我有一个包含多个样本的DataFrame
df1 = [[ 0 1 2 3 4 5 6 7 8 9 10 11 \
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 13.4 5.2 7.7 -2.1 1.6 -4.1 -0.5 8.2 15.9 12.9 11.8 9.3
2 -3.1 -0.6 -5.1 -0.5 -4.1 0.5 -3.6 -5.6 -9.7 -3.6 -4.7 -5.7
3 -10.3 -1.0 -9.8 0.5 -3.6 1.0 -1.5 -1.6 -5.1 -4.6 -13.3 -10.7
4 0.0 -5.6 -4.1 1.5 3.0 -1.0 2.6 6.7 12.3 6.6 -0.5 1.0
5 6.2 0.5 2.6 1.1 1.6 0.5 2.0 0.0 -0.5 0.5 7.7 5.6
6 -1.6 5.1 6.1 -1.1 -2.1 0.0 -1.5 -9.2 -13.9 -7.1 1.5 -0.5
7 -6.1 -4.1 -1.5 -1.5 -0.5 -0.5 -0.5 -2.6 2.6 -2.6 -6.6 -3.1
8 -0.5 -4.1 -6.7 1.5 0.0 0.5 1.0 8.2 8.7 4.1 -3.1 0.6
9 5.1 7.2 4.6 0.6 1.0 0.0 0.5 -0.5 -7.7 -0.5 5.6 0.5
10 0.5 2.6 5.7 -2.1 2.1 -1.0 0.0 -8.2 -10.2 -6.2 3.6 0.0
11 -5.1 -7.2 -4.6 1.0 -1.0 2.0 -2.0 8.7 14.3 10.3 2.1 8.2
12 0.5 -2.6 -1.6 3.1 -0.5 3.1 4.1 10.8 15.9 9.7 2.5 7.7
13 2.6 2.1 0.0 0.0 -0.6 -0.5 8.7 -8.7 -15.9 -13.3 -3.0 -9.3
14 -2.1 4.6 1.0 -2.6 1.1 0.0 0.0 -6.2 -10.8 -9.7 -1.1 -4.1
15 4.6 5.1 6.2 -0.5 7.7 3.1 -3.6 8.2 19.0 11.7 7.2 12.9
16 1.6 -6.1 -2.6 0.5 5.1 2.0 1.0 0.0 5.7 5.7 2.1 1.0
17 -11.8 -10.8 -10.7 -1.5 -6.2 -1.0 -3.1 -10.7 -23.1 -11.3 -6.7 -12.8
18 -6.2 -0.5 -0.5 0.0 -7.7 -3.6 -7.7 0.0 -3.1 0.0 -4.1 -0.6
19 9.8 3.6 4.1 1.5 -2.0 -4.6 -1.0 11.2 25.1 14.9 1.5 8.8
20 7.7 3.0 2.0 0.0 3.6 2.5 3.1 1.6 3.1 2.0 6.2 2.5
21 -1.1 3.1 3.1 -0.5 7.2 7.2 2.0 -11.3 -26.7 -12.8 3.1 -2.5
22 0.5 -1.0 0.0 0.5 3.0 0.5 -2.0 -0.5 -2.0 -1.0 -3.1 1.0
23 1.1 -2.1 -2.6 -1.5 -0.5 -0.5 -2.6 9.2 23.1 6.6 -1.0 1.0
24 -2.1 3.6 2.1 -1.0 1.1 3.6 1.6 -3.6 -9.3 -5.1 2.0 -2.0
25 1.6 4.6 5.1 2.0 -4.7 -2.6 1.0 -2.0 -11.8 -2.0 2.1 3.0
26 0.5 -1.5 -2.6 1.1 -7.7 -9.2 0.5 6.1 15.4 5.6 -2.1 0.0
27 -6.2 -11.3 -11.8 -0.6 -1.5 2.1 5.1 -3.6 -2.0 -4.6 -6.7 -11.2
28 -1.0 -4.1 -1.0 0.6 1.0 8.7 7.7 -4.1 -10.3 -2.1 0.0 -1.1
29 8.2 10.3 10.3 0.0 -0.5 0.5 3.6 3.1 6.2 4.7 6.2 10.3
.. ... ... ... ... ... ... ... ... ... ... ... ...
