本周与前一周最后一天的周差

Question

我有一个使用 pivot_table 方法从另一个 pandas 数据框（按商店销售）创建的数据透视表 Pandas 数据框（按地区销售）。

举个例子：

df = pd.DataFrame(
    {'store':['A','B','C','D','E']*7, 
     'region':['NW','NW','SW','NE','NE']*7, 
     'date':['2017-03-30']*5+['2017-04-05']*5+['2017-04-07']*5+['2017-04-12']*5+['2017-04-13']*5+['2017-04-17']*5+['2017-04-20']*5,
     'sales':[30,1,133,9,1,30,3,135,9,11,30,1,140,15,15,25,10,137,9,3,29,10,137,9,11,30,19,145,20,10,30,8,141,25,25]
     })
df_sales = df.pivot_table(index = ['region'], columns = ['date'], aggfunc = [np.sum], margins = True)
df_sales = df_sales.ix[:,range(0, df_sales.shape[1]-1)]

我的目标是对销售数据框执行以下操作。

添加一个名为“周差”的列，用于计算本周的总销售额与前一周的最新值（按日期）之间的差值。假设：我每周总是有几天的数据，但不是固定的日子。 随着新数据的到来，周差列会有所不同，但对于最新数据，如下所示：

>>> df_sales
              sum                                                         \
            sales                                                          
date   2017-03-30 2017-04-05 2017-04-07 2017-04-12 2017-04-13 2017-04-17   
region                                                                     
NE           10.0       20.0       30.0       12.0       20.0       30.0   
NW           31.0       33.0       31.0       35.0       39.0       49.0   
SW          133.0      135.0      140.0      137.0      137.0      145.0   
All         174.0      188.0      201.0      184.0      196.0      224.0   



date   2017-04-20 WeekDifference 
region             
NE           50.0    50.0-20.0
NW           38.0    38.0-39.0
SW          141.0    141.0-137.0
All         229.0    229-196.0

因为它是最晚日期和前一周最晚日期之间的差异。在这个具体的例子中，我们是在 2017-04-20 周，前一周数据的最后一天是 2017-04-13。

随着数据的更新，我想以一般方式执行此操作。

Answer 1

df = pd.DataFrame(
    {'store':['A','B','C','D','E']*7, 
     'region':['NW','NW','SW','NE','NE']*7, 
     'date':['2017-03-30']*5+['2017-04-05']*5+['2017-04-07']*5+['2017-04-12']*5+['2017-04-13']*5+['2017-04-17']*5+['2017-04-20']*5,
     'sales':[30,1,133,9,1,30,3,135,9,11,30,1,140,15,15,25,10,137,9,3,29,10,137,9,11,30,19,145,20,10,30,8,141,25,25]
     })
df_sales = df.pivot_table(index = ['region'], columns = ['date'], aggfunc = [np.sum], margins = True)
df_sales = df_sales.ix[:,range(0, df_sales.shape[1]-1)]

输入：

              sum                                                         \
            sales                                                          
date   2017-03-30 2017-04-05 2017-04-07 2017-04-12 2017-04-13 2017-04-17   
region                                                                     
NE           10.0       20.0       30.0       12.0       20.0       30.0   
NW           31.0       33.0       31.0       35.0       39.0       49.0   
SW          133.0      135.0      140.0      137.0      137.0      145.0   
All         174.0      188.0      201.0      184.0      196.0      224.0   



date   2017-04-20  weekdiffernce  
region                            
NE           50.0    50.0 - 20.0  
NW           38.0    38.0 - 39.0  
SW          141.0  141.0 - 137.0  
All         229.0  229.0 - 196.0  

计算上周和一周的偏移量：

last_column = pd.to_datetime(df_sales.iloc[:,-1].name[2])

last_week_column = last_column + pd.DateOffset(weeks=-1)

col_mask = (pd.to_datetime(df_sales.columns.get_level_values(2)).weekofyear == (last_column.weekofyear-1))    

df_sales.loc[:,('sum','sales','weekdiffernce')]=df_sales.iloc[:,-1].astype(str) + ' - '+df_sales.loc[ :,('sum','sales',last_week_column.strftime('%Y-%m-%d'))].astype(str)

df_sales.loc[:,('sum','sales','weekdiffernce')]=df_sales.iloc[:,-1].astype(str) + ' - '+df_sales.loc[:,('sum','sales',list(col_mask))].iloc[:,-1].astype(str)

print(df_sales)

输出：

              sum                                                         \
            sales                                                          
date   2017-03-30 2017-04-05 2017-04-07 2017-04-12 2017-04-13 2017-04-17   
region                                                                     
NE           10.0       20.0       30.0       12.0       20.0       30.0   
NW           31.0       33.0       31.0       35.0       39.0       49.0   
SW          133.0      135.0      140.0      137.0      137.0      145.0   
All         174.0      188.0      201.0      184.0      196.0      224.0   



date   2017-04-20  weekdiffernce  
region                            
NE           50.0    50.0 - 20.0  
NW           38.0    38.0 - 39.0  
SW          141.0  141.0 - 137.0  
All         229.0  229.0 - 196.0