减去数据框中的A列和B列

Question

我在一个数据帧中有两个字符串列，我想从a中减去B中的常用词

A ->  Stack Overlflow is great            


B -> stack great


A-B -> overflow is

我尝试了以下代码。但它只有在B列有一个单词时才起作用。你知道吗

df['A-B'] = [' '.join(set(a.split())-set(b.split())) for a, b in zip(df['A'], df['B'])]

我能做些什么改变吗？这样当B有多个单词时它就可以工作了？你知道吗

Answer 1

您可以使用^{}：

df['A-B']=df.apply(lambda x: ' '.join(np.setdiff1d(x['A'].lower().split(),
                                        x['B'].lower().split())),axis=1)
print(df)

---

                          A            B           A-B
0  Stack Overlflow is great  stack great  is overlflow

您的解决方案就快到了，只需在压缩它们时添加^{}：

df['A-B']=[' '.join(set(a.split())-set(b.split())) 
        for a, b in zip(df['A'].str.lower(), df['B'].str.lower())]

---

如果序列中有重复的字符串，请使用^{}，这有助于删除重复的字符串set()，但也要保持顺序：

df = pd.DataFrame({'A': ['Stack Overlflow is great is great'], 'B': ['stack great']})
                                  A            B
0  Stack Overlflow is great is great  stack great

---

from collections import OrderedDict
df['A-B']=[' '.join([ele for ele in OrderedDict.fromkeys(a) if ele not in b ])
 for a,b in zip(df.A.str.lower().str.split(),df.B.str.lower().str.split())]
print(df)

---

                                   A            B           A-B
0  Stack Overlflow is great is great  stack great  overlflow is