如果它們出現在另一列中，則替換熊貓 df 列中的值

Question

我有一個 pandas 數據to_hide something ，我想替換列中的單詞。 所以我想從這里出發

    to_hide                something
0       bla  there is bla over there
1       sth    i cannot see anything
2  sth else    sth else is beautiful

到這里

  to_hide                    something
0       bla  there is to_hide over there
1       sth        i cannot see anything
2  sth else         to_hide is beautiful

不幸的是，下面會引發錯誤

df = pd.DataFrame({'to_hide':['bla','sth','sth else'], 'something':['there is bla over there','i cannot see anything','sth else is beautiful']})
df.assign(**{'something' : df['something'].str.replace(df['to_hide'], 'to_hide')})

TypeError: 'Series' objects are mutable, thus they cannot be hashed

我該如何編寫它才能正常工作？

Answer 1

您需要在此處使用循環：

df['something'] = [s.replace(pat, 'to_hide')
                   for pat, s in zip(df['to_hide'], df['something'])]

輸出（為清楚起見作為新列）：

    to_hide                something                   something2
0       bla  there is bla over there  there is to_hide over there
1       sth    i cannot see anything        i cannot see anything
2  sth else    sth else is beautiful         to_hide is beautiful

Answer 2

另一種方法是使用apply() ：

df['something'] = df.apply(lambda x: x.something.replace(x.to_hide, 'to_hide'), axis=1)

例子

import pandas as pd

df = pd.DataFrame({'to_hide':['bla','sth','sth else'], 'something':['there is bla over there','i cannot see anything','sth else is beautiful']})
df['something'] = df.apply(lambda x: x.something.replace(x.to_hide, 'to_hide'), axis=1)

輸出

	隱藏	某物
0	布拉	那邊有 to_hide
1	某事	我什么都看不到
2	其他	to_hide 很漂亮