假设在10个变量中，在数据帧中有3个分类变量，分别为cname1、cname2和cname3。 然后下面的代码将自动在新的数据框架中创建一个热编码变量。

import category_encoders as ce
encoder_var=ce.OneHotEncoder(cols=['cname1','cname2','cname3'],handle_unknown='return_nan',return_df=True,use_cat_names=True)
new_df = encoder_var.fit_transform(old_df)

试试这个:

!pip install category_encoders
import category_encoders as ce

categorical_columns = [...the list of names of the columns you want to one-hot-encode ...]
encoder = ce.OneHotEncoder(cols=categorical_columns, use_cat_names=True)
df_train_encoded = encoder.fit_transform(df_train_small)

df_encoded.head ()

生成的数据框架df_train_encoded与原始数据框架相同，但是分类特征现在被它们的单热编码版本所取代。

更多关于category_encoders的信息请点击这里。

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.]]

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

这对我来说很管用:

pandas.factorize( ['B', 'C', 'D', 'B'] )[0]

输出:

[0, 1, 2, 0]