python - 在python中将分类变量转换为定量变量
问题描述
我正在尝试将分类变量更改为定量变量。我正在使用get_dummies应该返回定量变量的函数。
我的想法是在我的数据框中创建新列并将返回的定量变量添加到这些新列中，但是当我将其打印出来时，输出显示的是其他内容。
我的代码：
import pandas as pd
import numpy as np
df = pd.read_csv('/home/user/Documents/MOOC dataset cleaned/duplicate.csv')
df['0_to_35'],df['35_to_55'],df['greater then 55'] = pd.get_dummies(df['age_band'])
print(df['0_to_35'],df['35_to_55'],df['greater then 55'])
输出：
(0 0-35
1 0-35
2 0-35
3 0-35
4 0-35
5 0-35
6 0-35
7 0-35
8 0-35
9 0-35
10 0-35
11 0-35
12 0-35
13 0-35
14 0-35
15 0-35
16 0-35
17 0-35
18 0-35
19 0-35
20 0-35
21 0-35
22 0-35
23 0-35
24 0-35
25 0-35
26 0-35
27 0-35
28 0-35
29 0-35
...
28755 0-35
28756 0-35
28757 0-35
28758 0-35
28759 0-35
28760 0-35
28761 0-35
28762 0-35
28763 0-35
28764 0-35
28765 0-35
28766 0-35
28767 0-35
28768 0-35
28769 0-35
28770 0-35
28771 0-35
28772 0-35
28773 0-35
28774 0-35
28775 0-35
28776 0-35
28777 0-35
28778 0-35
28779 0-35
28780 0-35
28781 0-35
28782 0-35
28783 0-35
28784 0-35
Name: 0_to_35, dtype: object, 0 35-55
1 35-55
2 35-55
3 35-55
4 35-55
5 35-55
6 35-55 (0 0-35
1 0-35
2 0-35
3 0-35
4 0-35
5 0-35
6 0-35
7 0-35
8 0-35
9 0-35
10 0-35
11 0-35
12 0-35
13 0-35
14 0-35
15 0-35
16 0-35
17 0-35
18 0-35
19 0-35
20 0-35
21 0-35
22 0-35
23 0-35
24 0-35
25 0-35
26 0-35
27 0-35
28 0-35
29 0-35
...
28755 0-35
28756 0-35
28757 0-35
28758 0-35
28759 0-35
28760 0-35
28761 0-35
28762 0-35
28763 0-35
28764 0-35
28765 0-35
28766 0-35
28767 0-35
28768 0-35
28769 0-35
28770 0-35
28771 0-35
28772 0-35
28773 0-35
28774 0-35
28775 0-35
28776 0-35
28777 0-35
28778 0-35
28779 0-35
28780 0-35
28781 0-35
28782 0-35
28783 0-35
28784 0-35
Name: 0_to_35, dtype: object, 0 35-55
1 35-55
2 35-55
3 35-55
4 35-55
5 35-55
6 35-55
7 35-55
8 35-55
9 35-55
10 35-55
11 35-55
12 35-55
13 35-55
14 35-55
15 35-55
16 35-55
17 35-55
18 35-55
19 35-55
20 35-55
21 35-55
22 35-55
23 35-55
24 35-55
25 35-55
26 35-55
27 35-55
28 35-55
29 35-55
...
28755 35-55
28756 35-55
28757 35-55
28758 35-55
28759 35-55
28760 35-55
28761 35-55
28762 35-55
28763 35-55
28764 35-55
28765 35-55
28766 35-55
28767 35-55
28768 35-55
28769 35-55
28770 35-55
28771 35-55
28772 35-55
28773 35-55
28774 35-55
28775 35-55
28776 35-55
28777 35-55
28778 35-55
28779 35-55
28780 35-55
28781 35-55
28782 35-55
28783 35-55
28784 35-55
Name: 35_to_55, dtype: object, 0 55<=
1 55<=
2 55<=
3 55<=
4 55<=
5 55<=
6 55<=
7 55<=
8 55<=
9 55<=
10 55<=
11 55<=
12 55<=
13 55<=
14 55<=
15 55<=
16 55<=
17 55<=
18 55<=
19 55<=
20 55<=
21 55<=
22 55<=
23 55<=
24 55<=
25 55<=
26 55<=
27 55<=
28 55<=
29 55<=
...
