如何根据以下 table 从客户 ID 和产品代码中分组并将它们放在一行中？

Question

如何根据客户 ID 和产品代码按以下 table 进行分组，并使用 Python 将它们分成一行，如下所示？

Customer ID	Product Code	Days since the last transaction
A	1	10
A	1	23
A	1	7
A	2	8
A	2	9
A	3	6
B	1	18
B	2	4
B	3	4
B	3	12
C	2	27
C	2	15

需要按客户 ID 和产品代码对它们进行分组以低于 table。

Customer ID	Product Code	D1	D2	D3
A	1	10	23	7
A	2	8	9	N/A
A	3	6	N/A	N/A
B	1	18	N/A	N/A
B	2	4	N/A	N/A
B	3	4	12	N/A
C	2	27	15	N/A

Answer 1

df[''] = df.groupby(['Customer ID', 'Product Code']).cumcount()
df = df.pivot(index=['Customer ID', 'Product Code'], columns='')
print(df)

输出：

                         Days since the last transaction
                                                       0     1    2
Customer ID Product Code
A           1                                       10.0  23.0  7.0
            2                                        8.0   9.0  NaN
            3                                        6.0   NaN  NaN
B           1                                       18.0   NaN  NaN
            2                                        4.0   NaN  NaN
            3                                        4.0  12.0  NaN
C           2                                       27.0  15.0  NaN

Answer 2

下面的 python 代码也适用于我。

#keep only the needed data
grouped = df.groupby(['Customer_ID','Product Code'], as_index=False).agg({"Days since the last transaction": lambda x: x.tolist()[:3]+[x.iat[-1]]}).explode("Days since the last transaction")

#get the count for the age columns
grouped["idx"] = grouped.groupby(['Customer_ID','Product Code']).cumcount().add(1)

#pivot to get the required structure
output = grouped.pivot(["Customer_ID","Product Code"],"idx","Days since the last transaction").add_prefix("Days since the last transaction").reset_index().rename_axis(None, axis=1)

output.head()

如何根据以下 table 从客户 ID 和产品代码中分组并将它们放在一行中？

How can I group by below table from Customer ID and Product Code and get them to one row?

python

pandas

pandas-groupby