熊猫：根据从旧数据框中的字符串中提取的数据创建新的数据框

尝试这个：

df['FruitList'] = df['REMARKS'].str.extract('\[(.+?)\]').squeeze().str.split(',')
df['CODE'] = df['REMARKS'].str.extract('@\s(\d+)')
df.explode('FruitList')

输出：

  KEY RECORD SERIAL                                            REMARKS   FruitList  CODE
0   1      1   1470     FRUIT[APPLES,ORANGES,PEARS] IS HEALTHY FOR YOU      APPLES   NaN
0   1      1   1470     FRUIT[APPLES,ORANGES,PEARS] IS HEALTHY FOR YOU     ORANGES   NaN
0   1      1   1470     FRUIT[APPLES,ORANGES,PEARS] IS HEALTHY FOR YOU       PEARS   NaN
1   2      1   2321  I LIKE FRUIT[BANANAS,CHERRIES,GRAPES], BUT I D...     BANANAS   NaN
1   2      1   2321  I LIKE FRUIT[BANANAS,CHERRIES,GRAPES], BUT I D...    CHERRIES   NaN
1   2      1   2321  I LIKE FRUIT[BANANAS,CHERRIES,GRAPES], BUT I D...      GRAPES   NaN
2   3      1    300   THERE IS FRUIT[LEMONS,ORANGES,GRAPEFRUIT] @ 1234      LEMONS  1234
2   3      1    300   THERE IS FRUIT[LEMONS,ORANGES,GRAPEFRUIT] @ 1234     ORANGES  1234
2   3      1    300   THERE IS FRUIT[LEMONS,ORANGES,GRAPEFRUIT] @ 1234  GRAPEFRUIT  1234

如果您愿意，可以删除“备注”：

df.explode('FruitList').drop('REMARKS', axis=1))

输出：

  KEY RECORD SERIAL   FruitList  CODE
0   1      1   1470      APPLES   NaN
0   1      1   1470     ORANGES   NaN
0   1      1   1470       PEARS   NaN
1   2      1   2321     BANANAS   NaN
1   2      1   2321    CHERRIES   NaN
1   2      1   2321      GRAPES   NaN
2   3      1    300      LEMONS  1234
2   3      1    300     ORANGES  1234
2   3      1    300  GRAPEFRUIT  1234