python - 是否可以旋转数据框并获得每月聚合？

Question

我有一个客户购买的数据框:

   customer      shop  amount local_date
0      John  WALLMART    1.50 2019-04-10
1      John  WALLMART   40.79 2019-05-10
2      John      LIDL    2.64 2019-08-18
3      John  WALLMART   29.17 2019-02-18
4      John      LIDL   42.69 2019-07-22
5      John  WALLMART    1.50 2019-09-16
6      John  WALLMART   40.79 2019-09-17
7      Mary  WALLMART    2.64 2019-05-08
8      Mary      LIDL   29.17 2019-02-07
9      Mary  WALLMART   28.23 2019-02-21
10     Mary      ALDI    8.84 2019-10-15
11     Mary  WALLMART    5.59 2019-03-23
12     Mary      LIDL   53.09 2019-01-03
13     Mary      LIDL   46.03 2019-02-03
14     Mary  WALLMART   84.17 2019-10-18
15     Paul      LIDL    4.63 2019-02-21
16     Paul  WALLMART   19.82 2019-02-13
17     Paul      ALDI   19.02 2019-12-12
18     Paul      LIDL   41.88 2019-06-25
19     Paul      ALDI   37.79 2019-12-18

我可以对其进行透视并获取每个商店每个客户的总金额:

df.pivot_table(values='amount', index=['customer'], columns=['shop'], aggfunc='sum').reset_index().fillna(0)

shop customer   ALDI    LIDL  WALLMART
0        John   0.00   45.33    113.75
1        Mary   8.84  128.29    120.63
2        Paul  56.81   46.51     19.82

我怎样才能知道他们每月在每家商店的消费金额？

我尝试了一些东西，我计划将其转换为我所需的格式

# this makes no sense to me
df.set_index('local_date').groupby([pd.Grouper(freq='M'),'customer','shop'])['amount'].sum()

local_date  customer  shop    
2019-01-31  Mary      LIDL        53.09
2019-02-28  John      WALLMART    29.17
            Mary      LIDL        75.20
                      WALLMART    28.23
            Paul      LIDL         4.63
                      WALLMART    19.82
2019-03-31  Mary      WALLMART     5.59
2019-04-30  John      WALLMART     1.50
2019-05-31  John      WALLMART    40.79
            Mary      WALLMART     2.64
2019-06-30  Paul      LIDL        41.88
2019-07-31  John      LIDL        42.69
2019-08-31  John      LIDL         2.64
2019-09-30  John      WALLMART    42.29
2019-10-31  Mary      ALDI         8.84
                      WALLMART    84.17
2019-12-31  Paul      ALDI        56.81

我还通过按 dt.month 分组创建了一个数据框，然后对其进行旋转，但最终得到了与开始时相同的数据透视表:

# create dataframe grouped by monthly sum
newd = df.groupby([df.local_date.dt.month,'customer','shop'])['amount'].sum().to_frame()

#pivoting
newd.pivot_table(values='amount', index=['customer'], columns=['shop'], aggfunc='sum').reset_index().fillna(0)


shop customer   ALDI    LIDL  WALLMART
0        John   0.00   45.33    113.75
1        Mary   8.84  128.29    120.63
2        Paul  56.81   46.51     19.82

Answer 1

groupby 月份，to_period:

df.groupby([df['local_date'].dt.to_period('M'),'customer','shop'])['amount'].sum()

输出:

local_date  customer  shop    
2019-01     Mary      LIDL        53.09
2019-02     John      WALLMART    29.17
            Mary      LIDL        75.20
                      WALLMART    28.23
            Paul      LIDL         4.63
                      WALLMART    19.82
2019-03     Mary      WALLMART     5.59
2019-04     John      WALLMART     1.50
2019-05     John      WALLMART    40.79
            Mary      WALLMART     2.64
2019-06     Paul      LIDL        41.88
2019-07     John      LIDL        42.69
2019-08     John      LIDL         2.64
2019-09     John      WALLMART    42.29
2019-10     Mary      ALDI         8.84
                      WALLMART    84.17
2019-12     Paul      ALDI        56.81
Name: amount, dtype: float64

如果您希望将shop作为列，您可以unstack:

(df.groupby([df['local_date'].dt.to_period('M'),'customer','shop'])['amount']
     .sum().unstack('shop', fill_value=0)
)

输出:

shop                  ALDI   LIDL  WALLMART
local_date customer                        
2019-01    Mary       0.00  53.09      0.00
2019-02    John       0.00   0.00     29.17
           Mary       0.00  75.20     28.23
           Paul       0.00   4.63     19.82
2019-03    Mary       0.00   0.00      5.59
2019-04    John       0.00   0.00      1.50
2019-05    John       0.00   0.00     40.79
           Mary       0.00   0.00      2.64
2019-06    Paul       0.00  41.88      0.00
2019-07    John       0.00  42.69      0.00
2019-08    John       0.00   2.64      0.00
2019-09    John       0.00   0.00     42.29
2019-10    Mary       8.84   0.00     84.17
2019-12    Paul      56.81   0.00      0.00