我有一个Pandas数据框，上面有日期时间（TransactionDate）列、一个CustomerID列和一个Sales列。我希望对数据Daily重新采样，以分别对每个CustomerID的每日Sales求和。我尝试了两种不同的方法，但都没有生成所需的结果。当我尝试这样做时，通过仅将TransactionDate列设置为索引，销售额加总，但CustomerID列也加总，并且我丢失了有关哪个CustomerID产生多少销售额的信息。当我试图通过将TransactionDate列和CustomerID列都设置为索引来完成此操作时，我收到错误消息

TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'

如何才能获得按CustomerID列出的每日销售额的 Dataframe ？
包含完整数据的代码如下：

import pandas as pd
import numpy as np
import random

random.seed(30)
np.random.seed(30)

InvoiceNo = range(10000,10500)
print('len(InvoiceNo)',len(InvoiceNo))

start_date,end_date = '1/1/2015','12/31/2019'
date_rng = pd.date_range(start= start_date, periods=len(InvoiceNo), freq='3H')
length_of_field = date_rng.shape[0]
df = pd.DataFrame(date_rng, columns=['TransactionDate'])
df['InvoiceNo']=InvoiceNo

df['Quantity'] = np.random.randint(18,100,size=(len(date_rng)))

Items = ('ItemA','ItemB','ItemC','ItemD')
group_1 = np.random.choice(Items, len(InvoiceNo), p = [0.3, 0.5, 0.15, 0.05])
Price = (10.0,20,30,40)
dict_item_price = dict(zip(Items,Price))
PriceList = [dict_item_price[i] for i in group_1]

CustomerID = (18750,18751,18752,18753,18754,18756,18757)
group_2 = np.random.choice(CustomerID, len(InvoiceNo), p = [0.10, 0.25, 0.15, 0.05,0.35,0.05,0.05])

df['ItemCode'] = group_1
df['Price'] = PriceList
df['CustomerID'] = group_2
df['CustomerID'].astype(str)
df['Sales']=df['Price']*df['Quantity']

print('\ndf:')
print(df)
print(df.dtypes)

df1 = df[['CustomerID','Sales','TransactionDate']].copy().set_index(['TransactionDate'])
print('\n df1 :')
print(df1)

total_sales = df['Sales'].sum()

print('\ntotal sales :',total_sales)

daily_sales = df1.resample('D').sum()
print('\n daily_sales :')
print(daily_sales)