虽然这个问题和问题很相似:如何在数据框的某些行的所有列上使用pandas应用函数

Although this question is very similar to the question: How to use pandas apply function on all columns of some rows of data frame

我认为这里值得展示几个方法，在一行中使用 np.where 和从 isin、isin生成的布尔掩码> 将返回一个布尔系列，其中任何行都包含列表中的任何匹配项:

I think here it's worth showing a couple methods, on a single line using np.where with a boolean mask generated from isin, isin will return a boolean Series where any rows contain any matches in your list:

In [71]:
lookup = ['C','D']
df['result'] = np.where(df['letters'].isin(lookup), 1, 0)
df

Out[71]:
  letters  result
0       A       0
1       B       0
2       C       1
3       D       1
4       E       0
5       F       0

这里使用 2 个 loc 语句并使用 ~ 反转掩码:

here using 2 loc statements and using ~ to invert the mask:

In [72]:
df.loc[df['letters'].isin(lookup),'result'] = 1
df.loc[~df['letters'].isin(lookup),'result'] = 0
df

Out[72]:
  letters  result
0       A       0
1       B       0
2       C       1
3       D       1
4       E       0
5       F       0