28755 55<=
28756 55<=
28757 55<=
28758 55<=
28759 55<=
28760 55<=
28761 55<=
28762 55<=
28763 55<=
28764 55<=
28765 55<=
28766 55<=
28767 55<=
28768 55<=
28769 55<=
28770 55<=
28771 55<=
28772 55<=
28773 55<=
28774 55<=
28775 55<=
28776 55<=
28777 55<=
28778 55<=
28779 55<=
28780 55<=
28781 55<=
28782 55<=
28783 55<=
28784 55<=
Name: greater then 55, dtype: object)
7 35-55
8 35-55
9 35-55
10 35-55
11 35-55
12 35-55
13 35-55
14 35-55
15 35-55
16 35-55
17 35-55
18 35-55
19 35-55
20 35-55
21 35-55
22 35-55
23 35-55
24 35-55
25 35-55
26 35-55
27 35-55
28 35-55
29 35-55
...
28755 35-55
28756 35-55
28757 35-55
28758 35-55
28759 35-55
28760 35-55
28761 35-55
28762 35-55
28763 35-55
28764 35-55
28765 35-55
28766 35-55
28767 35-55
28768 35-55
28769 35-55
28770 35-55
28771 35-55
28772 35-55
28773 35-55
28774 35-55
28775 35-55
28776 35-55
28777 35-55
28778 35-55
28779 35-55
28780 35-55
28781 35-55
28782 35-55
28783 35-55
28784 35-55
Name: 35_to_55, dtype: object, 0 55<=
1 55<=
2 55<=
3 55<=
4 55<=
5 55<=
6 55<=
7 55<=
8 55<=
9 55<=
10 55<=
11 55<=
12 55<=
13 55<=
14 55<=
15 55<=
16 55<=
17 55<=
18 55<=
19 55<=
20 55<=
21 55<=
22 55<=
23 55<=
24 55<=
25 55<=
26 55<=
27 55<=
28 55<=
29 55<=
...
28755 55<=
28756 55<=
28757 55<=
28758 55<=
28759 55<=
28760 55<=
28761 55<=
28762 55<=
28763 55<=
28764 55<=
28765 55<=
28766 55<=
28767 55<=
28768 55<=
28769 55<=
28770 55<=
28771 55<=
28772 55<=
28773 55<=
28774 55<=
28775 55<=
28776 55<=
28777 55<=
28778 55<=
28779 55<=
28780 55<=
28781 55<=
28782 55<=
28783 55<=pd.get_dummies(df['age_band'])
28784 55<=
Name: greater then 55, dtype: object)
pd.get_dummies(df['age_band']) 的输出 -
0-35 35-55 55<=
0 0 0 1
1 0 1 0
2 0 1 0
3 0 1 0
4 1 0 0
5 0 1 0
6 1 0 0
7 1 0 0
8 1 0 0
9 0 0 1
10 0 1 0
11 1 0 0
12 0 1 0
13 1 0 0
14 0 1 0
15 1 0 0
16 0 1 0
17 0 1 0
18 0 1 0
19 0 1 0
20 1 0 0
21 1 0 0
22 0 1 0
23 0 1 0
24 1 0 0
25 0 1 0
26 1 0 0
27 1 0 0
28 0 1 0
29 0 1 0
... ... ... ...
28755 0 1 0
28756 0 1 0
28757 1 0 0
28758 0 1 0
28759 0 1 0
28760 0 1 0
28761 0 1 0
28762 0 1 0
28763 0 1 0
28764 0 1 0
28765 0 1 0
28766 0 1 0
28767 0 1 0
28768 0 1 0
28769 1 0 0
28770 0 1 0
28771 0 1 0
28772 0 1 0
28773 1 0 0
28774 0 1 0
28775 1 0 0
28776 1 0 0
28777 1 0 0
28778 0 1 0
28779 1 0 0
28780 1 0 0
28781 0 1 0
28782 1 0 0
28783 0 1 0
28784 0 1 0
[28785 rows x 3 columns]
[Finished in 0.216s]
我不明白为什么会这样。它应该将以上三个变量放在新列中。我怎样才能解决这个问题？
解决方案
我认为需要分配给新列名称的子集：
df[['0_to_35', '35_to_55', 'greater then 55']] = pd.get_dummies(df['age_band'])
或分配给新的 DataFrame 和join：
df1 = pd.get_dummies(df['age_band'])
#set new columns names if necessary
df1.columns = ['0_to_35','35_to_55','greater then 55']
df = df.join(df1)
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