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 Integersarray([0, 0, 1, 2], dtype=int64)>>> le.inverse_transform([0, 0, 1, 2]) # Transform Integers Into Categoriesarray([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 Integersarray([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 LabelEncoderimport numpy as npimport pandas as pddf=pd.read_csv('testdata.csv',sep='|',header=None) 0 1 2 3 4 50 37 52 55 50 38 541 17 32 20 9 6 482 28 10 56 51 45 163 27 49 41 30 53 194 44 29 8 1 46 135 11 26 21 14 7 336 0 39 22 33 35 437 18 15 47 5 25 348 23 2 4 9 3 319 12 57 36 40 42 24le = LabelEncoder()le.fit(np.unique(df.values))df.apply(le.transform) 0 1 2 3 4 50 37 52 55 50 38 541 17 32 20 9 6 482 28 10 56 51 45 163 27 49 41 30 53 194 44 29 8 1 46 135 11 26 21 14 7 336 0 39 22 33 35 437 18 15 47 5 25 348 23 2 4 9 3 319 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 pdfrom sklearn.preprocessing import LabelEncoderfrom sklearn.pipeline import Pipelineclass 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 50 8 8 8 7 5 91 3 5 2 2 1 82 7 1 9 8 7 13 6 7 6 4 9 24 9 4 1 0 8 05 1 3 3 3 2 56 0 6 4 5 4 77 4 2 7 1 3 68 5 0 0 2 0 49 2 9 5 6 6 3# Create some toy data in a Pandas dataframefruit_data = pd.DataFrame({ 'fruit': ['apple','orange','pear','orange'], 'color': ['red','orange','green','green'], 'weight': [5,6,3,4]}) color fruit weight0 red apple 51 orange orange 62 green pear 33 green orange 4MultiColumnLabelEncoder(columns = ['fruit','color']).fit_transform(fruit_data)

或者

fruit_data[['fruit','color']]=fruit_data[['fruit','color']].apply(LabelEncoder().fit_transform) color fruit weight0 2 0 51 1 1 62 0 2 33 0 1 4

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。