使用特定的开始/结束日期以及 groupby 重新采样数据框

Question

我有一些看起来像这样的交易数据。

import pandas as pd
from io import StringIO
from datetime import datetime
from datetime import timedelta

data = """\
cust_id,datetime,txn_type,txn_amt
100,2019-03-05 6:30,Credit,25000
100,2019-03-06 7:42,Debit,4000
100,2019-03-07 8:54,Debit,1000
101,2019-03-05 5:32,Credit,25000
101,2019-03-06 7:13,Debit,5000
101,2019-03-06 8:54,Debit,2000
"""

df = pd.read_table(StringIO(data), sep=',')
df['datetime'] = pd.to_datetime(df['datetime'], format='%Y-%m-%d %H:%M:%S')
# use datetime as the dataframe index
df = df.set_index('datetime')
print(df)

                    cust_id txn_type  txn_amt
datetime                                      
2019-03-05 06:30:00      100   Credit    25000
2019-03-06 07:42:00      100    Debit     4000
2019-03-07 08:54:00      100    Debit     1000
2019-03-05 05:32:00      101   Credit    25000
2019-03-06 07:13:00      101    Debit     5000
2019-03-06 08:54:00      101    Debit     2000

我想对cust_id 和txn_type 的每个组合在每日级别聚合（求和）txn_amount 的数据重新采样。同时，我想将索引标准化为5天（目前数据只包含3天的数据）。本质上，这就是我想要制作的：

             cust_id txn_type  txn_amt
datetime    
2019-03-03    100    Credit   0
2019-03-03    100    Debit    0
2019-03-03    101    Credit   0
2019-03-03    101    Debit    0
2019-03-04    100    Credit   0
2019-03-04    100    Debit    0
2019-03-04    101    Credit   0
2019-03-04    101    Debit    0
2019-03-05    100    Credit   25000
2019-03-05    100    Debit    0
2019-03-05    101    Credit   25000
2019-03-05    101    Debit    0
2019-03-06    100    Credit   0
2019-03-06    100    Debit    4000
2019-03-06    101    Credit   0
2019-03-06    101    Debit    7000   => (note: aggregated value)
2019-03-07    100    Credit   0
2019-03-07    100    Debit    1000
2019-03-07    101    Credit   0
2019-03-07    101    Debit    0

到目前为止，我已经尝试创建一个新的日期时间索引并尝试重新采样，然后像这样使用新创建的索引：

# create a 5 day datetime index
end_dt = max(df.index).to_pydatetime().strftime('%Y-%m-%d')
start_dt = max(df.index) - timedelta(days=4)
start_dt = start_dt.to_pydatetime().strftime('%Y-%m-%d')
dt_index = pd.date_range(start=start_dt, end=end_dt, freq='1D', name='datetime')

但是，我不确定如何进行分组部分。不分组重采样输出错误结果：

# resample timeseries so that one row is 1 day's worth of txns
df2 = df.resample(rule='D').sum().reindex(dt_index).fillna(0)
print(df2)
            cust_id  txn_amt
datetime                    
2019-03-03      0.0      0.0
2019-03-04      0.0      0.0
2019-03-05    201.0  50000.0
2019-03-06    302.0  11000.0
2019-03-07    100.0   1000.0

那么，我如何在重采样时合并cust_id 和tsn_type 的分组？我见过this similar question，但是op的数据结构不同。

Answer 1

我这里用的是reindex，关键是要设置Multiple索引

df.index=pd.to_datetime(df.index).date
df=df.groupby([df.index,df['txn_type'],df['cust_id']]).agg({'txn_amt':'sum'}).reset_index(level=[1,2])
drange=pd.date_range(end=df.index.max(),periods =5)
idx=pd.MultiIndex.from_product([drange,df.cust_id.unique(),df.txn_type.unique()])
Newdf=df.set_index(['cust_id','txn_type'],append=True).reindex(idx,fill_value=0).reset_index(level=[1,2])
Newdf
Out[749]: 
            level_1 level_2  txn_amt
2019-03-03      100  Credit        0
2019-03-03      100   Debit        0
2019-03-03      101  Credit        0
2019-03-03      101   Debit        0
2019-03-04      100  Credit        0
2019-03-04      100   Debit        0
2019-03-04      101  Credit        0
2019-03-04      101   Debit        0
2019-03-05      100  Credit    25000
2019-03-05      100   Debit        0
2019-03-05      101  Credit    25000
2019-03-05      101   Debit        0
2019-03-06      100  Credit        0
2019-03-06      100   Debit     4000
2019-03-06      101  Credit        0
2019-03-06      101   Debit     7000
2019-03-07      100  Credit        0
2019-03-07      100   Debit     1000
2019-03-07      101  Credit        0
2019-03-07      101   Debit        0