我有
a={0: ['I3925'], 1: ['I3925'], 2: ['I3925'], 3: ['I2355'], 4: ['I2355'], 5: ['I2355'], 6: ['I111'], 7: ['I111'], 8: ['I111'], 9: ['I405'], 10: ['I405'], 11: ['I3878', 'I2864'], 12: ['I3878'], 13: ['I534'], 14: ['I534'], 15: ['I134', 'I2276'], 16: ['I107'], 17: ['I107'], 18: ['I2864']}
包含一个密钥的一个补充I编号。
b = pd.Series(a,\
index = a.keys(),
name = "a")
pd.get_dummies(b.apply(pd.Series))
然后get_dummies无效，因为它创建了一个重复的列1_15来存储与第二个I号码的匹配，而不是将它们堆叠到同一列中。我不明白为什么。
0_I107 0_I111 0_I134 0_I2355 0_I2864 0_I3878 0_I3925 0_I405 0_I534 1_I2276 1_I2864
0 0 0 0 0 0 0 1 0 0 0 0
1 0 0 0 0 0 0 1 0 0 0 0
2 0 0 0 0 0 0 1 0 0 0 0
3 0 0 0 1 0 0 0 0 0 0 0
4 0 0 0 1 0 0 0 0 0 0 0
5 0 0 0 1 0 0 0 0 0 0 0
6 0 1 0 0 0 0 0 0 0 0 0
7 0 1 0 0 0 0 0 0 0 0 0
8 0 1 0 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 1 0 0 0
10 0 0 0 0 0 0 0 1 0 0 0
11 0 0 0 0 0 1 0 0 0 0 1
12 0 0 0 0 0 1 0 0 0 0 0
13 0 0 0 0 0 0 0 0 1 0 0
14 0 0 0 0 0 0 0 0 1 0 0
15 0 0 1 0 0 0 0 0 0 1 0
16 1 0 0 0 0 0 0 0 0 0 0
17 1 0 0 0 0 0 0 0 0 0 0
18 0 0 0 0 1 0 0 0 0 0 0
有人可以解释一下我做错了吗？
答案 0 :(得分：3)
选项1
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
pd.DataFrame(mlb.fit_transform(b), b.index, mlb.classes_)
I107 I111 I134 I2276 I2355 I2864 I3878 I3925 I405 I534
0 0 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 0 1 0 0
2 0 0 0 0 0 0 0 1 0 0
3 0 0 0 0 1 0 0 0 0 0
4 0 0 0 0 1 0 0 0 0 0
5 0 0 0 0 1 0 0 0 0 0
6 0 1 0 0 0 0 0 0 0 0
7 0 1 0 0 0 0 0 0 0 0
8 0 1 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0 1 0
10 0 0 0 0 0 0 0 0 1 0
11 0 0 0 0 0 1 1 0 0 0
12 0 0 0 0 0 0 1 0 0 0
13 0 0 0 0 0 0 0 0 0 1
14 0 0 0 0 0 0 0 0 0 1
15 0 0 1 1 0 0 0 0 0 0
16 1 0 0 0 0 0 0 0 0 0
17 1 0 0 0 0 0 0 0 0 0
18 0 0 0 0 0 1 0 0 0 0
选项2
b.str.join('|').str.get_dummies()
I107 I111 I134 I2276 I2355 I2864 I3878 I3925 I405 I534
0 0 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 0 1 0 0
2 0 0 0 0 0 0 0 1 0 0
3 0 0 0 0 1 0 0 0 0 0
4 0 0 0 0 1 0 0 0 0 0
5 0 0 0 0 1 0 0 0 0 0
6 0 1 0 0 0 0 0 0 0 0
7 0 1 0 0 0 0 0 0 0 0
8 0 1 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0 1 0
10 0 0 0 0 0 0 0 0 1 0
11 0 0 0 0 0 1 1 0 0 0
12 0 0 0 0 0 0 1 0 0 0
13 0 0 0 0 0 0 0 0 0 1
14 0 0 0 0 0 0 0 0 0 1
15 0 0 1 1 0 0 0 0 0 0
16 1 0 0 0 0 0 0 0 0 0
17 1 0 0 0 0 0 0 0 0 0
18 0 0 0 0 0 1 0 0 0 0
答案 1 :(得分：2)
这样的东西？
pd.get_dummies(b.apply(pd.Series).stack()).sum(level=0)
输出：
I107 I111 I134 I2276 I2355 I2864 I3878 I3925 I405 I534
0 0 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 0 1 0 0
2 0 0 0 0 0 0 0 1 0 0
3 0 0 0 0 1 0 0 0 0 0
4 0 0 0 0 1 0 0 0 0 0
5 0 0 0 0 1 0 0 0 0 0
6 0 1 0 0 0 0 0 0 0 0
7 0 1 0 0 0 0 0 0 0 0
8 0 1 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0 1 0
10 0 0 0 0 0 0 0 0 1 0
11 0 0 0 0 0 1 1 0 0 0
12 0 0 0 0 0 0 1 0 0 0
13 0 0 0 0 0 0 0 0 0 1
14 0 0 0 0 0 0 0 0 0 1
15 0 0 1 1 0 0 0 0 0 0
16 1 0 0 0 0 0 0 0 0 0
17 1 0 0 0 0 0 0 0 0 0
18 0 0 0 0 0 1 0 0 0 0