编辑修改后的问题现在每行可能有多个诊断。在这种情况下，我们希望在此列的第一个explode()。此外，要求初始Visit ID。因此，我们可以简化，当没有转诊时，只有referral_id，即NaN，或当转诊为“GP to SP”时，只有初始就诊ID。

让我们从一个可重现的示例开始：

df = pd.DataFrame({
    'Visit ID': [1, 2, 3, 4, 5, 6, 7],
    'Visit Date': ['2021-01-01', '2021-02-01', '2021-02-01', '2021-04-01',
                   '2021-08-01', '2021-09-01', '2021-10-01'],
    'ID Number': [1001, 1002, 1003, 1001, 1004, 1005, 1002],
    'Diagnosis': ['Psoriasis', 'Back Pain, Joint Pain', 'Fever', 'Psoriasis',
                  'Joint Paint', 'Joint Paint', 'Back Pain'],
    'Panel': ['Panel GP', 'Panel GP', 'Panel GP', 'Panel SP',
              'Panel GP', 'Panel GP', 'Panel SP']})
df['Visit Date'] = pd.to_datetime(df['Visit Date'])

现在：

def referral_id(g):
    p = g['Panel']
    isref = (p == 'Panel GP').shift() & (p == 'Panel SP')
    return g['Visit ID'].shift().loc[isref]

z = df.assign(
    Diagnosis=df['Diagnosis'].str.split(r'\s*,\s*', regex=True)
).explode('Diagnosis').sort_values('Visit Date')

dfnew = df.assign(referral_id=z.groupby(['ID Number', 'Diagnosis'], as_index=False, group_keys=False).apply(referral_id))

>>> dfnew
   Visit ID Visit Date  ID Number Diagnosis              Panel      referral_id
0  1        2021-01-01  1001                   Psoriasis  Panel GP  NaN        
1  2        2021-02-01  1002       Back Pain, Joint Pain  Panel GP  NaN        
2  3        2021-02-01  1003                       Fever  Panel GP  NaN        
3  4        2021-04-01  1001                   Psoriasis  Panel SP  1.0        
4  5        2021-08-01  1004                 Joint Paint  Panel GP  NaN        
5  6        2021-09-01  1005                 Joint Paint  Panel GP  NaN        
6  7        2021-10-01  1002                   Back Pain  Panel SP  2.0