熊猫groupby找到常见的字符串

Question

我的数据框：

    Name              fav_fruit
0   justin              apple
1   bieber justin       apple
2   Kris Justin bieber  apple
3   Kim Lee             orange
4   lee kim             orange
5   mary barnet         orange
6   tom hawkins         pears
7   Sr Tom Hawkins      pears
8   Jose Hawkins        pears
9   Shanita             pineapple
10  Joe                 pineapple

df1=pd.DataFrame({'Name':['justin','bieber justin','Kris Justin bieber','Kim Lee','lee kim','mary barnet','tom hawkins','Sr Tom Hawkins','Jose Hawkins','Shanita','Joe'],
'fav_fruit':['apple'
,'apple'
,'apple'
,'orange'
,'orange'
,'orange'
,'pears'
,'pears','pears'
,'pineapple','pineapple']})

我想在fav_fruit列上的grouby之后计算Name列中的常用单词数，因此对于苹果，计数为2贾斯汀·比伯，对于橙色kim，lee和对于菠萝，计数为0

预期输出：

Name                  fav_fruit            count
0   justin              apple                2
1   bieber justin       apple                2
2   Kris Justin bieber  apple                2
3   Kim Lee             orange               2
4   lee kim             orange               2
5   mary barnet         orange               2
6   tom hawkins         pears                2
7   Sr Tom Hawkins      pears                2
8   Jose Hawkins        pears                2
9   Shanita             pineapple            0
10  Joe                 pineapple            0

Answer 1

我认为需要transform具有自定义功能-首先创建一个大字符串连接值，转换为小写并拆分，最后使用collections.Counter过滤所有重复值：

from collections import Counter

def f(x):
    a = ' '.join(x).lower().split()
    return len([k for k, v in Counter(a).items() if v != 1])

df['count'] = df.groupby('fav_fruit')['Name'].transform(f)
print (df)
                  Name  fav_fruit  count
0               justin      apple      2
1        bieber justin      apple      2
2   Kris Justin bieber      apple      2
3              Kim Lee     orange      2
4              lee kim     orange      2
5          mary barnet     orange      2
6          tom hawkins      pears      2
7       Sr Tom Hawkins      pears      2
8         Jose Hawkins      pears      2
9              Shanita  pineapple      0
10                 Joe  pineapple      0