我知道我来晚了，但是以自动方式热编码数据框架的最简单方法是使用这个函数:

def hot_encode(df):
    obj_df = df.select_dtypes(include=['object'])
    return pd.get_dummies(df, columns=obj_df.columns).values

熊猫的热编码非常简单:

def one_hot(df, cols):
    """
    @param df pandas DataFrame
    @param cols a list of columns to encode 
    @return a DataFrame with one-hot encoding
    """
    for each in cols:
        dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)
        df = pd.concat([df, dummies], axis=1)
    return df

编辑:

使用sklearn的LabelBinarizer实现one_hot的另一种方法:

from sklearn.preprocessing import LabelBinarizer 
label_binarizer = LabelBinarizer()
label_binarizer.fit(all_your_labels_list) # need to be global or remembered to use it later

def one_hot_encode(x):
    """
    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 label_binarizer.transform(x)

这对我来说很管用:

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

输出:

[0, 1, 2, 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

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.]])

我知道我来晚了，但是以自动方式热编码数据框架的最简单方法是使用这个函数:

def hot_encode(df):
    obj_df = df.select_dtypes(include=['object'])
    return pd.get_dummies(df, columns=obj_df.columns).values