0002           False          True             True         True          False   False
2  good  0003            True          True            False        False          False   False
3   bad  0001           False         False             True         True           True    True

你可以试试这个单行解决方案：

d = pd.json_normalize([{
    "label":"bad",
    "id": "0009",
    "signals":["high_debt_ratio", "no_job"] 
},

{
    "label":"good",
     "id": "0002",
    "signals":["high_debt_ratio", "great_credit", "no_id_match"] 
},

{
    "label":"good",
    "id": "0003",
    "signals":["low_debt_ratio", "great_credit"] 
},

{
    "label":"bad",
    "id": "0001",
    "signals":["high_risk_loc", "high_debt_ratio", "no_job", "no_id_match"] 
}])

d.merge(d.signals.apply(lambda x: "|".join(x)).str.get_dummies(sep = "|"), left_index = True, right_index= True)

输出：

使用 sklearn 的一些直观的东西MultiLabelBinarizer，虽然稍微有点冗长：

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()
mlb.fit(df['signals'])
new_col_names = mlb.classes_

# New DataFrame containing 0/1 values of the signals
signals_df = pd.DataFrame(mlb.transform(df['signals']), columns=new_col_names)

# Concatenate with original DataFrame
pd.concat( [df, signals_df], axis=1 )