Question

我有两个数据帧 train_family_sales

    family  store_nbr   date    unit_sales
0   GROCERY I   1.0 2016-08-01  3.0
1   GROCERY I   1.0 2016-08-02  10.0
2   GROCERY I   1.0 2016-08-04  3.0
3   AUTOMOTIVE  1.0 2016-08-05  5.0
4   AUTOMOTIVE  1.0 2016-08-06  5.0

和train_sales

    date       store_nbr item_nbr unit_sales    family  
0   2016-08-01 1.0       103520   3.0         GROCERY I     
1   2016-08-02 1.0       103520   1.0         GROCERY I     
2   2016-08-04 1.0       103520   6.0         GROCERY I 
3   2016-08-05 1.0       103520   2.0         AUTOMOTIVE        
4   2016-08-06 1.0       103520   2.0         AUTOMOTIVE

我想将它们合并到我得到以下内容

    date       store_nbr item_nbr unit_sales    family    f_unit_sales
0   2016-08-01 1.0       103520   3.0         GROCERY I     3.0
1   2016-08-02 1.0       103520   1.0         GROCERY I     10.0
2   2016-08-04 1.0       103520   3.0         GROCERY I     3.0
3   2016-08-05 1.0       103520   2.0         AUTOMOTIVE    5.0 
4   2016-08-06 1.0       103520   2.0         AUTOMOTIVE    6.0

我正在尝试执行以下操作：

both_sales = train_sales_with_family.join(train_family_sales,how='left', on=['store_nbr','family','date'], rsuffix='f_')

但是我收到了一个错误。 ValueError：len（left_on）必须等于＆＃34;右边＆＃34;

索引中的级别数

有关如何合并的任何建议吗？

Answer 1

我认为你需要merge：

both_sales = train_sales.merge(train_family_sales,
                               how='left',
                               on=['store_nbr','family','date'],
                               suffixes=('','_'))

或为join添加set_index - 与MultiIndex参数中的列需要相同级别的on：

both_sales = train_sales.join(train_family_sales.set_index(['store_nbr','family','date']), 
                               on=['store_nbr','family','date'], 
                               rsuffix='_')

print (both_sales)

         date  store_nbr  item_nbr  unit_sales      family unit_sales_
0  2016-08-01        1.0    103520         3.0   GROCERY I          3.0
1  2016-08-02        1.0    103520         1.0   GROCERY I         10.0
2  2016-08-04        1.0    103520         6.0   GROCERY I          3.0
3  2016-08-05        1.0    103520         2.0  AUTOMOTIVE          5.0
4  2016-08-06        1.0    103520         2.0  AUTOMOTIVE          5.0

使用groupby但不创建系列

1 个答案: