sklearn.preprocessing OneHotEncoder——仅仅是数值型字段才可以，如果是字符类型字段则不能直接搞定...

 

>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder()

>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])  

>>> enc.n_values_
array([2, 3, 4])

>>> enc.feature_indices_
array([0, 2, 5, 9]) >>> enc.transform([[0, 1, 1]]).toarray() array([[ 1., 0., 0., 1., 0., 0., 1., 0., 0.]])

注意：仅仅是数值型字段才可以，如果是字符类型字段则不能直接搞定

需要使用pandas get_dummies搞定

例如：

Using the get_dummies will create a new column for every unique string in a certain column:使用get_dummies进行one-hot编码

pd.get_dummies(df)

还可以：

import pandas as pd
import numpy as np
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import OneHotEncoder

data = pd.DataFrame({'text':['aaa', 'bbb'], 'number_1':[1, 1], 'number_2':[2, 2]})

#    number_1  number_2 text
# 0         1         2  aaa
# 1         1         2  bbb

# SomeEncoder here must be any encoder which will help you to get
# numerical representation from text column
mapper = DataFrameMapper([
    ('text', SomeEncoder),
    (['number_1', 'number_2'], OneHotEncoder())
])
mapper.fit_transform(data)