使用熊猫.替换可更改列中除一个值外的所有值

Question

假设我有下面这个数据框，并且我想将所有非' united states' native_country更改为'Other'，我可以使用pandas吗?代替做这个?如果不是，我该怎么办?

age education       marital_status  occupation  race    sex     native_country
1   37  Non-grad        Married         Sales       Other   Female  ?
2   39  HS-grad         Married         Sales       Other   Female  Dominican-Republic
3   29  HS-grad         Married         Sales       White   Female  United-States
4   64  HS-grad         Married         Sales       White   Female  United-States
5   31  HS-grad         Married         Sales       White   Female  United-States
6   53  HS-grad         Married         Sales       White   Female  United-States
7   56  HS-grad         Married         Sales       White   Female  United-States
8   28  Bachelors       Married         Sales       White   Female  United-States
9   52  Some-college    Married         Sales       White   Female  Canada
10  23  Some-college    Married         Sales       White   Female  United-States

我想要的输出:

age education       marital_status  occupation  race    sex     native_country
1   37  Non-grad        Married         Sales       Other   Female  Other
2   39  HS-grad         Married         Sales       Other   Female  Other
3   29  HS-grad         Married         Sales       White   Female  United-States
4   64  HS-grad         Married         Sales       White   Female  United-States
5   31  HS-grad         Married         Sales       White   Female  United-States
6   53  HS-grad         Married         Sales       White   Female  United-States
7   56  HS-grad         Married         Sales       White   Female  United-States
8   28  Bachelors       Married         Sales       White   Female  United-States
9   52  Some-college    Married         Sales       White   Female  Other
10  23  Some-college    Married         Sales       White   Female  United-States

Answer 1

using mask or loc

# using mask, when native country is not United-States, makes it other, else
# leave value as is
df['native_country'] = df['native_country'].mask(df['native_country'].ne('United-States'),'other')
df

或

# using loc, updated native country, where its not united-states
df.loc[df['native_country'].ne('United-States'), 'native_country'] = 'other'
df

age     education   marital_status  occupation  race    sex     native_country
1   37  Non-grad    Married     Sales   Other   Female  other
2   39  HS-grad     Married     Sales   Other   Female  other
3   29  HS-grad     Married     Sales   White   Female  United-States
4   64  HS-grad     Married     Sales   White   Female  United-States
5   31  HS-grad     Married     Sales   White   Female  United-States
6   53  HS-grad     Married     Sales   White   Female  United-States
7   56  HS-grad     Married     Sales   White   Female  United-States
8   28  Bachelors   Married     Sales   White   Female  United-States
9   52  Some-college    Married     Sales   White   Female  other
10  23  Some-college    Married     Sales   White   Female  United-States