使用sklearn之LabelEncoder将Label标准化

LabelEncoder可以将标签分配一个0—n_classes-1之间的编码
将各种标签分配一个可数的连续编号：

>>> from sklearn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> le.fit([1, 2, 2, 6])
LabelEncoder()
>>> le.classes_
array([1, 2, 6])
>>> le.transform([1, 1, 2, 6]) # Transform Categories Into Integers
array([0, 0, 1, 2], dtype=int64)
>>> le.inverse_transform([0, 0, 1, 2]) # Transform Integers Into Categories
array([1, 1, 2, 6])

>>> le = preprocessing.LabelEncoder()
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
LabelEncoder()
>>> list(le.classes_)
['amsterdam', 'paris', 'tokyo']
>>> le.transform(["tokyo", "tokyo", "paris"]) # Transform Categories Into Integers
array([2, 2, 1], dtype=int64)
>>> list(le.inverse_transform([2, 2, 1])) #Transform Integers Into Categories
['tokyo', 'tokyo', 'paris']

将DataFrame中的所有ID标签转换成连续编号：

from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
df=pd.read_csv('testdata.csv',sep='|',header=None)

    0   1   2   3   4   5
0   37  52  55  50  38  54
1   17  32  20  9   6   48
2   28  10  56  51  45  16
3   27  49  41  30  53  19
4   44  29  8   1   46  13
5   11  26  21  14  7   33
6   0   39  22  33  35  43
7   18  15  47  5   25  34
8   23  2   4   9   3   31
9   12  57  36  40  42  24

le = LabelEncoder()
le.fit(np.unique(df.values))
df.apply(le.transform)

    0   1   2   3   4   5
0   37  52  55  50  38  54
1   17  32  20  9   6   48
2   28  10  56  51  45  16
3   27  49  41  30  53  19
4   44  29  8   1   46  13
5   11  26  21  14  7   33
6   0   39  22  33  35  43
7   18  15  47  5   25  34
8   23  2   4   9   3   31
9   12  57  36  40  42  24

将DataFrame中的每一行ID标签分别转换成连续编号：

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline


class MultiColumnLabelEncoder:
    def __init__(self,columns = None):
        self.columns = columns # array of column names to encode

    def fit(self,X,y=None):
        return self # not relevant here

    def transform(self,X):
        '''
        Transforms columns of X specified in self.columns using
        LabelEncoder(). If no columns specified, transforms all
        columns in X.
        '''
        output = X.copy()
        if self.columns is not None:
            for col in self.columns:
                output[col] = LabelEncoder().fit_transform(output[col])
        else:
            for colname,col in output.iteritems():
                output[colname] = LabelEncoder().fit_transform(col)
        return output

    def fit_transform(self,X,y=None):
        return self.fit(X,y).transform(X)

MultiColumnLabelEncoder(columns = [0, 1, 2, 3, 4, 5]).fit_transform(df)

或者

df.apply(LabelEncoder().fit_transform)

    0   1   2   3   4   5
0   8   8   8   7   5   9
1   3   5   2   2   1   8
2   7   1   9   8   7   1
3   6   7   6   4   9   2
4   9   4   1   0   8   0
5   1   3   3   3   2   5
6   0   6   4   5   4   7
7   4   2   7   1   3   6
8   5   0   0   2   0   4
9   2   9   5   6   6   3

# Create some toy data in a Pandas dataframe
fruit_data = pd.DataFrame({
    'fruit':  ['apple','orange','pear','orange'],
    'color':  ['red','orange','green','green'],
    'weight': [5,6,3,4]
})

    color   fruit   weight
0   red     apple   5
1   orange  orange  6
2   green   pear    3
3   green   orange  4

MultiColumnLabelEncoder(columns = ['fruit','color']).fit_transform(fruit_data)

或者

fruit_data[['fruit','color']]=fruit_data[['fruit','color']].apply(LabelEncoder().fit_transform)

    color   fruit   weight
0   2       0       5
1   1       1       6
2   0       2       3
3   0       1       4