python - Pandas groupby 查找公共(public)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']})

我想计算 fav_fruit 列上的 grouby 之后的 Name 列中常见单词的数量，因此对于 apple 计数是 2 justin bieber，对于 Orange kim,lee 和 pineapple 是 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使用自定义函数 - 首先创建一大串连接值，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