将多列类别变量重塑和编码为一种热编码

Question

我有一些看起来如下的数据：

    Owner   Label1  Label2  Label3      
    Bob     Dog     N/A     N/A 
    John    Cat     Mouse   N/A 
    Lee     Dog     Cat     N/A
    Jane    Hamster Rat     Ferret

我想将其重塑为一键编码。像这样：

    Owner   Dog     Cat     Mouse    Hamster    Rat    Ferret   
    Bob     1       0       0        0          0      0
    John    0       1       1        0          0      0    
    Lee     1       1       0        0          0      0
    Jane    0       0       0        1          1      1

我查看了文档和stackoverflow，但是无法确定实现此目的所需的相关功能。 get_dummies非常接近，但是只有当该类别出现在相应的列中时，它才会为每个类别创建一个前缀。

Answer 1

使用

df.set_index('Owner').stack().str.get_dummies().sum(level=0)
Out[535]: 
       Cat  Dog  Ferret  Hamster  Mouse  Rat
Owner                                       
Bob      0    1       0        0      0    0
John     1    0       0        0      1    0
Lee      1    1       0        0      0    0
Jane     0    0       1        1      0    1

或

s=df.melt('Owner')
pd.crosstab(s.Owner,s.value)
Out[540]: 
value  Cat  Dog  Ferret  Hamster  Mouse  Rat
Owner                                       
Bob      0    1       0        0      0    0
Jane     0    0       1        1      0    1
John     1    0       0        0      1    0
Lee      1    1       0        0      0    0

Answer 2

您可以在堆叠的数据集上使用get_dummies，然后使用groupby和求和：

pd.get_dummies(df.set_index('Owner').stack()).groupby('Owner').sum()

       Cat  Dog  Ferret  Hamster  Mouse  Rat
Owner                                       
Bob      0    1       0        0      0    0
John     1    0       0        0      1    0
Lee      1    1       0        0      0    0
Jane     0    0       1        1      0    1

Answer 3

sklearn.preprocessing.MultiLabelBinarizer

from sklearn.preprocessing import MultiLabelBinarizer

o, l = zip(*[[o, [*filter(pd.notna, l)]] for o, *l in zip(*map(df.get, df))])

mlb = MultiLabelBinarizer()

d = mlb.fit_transform(l)
pd.DataFrame(d, o, mlb.classes_)

      Cat  Dog  Ferret  Hamster  Mouse  Rat
Bob     0    1       0        0      0    0
John    1    0       0        0      1    0
Lee     1    1       0        0      0    0
Jane    0    0       1        1      0    1

---

答案相同

o = df.Owner
l = [[x for x in l if pd.notna(x)] for l in df.filter(like='Label').values]

mlb = MultiLabelBinarizer()

d = mlb.fit_transform(l)
pd.DataFrame(d, o, mlb.classes_)

       Cat  Dog  Ferret  Hamster  Mouse  Rat
Owner                                       
Bob      0    1       0        0      0    0
John     1    0       0        0      1    0
Lee      1    1       0        0      0    0
Jane     0    0       1        1      0    1

Answer 4

pandas.get_dummies函数可一步将分类变量转换为虚拟变量/指标变量