你可以fit http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder.fit标签编码器及更高版本transform http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder.transform标签的标准化编码如下:
In [4]: from sklearn import preprocessing
...: import numpy as np
In [5]: le = preprocessing.LabelEncoder()
In [6]: le.fit(np.unique(df.values))
Out[6]: LabelEncoder()
In [7]: list(le.classes_)
Out[7]: ['A', 'B', 'C', 'D', 'E']
In [8]: df.apply(le.transform)
Out[8]:
Feat1 Feat2 Feat3 Feat4 Feat5
0 0 0 0 0 4
1 1 1 2 2 4
2 2 3 2 2 4
3 3 0 2 3 4
默认情况下指定标签的一种方法是:
In [9]: labels = ['A', 'B', 'C', 'D', 'E']
In [10]: enc = le.fit(labels)
In [11]: enc.classes_ # sorts the labels in alphabetical order
Out[11]:
array(['A', 'B', 'C', 'D', 'E'],
dtype='<U1')
In [12]: enc.transform('E')
Out[12]: 4