python数据处理之OneHot

最新推荐文章于 2025-07-05 23:28:23 发布

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#python #sklearn #onehot

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本文详细介绍了一种常见的机器学习预处理技术——OneHot编码，并通过Python的pandas和sklearn库展示了如何实现OneHot编码，包括自动和手动编码过程。文章首先使用pandas库的get_dummies函数进行OneHot变换，随后通过sklearn的LabelEncoder和OneHotEncoder实现了手动编码，最后展示了如何将编码后的数据逆转换为原始类别。

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1.OneHot变换

import pandas as pd
 
df1=pd.DataFrame({'key':['a','b','c'],'data1':[1,2,3]})  
print(df1)
df2=pd.get_dummies(df1['key'])
print(df2)
df3=pd.get_dummies(df1)
print(df3)

2.OneHot编码

手动实现

# 手动实现onehot
from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# define example
data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']
values = array(data)
print(values)
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
print(integer_encoded)
# binary encode
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded)
# invert first example
inverted = label_encoder.inverse_transform([argmax(onehot_encoded[0, :])])
print(inverted)

基于sklearn

from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# define example
data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']
values = array(data)
print(values)
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
print(integer_encoded)
# binary encode
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded)
# invert first example
inverted = label_encoder.inverse_transform([argmax(onehot_encoded[0, :])])
print(inverted)