python - 如何在 sklearn 中一次在多个列上应用预处理方法

Question

我的问题是我的熊猫数据框中有很多列，我正在尝试使用 sklearn-pandas 库中的数据帧映射器应用 sklearn 预处理，例如

mapper= DataFrameMapper([
    ('gender',sklearn.preprocessing.LabelBinarizer()),
    ('gradelevel',sklearn.preprocessing.LabelEncoder()),
    ('subject',sklearn.preprocessing.LabelEncoder()),
    ('districtid',sklearn.preprocessing.LabelEncoder()),
    ('sbmRate',sklearn.preprocessing.StandardScaler()),
    ('pRate',sklearn.preprocessing.StandardScaler()),
    ('assn1',sklearn.preprocessing.StandardScaler()),
    ('assn2',sklearn.preprocessing.StandardScaler()),
    ('assn3',sklearn.preprocessing.StandardScaler()),
    ('assn4',sklearn.preprocessing.StandardScaler()),
    ('assn5',sklearn.preprocessing.StandardScaler()),
    ('attd1',sklearn.preprocessing.StandardScaler()),
    ('attd2',sklearn.preprocessing.StandardScaler()),
    ('attd3',sklearn.preprocessing.StandardScaler()),
    ('attd4',sklearn.preprocessing.StandardScaler()),
    ('attd5',sklearn.preprocessing.StandardScaler()),
    ('sbm1',sklearn.preprocessing.StandardScaler()),
    ('sbm2',sklearn.preprocessing.StandardScaler()),
    ('sbm3',sklearn.preprocessing.StandardScaler()),
    ('sbm4',sklearn.preprocessing.StandardScaler()),
    ('sbm5',sklearn.preprocessing.StandardScaler())
 ])

我只是想知道是否还有另一种更简洁的方法可以一次预处理许多变量而无需明确写出它们。

我发现有点烦人的另一件事是，当我将所有 pandas 数据框转换为 sklearn 可以使用的数组时，它们会丢失列名特征，这使得选择非常困难。有谁知道在将 pandas 数据帧更改为 np 数组时如何将列名保留为键？

太感谢了！

Answer 1

from sklearn.preprocessing import LabelBinarizer, LabelEncoder, StandardScaler
from sklearn_pandas import DataFrameMapper

encoders = ['gradelevel', 'subject', 'districtid']
scalars = ['sbmRate', 'pRate', 'assn1', 'assn2', 'assn3', 'assn4', 'assn5', 'attd1', 'attd2', 'attd3', 'attd4', 'attd5', 'sbm1', 'sbm2', 'sbm3', 'sbm4', 'sbm5']
mapper = DataFrameMapper(
    [('gender', LabelBinarizer())] +
    [(encoder, LabelEncoder()) for encoder in encoders] +
    [(scalar, StandardScaler()) for scalar in scalars]
)

如果您经常这样做，您甚至可以编写自己的函数：

mapper = data_frame_mapper(binarizers=['gender'],
    encoders=['gradelevel', 'subject', 'districtid'],
    scalars=['sbmRate', 'pRate', 'assn1', 'assn2', 'assn3', 'assn4', 'assn5', 'attd1', 'attd2', 'attd3', 'attd4', 'attd5', 'sbm1', 'sbm2', 'sbm3', 'sbm4', 'sbm5'])