方法1：
a = ['A','B','A','C']
from sklearn.preprocessing import OneHotEncoder,LabelEncoder
label_value = label_encoder.fit_transform(a)
>>label_encoder.classes_
array(['A', 'B', 'C'], dtype='<U1')
>>label_value 
array([0, 1, 0, 2], dtype=int64)
encoder = OneHotEncoder()
>>one_hot.toarray()
[[ 1. 0. 0.]
[ 0. 1. 0.]
[ 1. 0. 0.]
[ 0. 0. 1.]]

方法2：
from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
one_hot = encoder.fit_transform(a)
>>one_hot
array([[1, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 0, 1]])

方法3：
def dense_to_one_hot(labels_dense, num_classes):
   """Convert class labels from scalars to one-hot vectors."""
   num_labels = labels_dense.shape[0]
   index_offset = np.arange(num_labels) * num_classes
   labels_one_hot = np.zeros((num_labels, num_classes))
   labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
   return labels_one_hot