98 -1.0 -4.1 -1.0 1.1 0.5 -3.1 -6.7 1.5 10.3 2.5 -1.0 -6.1
99 5.6 9.8 5.1 -1.6 1.6 -1.0 4.7 11.8 18.4 22.6 11.8 13.3
100 -1.0 2.0 1.1 0.5 -1.1 1.5 11.2 -5.6 -4.1 8.2 4.1 9.3
101 -4.6 -9.2 -5.7 2.6 -1.0 1.6 -0.5 -10.8 -14.3 -16.4 -8.2 -14.4
102 1.5 0.0 2.6 -1.5 0.5 -0.5 -10.7 4.6 -2.1 -13.3 -0.5 -3.1
103 -2.5 -2.6 1.5 -1.6 -4.1 -2.6 -5.2 2.6 2.6 -2.6 -3.1 2.6
104 -7.7 -6.7 -7.2 1.6 -6.1 -4.1 1.1 -5.6 -2.1 -2.1 -10.3 -12.8
105 2.6 7.2 -1.0 0.0 2.0 -9.2 0.0 4.1 1.6 4.1 -1.0 -3.1
106 5.6 7.2 7.2 -2.6 5.6 -1.1 -2.1 4.6 1.5 7.2 4.1 9.7
107 -0.5 -5.6 1.0 3.1 1.6 8.8 1.0 -4.6 -1.5 -7.7 -2.6 -6.6
108 -4.6 -3.6 -8.7 1.5 -2.6 0.0 2.6 0.5 5.1 -4.6 -2.5 -7.7
109 -3.6 1.5 -5.6 -5.6 -7.7 -10.8 -7.7 2.5 -0.5 5.1 0.5 4.1
110 0.0 2.1 4.1 0.0 -1.5 -1.5 -8.7 -7.7 -11.3 -8.2 0.5 0.5
111 7.2 6.6 9.7 4.6 9.7 10.2 8.7 -4.1 -7.2 -7.7 3.6 2.5
112 5.1 2.6 2.1 -1.6 5.2 5.1 5.6 1.6 3.6 7.7 1.0 8.8
113 -7.7 -5.1 2.5 -2.5 -8.2 -8.7 -11.8 5.6 16.9 11.3 -1.0 -1.1
114 -7.2 1.5 10.8 2.0 -6.2 -8.2 -1.0 4.6 9.8 10.3 6.1 2.6
115 -2.6 -1.5 -2.1 0.5 5.1 5.7 12.8 -9.2 -22.1 -10.3 1.1 2.6
116 -2.0 -8.2 -9.7 -0.5 3.1 9.2 3.1 -9.2 -16.9 -24.6 -11.8 -11.8
117 0.5 1.5 5.6 0.0 -5.1 -1.5 -3.6 13.3 24.1 6.2 -1.6 -3.1
118 1.5 7.2 6.7 -0.5 1.0 -2.1 2.1 8.7 10.3 18.4 9.3 10.8
119 8.3 6.1 3.1 1.0 8.2 4.1 -0.5 -16.4 -22.6 -6.6 1.5 2.0
120 9.2 5.7 5.6 1.6 2.6 1.0 -10.3 -5.6 2.6 -2.1 2.1 0.0
121 -2.1 -4.1 -1.5 -2.6 -2.1 -3.6 -8.2 13.3 22.5 9.7 7.1 4.1
122 -5.6 -2.6 -2.6 -2.0 -1.0 1.1 6.2 4.6 -2.5 -0.5 -1.0 -0.5
123 4.1 7.2 6.7 2.5 -1.5 2.0 11.2 -7.2 -14.4 -6.1 -1.0 -1.0
124 3.1 1.5 4.6 -1.0 0.0 -1.0 -2.5 -3.5 -2.0 0.0 1.5 1.0
125 -2.1 -2.0 -3.6 -2.1 1.5 1.0 -6.7 -1.1 1.5 0.0 -5.1 -5.1
126 1.6 3.0 -0.5 1.1 0.0 -1.5 1.0 1.6 4.6 0.5 -4.6 0.0
127 -2.1 -1.0 -2.6 1.0 0.5 -6.2 4.1 7.1 12.3 3.6 2.0 3.6
12 13
0 NaN NaN
1 12.8 14.9
2 -3.0 -7.7
3 -5.7 -10.8
4 2.6 1.5
5 4.1 4.1
6 -2.1 1.1
7 -7.7 -2.6
8 1.6 -3.6
9 4.1 0.5
10 -5.7 3.1
11 2.1 2.6
12 8.2 3.6
13 -2.1 -1.6
14 -2.5 -2.0
15 6.1 5.6
16 0.5 0.0
17 -5.1 -11.3
18 -2.0 -6.1
19 1.5 8.7
20 2.1 7.2
21 -0.6 -0.5
22 2.1 3.0
23 2.0 2.6
24 -5.1 -3.6
25 2.1 2.6
26 2.5 3.6
27 -13.8 -12.3
28 -3.6 -4.1
29 11.3 11.2
.. ... ...
