您需要重新整形，这里有一个选项，使用concat + stack/unstack：

tables = [Table_1, Table_2, Table_3]

out = (pd
   .concat([t.set_index(['PatientID', 'Week']) for t in tables], axis=1)
   .stack().unstack(level='PatientID').add_prefix('Patient ')
   .pipe(lambda d: d.set_axis('Week'+d.index.get_level_values('Week').astype(str)
                              +' '+d.index.get_level_values(1))
        )
   .rename_axis(index='Clinical Data', columns=None).reset_index()
)

字符串
或者使用functools.reduce + merge和melt + pivot：

from functools import reduce

tables = [Table_1, Table_2, Table_3]

out = (reduce(lambda a, b: a.merge(b, on=['PatientID', 'Week']), tables)
 .melt(['PatientID', 'Week'])
 .assign(**{'Clinical Data': lambda d: 'Week'+d.pop('Week').astype(str)
                                       +' '+d.pop('variable')})
 .pivot(index='Clinical Data', columns='PatientID', values='value')
 .rename_axis(columns=None).reset_index()
)

型
输出量：

Clinical Data  Patient 1  Patient 2
0  Week2 Medication        1.0        2.0
1     Week2 MG_dose       32.0       20.0
2      Week2 Test 1        0.0        1.0
3      Week2 Test 2        1.0        0.0
4   Week2 Survey Q1        1.0        0.0
5   Week2 Survey Q2        0.0        1.0

型