在 Pandas Dataframe 中创建由另一列分区的列的所有可能排列

Question

我的数据框看起来像这样：

我的目标是：

解释：

1. 每个客户都下了 3 个订单
2. 每个订单可以从多个类别中购买
3. 期望状态：获取客户按订单顺序购买的类别的所有可能排列。第二张图片将有助于更好地理解这一点
4. 处于期望状态的Category1 表示以第一顺序购买的Category，Category2 表示以第二顺序购买的Category，依此类推。

我正在使用的代码：

start_time = time.time()

df = pd.DataFrame()
for CustomerName in base_df.CustomerName.unique():
    df1 = base_df[(base_df['CustomerName']== CustomerName)][['CustomerName','order_seq','Category']]
    df2 = pd.DataFrame(index=pd.MultiIndex.from_product([subdf['Category'] for p, subdf in df1.groupby(['order_seq'])], names = df1.order_seq.unique())).reset_index()
    df2['CustomerName'] = CustomerName
    df = df.append(df2)

print("--- %s seconds ---" %(time.time() - start_time))

在我的数据集上运行大约需要 10 分钟 - 寻找更快的方法。

我现在正在研究 Pandas，但也欢迎 R 或 SQL 的指针！谢谢！

Answer 1

考虑合并三个 OrderSequence 数据框，每个数据框都连接到不同的 CustomerName：

import pandas as pd

df = pd.DataFrame({'CustomerName': [1,1,1,1,1,1,1,2,2,2,3,3,3,3],
                   'OrderSequence': [1,2,2,2,3,3,3,1,2,3,1,1,2,3],
                   'Category': ['Food','Food','Clothes','Furniture','Clothes','Food','Toys',
                                'Clothes','Toys','Food','Furniture','Toys','Food','Food']})

finaldf = pd.DataFrame(df['CustomerName'].drop_duplicates())

for i in range(1,4):
    seqdf = df[df['OrderSequence']==i][['CustomerName', 'Category']].\               
                                      rename(columns={'Category':'Category'+str(i)})
    finaldf = pd.merge(finaldf, seqdf, on=['CustomerName'])

print(finaldf)

#     CustomerName  Category1  Category2 Category3
# 0              1       Food       Food   Clothes
# 1              1       Food       Food      Food
# 2              1       Food       Food      Toys
# 3              1       Food    Clothes   Clothes
# 4              1       Food    Clothes      Food
# 5              1       Food    Clothes      Toys
# 6              1       Food  Furniture   Clothes
# 7              1       Food  Furniture      Food
# 8              1       Food  Furniture      Toys
# 9              2    Clothes       Toys      Food
# 10             3  Furniture       Food      Food
# 11             3       Toys       Food      Food

诚然，上面的设置首先是在 SQL 中使用自联接考虑的，然后翻译为 pandas：

SELECT t1.CustomerName, t2.Category AS Category1, 
       t3.Category AS Category2, t4.Category AS Category3

FROM (SELECT DISTINCT CustomerName FROM DataFrame) AS t1 
INNER JOIN DataFrame AS t2 
ON t1.CustomerName = t2.CustomerName 
INNER JOIN DataFrame AS t3
ON t1.CustomerName = t3.CustomerName 
INNER JOIN DataFrame AS t4
ON t1.CustomerName = t4.CustomerName

WHERE (t2.OrderSequence=1) AND (t3.OrderSequence=2) AND (t4.OrderSequence=3);

Answer 2

好的。做了一些工作，但我做到了。希望有帮助。

import pandas as pd
import numpy as np
from itertools import combinations

df = pd.DataFrame([], columns=['CustomerName','Order Sequence','Category'])

df['CustomerName'] = [1,1,1,1,1,1,1,2,2,2,3,3,3,3]
df['Order Sequence'] = [1,2,2,2,3,3,3,1,2,3,1,1,2,3]
df['Category'] = ['Food','Food','Clothes','Furniture','Clothes','Food','Toys','Clothes','Toys','Food','Furniture','Toys','Food','Food']

df2 = pd.DataFrame([], columns=['CustomerName','Category1','Category2','Category3'])

for CN in sorted(set(df['CustomerName'])):

    df_temp = pd.DataFrame([], columns=['CustomerName','Category1','Category2','Category3'])

    list_OS_1 = []
    list_OS_2 = []
    list_OS_3 = []

    MMC = reduce(lambda x, y: x*y,df.loc[df['CustomerName']==CN, 'Order Sequence'].value_counts().values)

    for N in np.arange(MMC / len(df.loc[((df['CustomerName']==CN) & (df['Order Sequence']==1)), 'Category'])):

        for CTG in df.loc[((df['CustomerName']==CN) & (df['Order Sequence']==1)), 'Category']:

            list_OS_1.append(CTG) 

    for N in np.arange(MMC / len(df.loc[((df['CustomerName']==CN) & (df['Order Sequence']==2)), 'Category'])):

        for CTG in df.loc[((df['CustomerName']==CN) & (df['Order Sequence']==2)), 'Category']:

            list_OS_2.append(CTG) 

    for N in np.arange(MMC / len(df.loc[((df['CustomerName']==CN) & (df['Order Sequence']==3)), 'Category'])):

        for CTG in df.loc[((df['CustomerName']==CN) & (df['Order Sequence']==3)), 'Category']:

            list_OS_3.append(CTG) 

    df_temp['Category1'] = list_OS_1
    df_temp['Category2'] = list_OS_2
    df_temp['Category3'] = list_OS_3
    df_temp['CustomerName'] = CN

    df2 = pd.concat([df2,df_temp],0)

print (df2)

输出：

   CustomerName  Category1  Category2 Category3
0           1.0       Food       Food   Clothes
1           1.0       Food    Clothes      Food
2           1.0       Food  Furniture      Toys
3           1.0       Food       Food   Clothes
4           1.0       Food    Clothes      Food
5           1.0       Food  Furniture      Toys
6           1.0       Food       Food   Clothes
7           1.0       Food    Clothes      Food
8           1.0       Food  Furniture      Toys
0           2.0    Clothes       Toys      Food
0           3.0  Furniture       Food      Food
1           3.0       Toys       Food      Food

ps：它不是动态的，所以如果你添加或删除类别，它会被淘汰。 但只要它遵循你通过我的初始标准，它就可以工作