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：
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>>> 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])
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>>> 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']
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将DataFrame中的所有ID标签转换成连续编号：
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from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
df=pd.read_csv('testdata.csv',sep='|',header=None)
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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
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le = LabelEncoder()
le.fit(np.unique(df.values))
df.apply(le.transform)
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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
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将DataFrame中的每一行ID标签分别转换成连续编号：
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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)
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MultiColumnLabelEncoder(columns = [0, 1, 2, 3, 4, 5]).fit_transform(df)
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或者
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df.apply(LabelEncoder().fit_transform)
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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
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# 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]
})
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color fruit weight
0 red apple 5
1 orange orange 6
2 green pear 3
3 green orange 4
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MultiColumnLabelEncoder(columns = ['fruit','color']).fit_transform(fruit_data)
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或者
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fruit_data[['fruit','color']]=fruit_data[['fruit','color']].apply(LabelEncoder().fit_transform)
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color fruit weight
0 2 0 5
1 1 1 6
2 0 2 3
3 0 1 4
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以上这篇使用sklearn之LabelEncoder将Label标准化的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/u010412858/article/details/78386407