在 python 中有效地从现有变量创建新变量

Question

我正在尝试重新编码变量。我已经能够用地图做到这一点，但是，我试图找出一种有效的方法来将重新编码的多个值（a、b、c）转换为单个值。在下面的示例中，我对 Asian 进行了三种不同的分类，并想相应地重新编码它们。我尝试使用布尔值，但出现以下错误。

df['Race'] = df['Race'].map({ 
    'Black or African American' : 'Black', 
    'White' : 'White', 
    'Hispanic or Latino': 'Non-White Hispanic', 
    ('Asian' | 'Asian/Indian/Pacific Islander' | 'Native Hawaiian or Other Pacific Islander') : 'Asian/Pacific Islander', 
    ('American Indian or Alaska Native' | 'Other/Mixed') : 'Multiracial/other', 
    'Unspecified' : np.nan
})

TypeError: unsupported operand type(s) for |: 'str' and 'str'

是否有更简单但仍然有效的方法将多个变量重新编码为单个值？不一定是地图，那是我最熟悉的。

Answer 1

如何使用字典理解和解包：

df['Race'] = df['Race'].map({ 
    'Black or African American' : 'Black', 
    'White' : 'White', 
    'Hispanic or Latino': 'Non-White Hispanic', 
    **{i: 'Asian/Pacific Islander' for i in ('Asian', 'Asian/Indian/Pacific Islander', 'Native Hawaiian or Other Pacific Islander')}, 
    **{i: 'Multiracial/other' for i in ('American Indian or Alaska Native', 'Other/Mixed')}, 
    'Unspecified' : np.nan
})

Answer 2

事实上，这样做就可以了：

df['Race'] = df['Race'].map({ 
    'Black or African American' : 'Black', 
    'White' : 'White', 
    'Hispanic or Latino': 'Non-White Hispanic', 

    'Asian': 'Asian/Pacific Islander',
    'Asian/Indian/Pacific Islander': 'Asian/Pacific Islander',
    'Native Hawaiian or Other Pacific Islander': 'Asian/Pacific Islander', 

    'American Indian or Alaska Native': 'Multiracial/other',
    'Other/Mixed': 'Multiracial/other', 

    'Unspecified' : np.nan
})

Answer 3

使用dict.fromkeys:

df['Race'] = df['Race'].map({ 
    'Black or African American' : 'Black', 
    'White' : 'White', 
    'Hispanic or Latino': 'Non-White Hispanic',  
    'Unspecified' : np.nan,
    **dict.fromkeys(['Asian', 'Asian/Indian/Pacific Islander', 'Native Hawaiian or Other Pacific Islander'], 'Asian/Pacific Islander'), 
    **dict.fromkeys(['American Indian or Alaska Native', 'Other/Mixed'], 'Multiracial/other'),
})

Answer 4

使用 apply 提高可读性。

 race=[
    'Black or African American', 
    'White', 
    'Hispanic or Latino', 
    'Asian', 
    'Asian/Indian/Pacific Islander',
    'Native Hawaiian or Other Pacific Islander',
    'American Indian or Alaska Native',
    'Other/Mixed',
   'Unspecified'
 ]

 df=pd.DataFrame({'Race':race})

 def lookup(x):
     dictLookup={ 
        'Black or African American' : 'Black', 
        'White' : 'White', 
        'Hispanic or Latino': 'Non-White Hispanic', 
        'Unspecified' : np.nan,
        **{i:'Asian/Pacific Islander' for i in('Asian', 'Asian/Indian/Pacific Islander' , 'Native Hawaiian or Other Pacific Islander')},
        **{i:'Multiracial/other' for i in('American Indian or Alaska Native','Alaska Native', 'Other/Mixed')}
     }
     return dictLookup[x]

    df['Race']=df['Race'].apply(lambda x: lookup(x))
                         
    print(df.head(20))

在 python 中有效地从现有变量创建新变量

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