Python 3.x 使用|和&；的多个if语句的Pandas语法；操作员

如果我想在Pandas中使用以下语法使用比较运算符创建

If

条件，我是否需要将

或

条件包装在它们自己的括号内

没有额外的括号

加括号

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或者两者都有效？很难从文档中推断出哪些应该是正确的。

我认为最好使用：

但你的说法不同：

@深空-评论：

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它与大熊猫没有直接关系，而是与动物有关。位and（&）优先于位or（|），因此在这种情况下需要括号

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它与pandas没有直接关系，而是与Python中的运算符优先级有关。按位

和

（

和

）优先于按位

或

（

|

），因此在本例中需要括号。感谢jezrael，因此包装

或

条件似乎会导致与

isin

相同的结果，而第一个条件不起作用，这正是我所经历的。我将使用

isin

版本，谢谢。

df.loc[(df['Col1'] == 'Yes') &
       (df['Col2'] == 'Yes') &
       (df['Col3'] == 'No') |
       (df['Col3'] == 'Yes') |
       (df['Col3'] == 'Maybe'),
       ['Result']] = 'CORRECT'

df.loc[(df['Col1'] == 'Yes') &
       (df['Col2'] == 'Yes') &
       ((df['Col3'] == 'No') |
       (df['Col3'] == 'Yes') |
       (df['Col3'] == 'Maybe')),
       ['Result']] = 'CORRECT'

df.loc[(df['Col1'] == 'Yes') &
       (df['Col2'] == 'Yes') &
       (df['Col3'].isin(['No', 'Yes', 'Maybe'])), 'Result'] = 'CORRECT'

np.random.seed(1245)

a = ['No', 'Yes', 'Maybe']
df = pd.DataFrame(np.random.choice(a, size=(10, 3)), columns=['Col1','Col2','Col3'])

df.loc[(df['Col1'] == 'Yes') &
       (df['Col2'] == 'Yes') &
       (df['Col3'] == 'No') |
       (df['Col3'] == 'Yes') |
       (df['Col3'] == 'Maybe'),
       'Result1'] = 'CORRECT'

df.loc[(df['Col1'] == 'Yes') &
       (df['Col2'] == 'Yes') &
       ((df['Col3'] == 'No') |
       (df['Col3'] == 'Yes') |
       (df['Col3'] == 'Maybe')),
       'Result2'] = 'CORRECT'

df.loc[(df['Col1'] == 'Yes') &
       (df['Col2'] == 'Yes') &
       (df['Col3'].isin(['No', 'Yes', 'Maybe'])), 'Result3'] = 'CORRECT'

print (df)
    Col1   Col2   Col3  Result1  Result2  Result3
0  Maybe    Yes    Yes  CORRECT      NaN      NaN
1     No  Maybe     No      NaN      NaN      NaN
2    Yes     No  Maybe  CORRECT      NaN      NaN
3  Maybe    Yes     No      NaN      NaN      NaN
4    Yes    Yes  Maybe  CORRECT  CORRECT  CORRECT
5    Yes  Maybe    Yes  CORRECT      NaN      NaN
6  Maybe  Maybe    Yes  CORRECT      NaN      NaN
7  Maybe     No    Yes  CORRECT      NaN      NaN
8  Maybe  Maybe  Maybe  CORRECT      NaN      NaN
9    Yes    Yes     No  CORRECT  CORRECT  CORRECT