字符串包含两个pandas系列

Question

我在pandas数据框中有一些字符串.我想在相邻列中搜索该字符串的存在.

在下面的例子中,我想搜索'choice'系列中的字符串是否包含在'fruit'系列中,在新列'choice_match'中返回true(1)或false(0).

示例DataFrame:

import pandas as pd
d = {'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'fruit': [
'apple, banana', 'apple', 'apple', 'pineapple', 'apple, pineapple',            'orange', 'apple, orange', 'orange', 'banana', 'apple, peach'],
'choice': ['orange', 'orange', 'apple', 'pineapple', 'apple', 'orange',  'orange', 'orange', 'banana', 'banana']}
df = pd.DataFrame(data=d)

期望的DataFrame:

import pandas as pd
d = {'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'fruit': [
'apple, banana', 'apple', 'apple', 'pineapple', 'apple, pineapple',   'orange', 'apple, orange', 'orange', 'banana', 'apple, peach'],
'choice': ['orange', 'orange', 'apple', 'pineapple', 'apple', 'orange',      'orange', 'orange', 'banana', 'banana'],
'choice_match': [0, 0, 1, 1, 1, 1, 1, 1, 1, 0]}
df = pd.DataFrame(data=d)

Answer 1

In [75]: df['choice_match'] = (df['fruit']
                                 .str.split(',\s*', expand=True)
                                 .eq(df['choice'], axis=0)
                                 .any(1).astype(np.int8))

In [76]: df
Out[76]:
   ID     choice             fruit  choice_match
0   1     orange     apple, banana             0
1   2     orange             apple             0
2   3      apple             apple             1
3   4  pineapple         pineapple             1
4   5      apple  apple, pineapple             1
5   6     orange            orange             1
6   7     orange     apple, orange             1
7   8     orange            orange             1
8   9     banana            banana             1
9  10     banana      apple, peach             0

一步步:

In [78]: df['fruit'].str.split(',\s*', expand=True)
Out[78]:
           0          1
0      apple     banana
1      apple       None
2      apple       None
3  pineapple       None
4      apple  pineapple
5     orange       None
6      apple     orange
7     orange       None
8     banana       None
9      apple      peach

In [79]: df['fruit'].str.split(',\s*', expand=True).eq(df['choice'], axis=0)
Out[79]:
       0      1
0  False  False
1  False  False
2   True  False
3   True  False
4   True  False
5   True  False
6  False   True
7   True  False
8   True  False
9  False  False

In [80]: df['fruit'].str.split(',\s*', expand=True).eq(df['choice'], axis=0).any(1)
Out[80]:
0    False
1    False
2     True
3     True
4     True
5     True
6     True
7     True
8     True
9    False
dtype: bool

In [81]: df['fruit'].str.split(',\s*', expand=True).eq(df['choice'], axis=0).any(1).astype(np.int8)
Out[81]:
0    0
1    0
2    1
3    1
4    1
5    1
6    1
7    1
8    1
9    0
dtype: int8

Answer 2

这是一种方式:

df['choice_match'] = df.apply(lambda row: row['choice'] in row['fruit'].split(','),\
                              axis=1).astype(int)

说明

• df.apply与axis=1通过每行和循环应用逻辑; 它接受匿名lambda函数.
• row['fruit'].split(',')从fruit列创建列表.这是必要的,例如,apple不考虑pineapple.
• astype(int) 必须将布尔值转换为整数以便显示.