Python 在每个唯一 ID 的列中查找特定值之前的索引列表

我有这样的数据帧:

id |    date   | status
________________________
...     ...      ...
1  |2020-01-01 | reserve
1  |2020-01-02 | sold
2  |2020-01-01 | free
3  |2020-01-03 | reserve
3  |2020-01-25 | signed
3  |2020-01-30 | sold
...     ...      ...
10 |2020-01-02 | signed
10 |2020-02-15 | sold 
...     ...      ....

我希望找到状态为sold的所有行的索引，然后在其他情况下，为这些行的前29天(状态为sold的行)分配1和0.

所需的数据帧如下

id |    date   | status  | label
_________________________________
...     ...      ...        ...
1  |2020-01-01 | reserve | 1 
1  |2020-01-02 | sold    | 1
2  |2019-12-02 | free    | 0    # no sold status for 2
3  |2020-01-03 | reserve | 1
3  |2020-01-25 | signed  | 1
3  |2020-01-30 | sold    | 1
...     ...      ...        ...
10 |2020-01-02 | signed  | 0
10 |2020-02-15 | sold    | 1    # more than 29 days from 2020-02-15
...     ...      ....       ...

我try 使用apply()，但我发现不能像那样调用函数

def make_labels(df):    
    
    def get_indices(df):
        return list(df[df['date'] >= df.iloc[-1]['date'] - timedelta(days=29)].index)

    df.sort_values(['id', 'date'], inplace=True)
    zero_labels = pd.Series(0, index = df.index, name='sold_labels')    
    one_lables = df.groupby('id')['status'].apply(lambda s: get_indices if s.iloc[-1] == 'sold').sum()
    zero_labels.loc[one_lables] = 1
    
    return zero_labels

df['label'] = make_labels(df)

输入的数据帧构造函数:

d = {'id': [1, 1, 2, 3, 3, 3, 10, 10], 
     'date': ['2020-01-01', '2020-01-02', '2020-01-01', '2020-01-03', '2020-01-25', '2020-01-30', '2020-01-02', '2020-02-15'],
     'status': ['reserve', 'sold', 'free', 'reserve', 'signed', 'sold', 'signed', 'sold']
    }
df = pd.DataFrame(data=d)