Pandas as有内置函数“get_dummies”来获取特定列/秒的热编码。

单热编码的一行代码:

df=pd.concat([df,pd.get_dummies(df['column name'],prefix='column name')],axis=1).drop(['column name'],axis=1)

您可以使用numpy。眼睛的功能。

import numpy as np

def one_hot_encode(x, n_classes):
    """
    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
    : x: List of sample Labels
    : return: Numpy array of one-hot encoded labels
     """
    return np.eye(n_classes)[x]

def main():
    list = [0,1,2,3,4,3,2,1,0]
    n_classes = 5
    one_hot_list = one_hot_encode(list, n_classes)
    print(one_hot_list)

if __name__ == "__main__":
    main()

结果

D:\Desktop>python test.py
[[ 1.  0.  0.  0.  0.]
 [ 0.  1.  0.  0.  0.]
 [ 0.  0.  1.  0.  0.]
 [ 0.  0.  0.  1.  0.]
 [ 0.  0.  0.  0.  1.]
 [ 0.  0.  0.  1.  0.]
 [ 0.  0.  1.  0.  0.]
 [ 0.  1.  0.  0.  0.]
 [ 1.  0.  0.  0.  0.]]

下面是使用DictVectorizer和Pandas datafframe .to_dict('records')方法的解决方案。

>>> import pandas as pd
>>> X = pd.DataFrame({'income': [100000,110000,90000,30000,14000,50000],
                      'country':['US', 'CAN', 'US', 'CAN', 'MEX', 'US'],
                      'race':['White', 'Black', 'Latino', 'White', 'White', 'Black']
                     })

>>> from sklearn.feature_extraction import DictVectorizer
>>> v = DictVectorizer()
>>> qualitative_features = ['country','race']
>>> X_qual = v.fit_transform(X[qualitative_features].to_dict('records'))
>>> v.vocabulary_
{'country=CAN': 0,
 'country=MEX': 1,
 'country=US': 2,
 'race=Black': 3,
 'race=Latino': 4,
 'race=White': 5}

>>> X_qual.toarray()
array([[ 0.,  0.,  1.,  0.,  0.,  1.],
       [ 1.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  1.,  0.],
       [ 1.,  0.,  0.,  0.,  0.,  1.],
       [ 0.,  1.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  1.,  1.,  0.,  0.]])

一个在numpy中使用矢量化并在pandas中应用的简单示例:

import numpy as np

a = np.array(['male','female','female','male'])

#define function
onehot_function = lambda x: 1.0 if (x=='male') else 0.0

onehot_a = np.vectorize(onehot_function)(a)

print(onehot_a)
# [1., 0., 0., 1.]

# -----------------------------------------

import pandas as pd

s = pd.Series(['male','female','female','male'])
onehot_s = s.apply(onehot_function)

print(onehot_s)
# 0    1.0
# 1    0.0
# 2    0.0
# 3    1.0
# dtype: float64

我在我的声学模型中使用了这个: 也许这对你的模型有帮助。

def one_hot_encoding(x, n_out):
    x = x.astype(int)  
    shape = x.shape
    x = x.flatten()
    N = len(x)
    x_categ = np.zeros((N,n_out))
    x_categ[np.arange(N), x] = 1
    return x_categ.reshape((shape)+(n_out,))

你也可以做以下事情。注意，对于下面的内容，您不必使用pd.concat。

import pandas as pd 
# intialise data of lists. 
data = {'Color':['Red', 'Yellow', 'Red', 'Yellow'], 'Length':[20.1, 21.1, 19.1, 18.1],
       'Group':[1,2,1,2]} 

# Create DataFrame 
df = pd.DataFrame(data) 

for _c in df.select_dtypes(include=['object']).columns:
    print(_c)
    df[_c]  = pd.Categorical(df[_c])
df_transformed = pd.get_dummies(df)
df_transformed

还可以将显式列更改为分类列。例如，这里我正在更改颜色和组

import pandas as pd 
# intialise data of lists. 
data = {'Color':['Red', 'Yellow', 'Red', 'Yellow'], 'Length':[20.1, 21.1, 19.1, 18.1],
       'Group':[1,2,1,2]} 

# Create DataFrame 
df = pd.DataFrame(data) 
columns_to_change = list(df.select_dtypes(include=['object']).columns)
columns_to_change.append('Group')
for _c in columns_to_change:
    print(_c)
    df[_c]  = pd.Categorical(df[_c])
df_transformed = pd.get_dummies(df)
df_transformed