Question

我的数据框看起来像这样：

Customer_ID     Category   Products 
  1               Veg         A
  2               Veg         B
  3              Fruit        A   
  3              Fruit        B
  3               Veg         B 
  1              Fruit        A
  3               Veg         C 
  1              Fruit        C

我想找出购买产品的每个类别的每个客户ID，并相应地为每个产品创建一个列。输出看起来像这样：

Customer_ID     Category    Pro_1    Pro_2     Pro_3
  1               Veg        A        NA         NA
  1              Fruit       A        NA         C
  2               Veg        NA       B          NA
  3               Veg        NA       B          C
  3              Fruit       A        B          NA

Answer 1

使用crosstab的另一个选项：

pd.crosstab([df['Customer_ID'],df['Category']], df['Products'])

输出：

Products              A  B  C
Customer_ID Category         
1           Fruit     1  0  1
            Veg       1  0  0
2           Veg       0  1  0
3           Fruit     1  1  0
            Veg       0  1  1

之后，您可以将索引重置为您想要的类似解决方案。

df = df.reset_index()
Products  Customer_ID Category  A  B  C
0                   1    Fruit  1  0  1
1                   1      Veg  1  0  0
2                   2      Veg  0  1  0
3                   3    Fruit  1  1  0
4                   3      Veg  0  1  1

Answer 2

将jsfiddle与groupby一起使用，但如果重复行数据是并行的话：

df = df.groupby(['Customer_ID','Category','Products'])['Products'].sum().unstack()
df.columns = ['Pro_{}'.format(x) for x in range(1, len(df.columns)+1)]
df = df.reset_index()
print (df)
   Customer_ID Category Pro_1 Pro_2 Pro_3
0            1    Fruit     A  None     C
1            1      Veg     A  None  None
2            2      Veg  None     B  None
3            3    Fruit     A     B  None
4            3      Veg  None     B     C

另一个带有辅助列的解决方案，三元组必须是唯一的：

#if not unique triples remove duplicates
df = df.drop_duplicates(['Customer_ID','Category','Products'])

df['a'] = df['Products']
df = df.set_index(['Customer_ID','Category','Products'])['a'].unstack()
df.columns = ['Pro_{}'.format(x) for x in range(1, len(df.columns)+1)]
df = df.reset_index()
print (df)
   Customer_ID Category Pro_1 Pro_2 Pro_3
0            1    Fruit     A  None     C
1            1      Veg     A  None  None
2            2      Veg  None     B  None
3            3    Fruit     A     B  None
4            3      Veg  None     B     C

Answer 3

试试这个:(不要介意IO只是简单的复制/粘贴）

import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO("""
Customer_ID     Category   Products 
  1               Veg         A
  2               Veg         B
  3              Fruit        A   
  3              Fruit        B
  3               Veg         B 
  1              Fruit        A
  3               Veg         C 
  1              Fruit        C"""), sep='\s+')
df = df.join(pd.get_dummies(df['Products']))
g = df.groupby(['Customer_ID', 'Category']).sum()
print(g)

输出：

                      A  B  C
Customer_ID Category         
1           Fruit     1  0  1
            Veg       1  0  0
2           Veg       0  1  0
3           Fruit     1  1  0
            Veg       0  1  1

将长表转换为宽表并根据行

3 个答案: