我有一个TimeSeries数据，我试图按月分组，然后是服务类型，所以基本上分组在多个列上。
我可以让每个小组独立工作（见下面的两个案例）。但是，当我尝试将它们组合在一起时失败并出现以下异常（下面）
第一个groupby就在'service'列上：
import xlwings as xw
bk = xw.Book(file_path)
sheet = bk.sheets['Sheet1'] # or whatever the sheet is named
def xl_col_sort(sheet,col_num):
sheet.range((2,col_num)).api.Sort(Key1=sheet.range((2,col_num)).api, Order1=1)
return
第二个groupby在TimeSeries Index上（频率= Month）：
df_service =df_mem[['service','amount']].groupby('service').agg(['sum','count'])
sum count
service
10-Class Pack - $170+HST 1728.90 9
646 Blue T-shirt (stars) 25.00 1
646 Foundations + 12 classes $210+HST 237.30 1
646 Foundations + 8 Classes - $159+HST 876.38 5
646 Klawkov Tee, Wonder Woman Tee, 2 Drop-Ins 96.05 1
Bronze (8/mth) 1830.60 12
Bronze (8/mth) $135+HST 1121.07 10
Clothing - Sweatpants XL, Grey Hoodie L 94.27 2
Drop-In $20+HST 158.20 7
Gold (Unlimited) - $185+HST 1604.56 19
Leather lifting straps 25.00 1
Men's Dimas Tee, Large 28.25 1
Open Gym 220.35 3
Open Gym - $65+HST 83.07 3
Red 646 Raglan, Large 33.90 1
Silver 2237.40 12
Silver (12/mth) $165+HST 1294.28 13
Test 2.00 2
Thumb Tape 6.25 1
Unlimited Gold 6898.65 33
Women's Fleece Pants 48.59 1
Wonder Woman muscle tank (2) +HST 56.50 1
当我将两者结合在一起时，它失败了：
df_montly_service = df_mem[['service','amount']].groupby(pd.Grouper(freq='M')).agg(['sum','count'])
amount
sum count
date
2017-03-31 2.00 2
2017-04-30 3053.68 38
2017-05-31 4854.03 34
2017-06-30 5650.85 39
2017-07-31 5227.17 29