pandas 的独热功能实现

最新推荐文章于 2023-02-26 23:15:33 发布

原创最新推荐文章于 2023-02-26 23:15:33 发布 · 343 阅读

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#python #pandas

本文介绍了如何使用Pandas读取鸢尾花数据集，进行特征提取和标签处理，特别展示了如何将类别标签转换为独热编码，以便于3分类问题的分析。

鸢尾花数据集

# 获取数据
data = pd.read_csv('iris.txt',delimiter=',',header=None)
# header = None 添加表格题头
# 获取特征和标签
x=data.iloc[:,:-1]
y=data.iloc[:,-1]  
print(y)

       0    1    2    3               4
0    5.1  3.5  1.4  0.2     Iris-setosa
1    4.9  3.0  1.4  0.2     Iris-setosa
2    4.7  3.2  1.3  0.2     Iris-setosa
3    4.6  3.1  1.5  0.2     Iris-setosa
4    5.0  3.6  1.4  0.2     Iris-setosa
5    5.4  3.9  1.7  0.4     Iris-setosa
6    4.6  3.4  1.4  0.3     Iris-setosa
7    5.0  3.4  1.5  0.2     Iris-setosa
8    4.4  2.9  1.4  0.2     Iris-setosa
9    4.9  3.1  1.5  0.1     Iris-setosa
10   5.4  3.7  1.5  0.2     Iris-setosa
11   4.8  3.4  1.6  0.2     Iris-setosa
12   4.8  3.0  1.4  0.1     Iris-setosa
13   4.3  3.0  1.1  0.1     Iris-setosa
14   5.8  4.0  1.2  0.2     Iris-setosa
15   5.7  4.4  1.5  0.4     Iris-setosa
16   5.4  3.9  1.3  0.4     Iris-setosa
17   5.1  3.5  1.4  0.3     Iris-setosa
18   5.7  3.8  1.7  0.3     Iris-setosa
19   5.1  3.8  1.5  0.3     Iris-setosa
20   5.4  3.4  1.7  0.2     Iris-setosa
21   5.1  3.7  1.5  0.4     Iris-setosa
22   4.6  3.6  1.0  0.2     Iris-setosa
23   5.1  3.3  1.7  0.5     Iris-setosa
24   4.8  3.4  1.9  0.2     Iris-setosa
25   5.0  3.0  1.6  0.2     Iris-setosa
26   5.0  3.4  1.6  0.4     Iris-setosa
27   5.2  3.5  1.5  0.2     Iris-setosa
28   5.2  3.4  1.4  0.2     Iris-setosa
29   4.7  3.2  1.6  0.2     Iris-setosa
..   ...  ...  ...  ...             ...
120  6.9  3.2  5.7  2.3  Iris-virginica
121  5.6  2.8  4.9  2.0  Iris-virginica
122  7.7  2.8  6.7  2.0  Iris-virginica
123  6.3  2.7  4.9  1.8  Iris-virginica
124  6.7  3.3  5.7  2.1  Iris-virginica
125  7.2  3.2  6.0  1.8  Iris-virginica
126  6.2  2.8  4.8  1.8  Iris-virginica
127  6.1  3.0  4.9  1.8  Iris-virginica
128  6.4  2.8  5.6  2.1  Iris-virginica
129  7.2  3.0  5.8  1.6  Iris-virginica
130  7.4  2.8  6.1  1.9  Iris-virginica
131  7.9  3.8  6.4  2.0  Iris-virginica
132  6.4  2.8  5.6  2.2  Iris-virginica
133  6.3  2.8  5.1  1.5  Iris-virginica
134  6.1  2.6  5.6  1.4  Iris-virginica
135  7.7  3.0  6.1  2.3  Iris-virginica
136  6.3  3.4  5.6  2.4  Iris-virginica
137  6.4  3.1  5.5  1.8  Iris-virginica
138  6.0  3.0  4.8  1.8  Iris-virginica
139  6.9  3.1  5.4  2.1  Iris-virginica
140  6.7  3.1  5.6  2.4  Iris-virginica
141  6.9  3.1  5.1  2.3  Iris-virginica
142  5.8  2.7  5.1  1.9  Iris-virginica
143  6.8  3.2  5.9  2.3  Iris-virginica
144  6.7  3.3  5.7  2.5  Iris-virginica
145  6.7  3.0  5.2  2.3  Iris-virginica
146  6.3  2.5  5.0  1.9  Iris-virginica
147  6.5  3.0  5.2  2.0  Iris-virginica
148  6.2  3.4  5.4  2.3  Iris-virginica
149  5.9  3.0  5.1  1.8  Iris-virginica

经典的鸢尾花数据集是3分类

利用pandas做独热处理

# y 的维度很重要  y=data.iloc[:,-1]    y 输出是  (150,) 
# 在此基础上做独热
y=pd.get_dummies(y)
print(y)   
     Iris-setosa  Iris-versicolor  Iris-virginica
0              1                0               0
1              1                0               0
2              1                0               0
3              1                0               0
4              1                0               0
5              1                0               0
6              1                0               0
7              1                0               0
8              1                0               0
9              1                0               0
10             1                0               0
11             1                0               0
12             1                0               0
13             1                0               0
14             1                0               0
15             1                0               0
16             1                0               0
17             1                0               0
18             1                0               0
19             1                0               0
20             1                0               0
21             1                0               0
22             1                0               0
23             1                0               0
24             1                0               0
25             1                0               0
26             1                0               0
27             1                0               0
28             1                0               0
29             1                0               0
..           ...              ...             ...
120            0                0               1
121            0                0               1
122            0                0               1
123            0                0               1
124            0                0               1
125            0                0               1
126            0                0               1
127            0                0               1
128            0                0               1
129            0                0               1
130            0                0               1
131            0                0               1
132            0                0               1
133            0                0               1
134            0                0               1
135            0                0               1
136            0                0               1
137            0                0               1
138            0                0               1
139            0                0               1
140            0                0               1
141            0                0               1
142            0                0               1
143            0                0               1
144            0                0               1
145            0                0               1
146            0                0               1
147            0                0               1
148            0                0               1
149            0                0               1

[150 rows x 3 columns]

以上做一个记录和分享

2021-5-21