Python ，Pandas .如何根据观察次数分配分组编号

我想 for each 产品及其子类别分配一个唯一的ID.输入数据如下所示:

d = {'Manufacturer': ['Samsung','Samsung','Siemens','Siemens','Siemens','Apple','Apple'],
     'Product': ['Phone','Phone','Computer','Sensor','Sensor','Phone','MacBook']}
df = pd.DataFrame(data=d)


Manufacturer    Product
0   Samsung Phone
1   Samsung Phone
2   Siemens Computer
3   Siemens Sensor
4   Siemens Sensor
5   Apple   Phone
6   Apple   MacBook

我希望UNIQUE_ID将通知Manufacturer和Product，所以我想出了这样的解决方案:

df['ID_Manufacturer'] = df.groupby(['Manufacturer']).ngroup()
df['ID_Product'] = df.groupby(['Product']).ngroup()

columns = ['ID_Manufacturer', 'ID_Product']
df[columns] = df[columns].astype(str)
df['UNIQUE_ID'] = df[columns].apply(lambda x: '.'.join(x[x.notnull()]), axis = 1)
df.drop(['ID_Manufacturer', 'ID_Product'], axis = 1)

结果是:

    Manufacturer    Product   UNIQUE_ID
0   Samsung         Phone     1.2
1   Samsung         Phone     1.2
2   Siemens         Computer  2.0
3   Siemens         Sensor    2.3
4   Siemens         Sensor    2.3
5   Apple           Phone     0.2
6   Apple           MacBook   0.1

然而，我更想实现的是:

• ID号从1开始，而不是0
• 观察值最多的组分配的编号较低(当前按字母顺序排列)

因此，最终输出应如下所示:

    Manufacturer    Product   UNIQUE_ID
0   Samsung         Phone     3.1
1   Samsung         Phone     3.1
2   Siemens         Computer  1.2
3   Siemens         Sensor    1.3
4   Siemens         Sensor    1.3
5   Apple           Phone     2.4
6   Apple           MacBook   2.3