98 6.2 1.0
99 12.3 9.7
100 -2.1 2.1
101 -7.2 -6.2
102 3.6 -1.5
103 -4.1 -3.1
104 -12.8 -7.2
105 3.6 0.6
106 13.8 4.6
107 -2.5 -3.1
108 -11.3 -7.2
109 -2.6 -3.1
110 4.1 1.1
111 7.2 4.6
112 4.1 4.1
113 -4.1 -2.1
114 3.6 3.1
115 0.5 -4.1
116 -11.3 -12.3
117 0.0 3.1
118 9.3 11.8
119 0.5 3.6
120 -1.1 1.5
121 2.6 -0.5
122 -2.6 -2.6
123 -1.5 4.1
124 2.1 1.0
125 -1.1 -1.0
126 0.5 5.7
127 0.6 -2.6
[128 rows x 14 columns x 60 samples]]
我有大约60个，然后我有另一个大小为df2 = (1x14)
我想要做的是，检查df2中的行的值是否等于或大于df1中的相应行，这样它应该给我1, 0 or -1的值对于该行中的每个元素。
应该看起来像这样
0 1 0 0 0 0 -1 0 -0....
-1 0 1 1 0 -1 0 0 ....
.
.
任何人都可以帮我这个吗？
答案 0 :(得分：2)
好的，我认为以下内容应该有效：
In [208]:
# load some data
t="""1 13.4 5.2 7.7 -2.1 1.6 -4.1 -0.5 8.2 15.9 12.9 11.8 9.3
2 -3.1 -0.6 -5.1 -0.5 -4.1 0.5 -3.6 -5.6 -9.7 -3.6 -4.7 -5.7
3 -10.3 -1.0 -9.8 0.5 -3.6 1.0 -1.5 -1.6 -5.1 -4.6 -13.3 -10.7
4 0.0 -5.6 -4.1 1.5 3.0 -1.0 2.6 6.7 12.3 6.6 -0.5 1.0
5 6.2 0.5 2.6 1.1 1.6 0.5 2.0 0.0 -0.5 0.5 7.7 5.6
6 -1.6 5.1 6.1 -1.1 -2.1 0.0 -1.5 -9.2 -13.9 -7.1 1.5 -0.5
7 -6.1 -4.1 -1.5 -1.5 -0.5 -0.5 -0.5 -2.6 2.6 -2.6 -6.6 -3.1
8 -0.5 -4.1 -6.7 1.5 0.0 0.5 1.0 8.2 8.7 4.1 -3.1 0.6
9 5.1 7.2 4.6 0.6 1.0 0.0 0.5 -0.5 -7.7 -0.5 5.6 0.5"""
df = pd.read_csv(io.StringIO(t), delim_whitespace=True, header = None, index_col=[0])
df.reset_index(inplace=True, drop=True)
df
Out[208]:
1 2 3 4 5 6 7 8 9 10 11 12
0 13.4 5.2 7.7 -2.1 1.6 -4.1 -0.5 8.2 15.9 12.9 11.8 9.3
1 -3.1 -0.6 -5.1 -0.5 -4.1 0.5 -3.6 -5.6 -9.7 -3.6 -4.7 -5.7
2 -10.3 -1.0 -9.8 0.5 -3.6 1.0 -1.5 -1.6 -5.1 -4.6 -13.3 -10.7
3 0.0 -5.6 -4.1 1.5 3.0 -1.0 2.6 6.7 12.3 6.6 -0.5 1.0
4 6.2 0.5 2.6 1.1 1.6 0.5 2.0 0.0 -0.5 0.5 7.7 5.6
5 -1.6 5.1 6.1 -1.1 -2.1 0.0 -1.5 -9.2 -13.9 -7.1 1.5 -0.5
6 -6.1 -4.1 -1.5 -1.5 -0.5 -0.5 -0.5 -2.6 2.6 -2.6 -6.6 -3.1
7 -0.5 -4.1 -6.7 1.5 0.0 0.5 1.0 8.2 8.7 4.1 -3.1 0.6
8 5.1 7.2 4.6 0.6 1.0 0.0 0.5 -0.5 -7.7 -0.5 5.6 0.5
现在使用嵌套np.where来屏蔽df，使用gt和lt分别设置1和-1 0 ;如果两个条件都不满足，则相等：
In [213]:
df1 = pd.DataFrame(np.arange(12)).astype(float)
df = pd.DataFrame(np.where(df.gt(df1.squeeze(), axis=0), 1, np.where(df.lt(df1.squeeze(), axis=0), -1, 0)))
df
Out[213]:
0 1 2 3 4 5 6 7 8 9 10 11
0 1 1 1 -1 1 -1 -1 1 1 1 1 1
1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
3 -1 -1 -1 -1 0 -1 -1 1 1 1 -1 -1
4 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1
5 -1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1
6 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
7 -1 -1 -1 -1 -1 -1 -1 1 1 -1 -1 -1
8 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
9 0 0 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0 0 0
只要索引和列标签匹配
，上述内容就可以正常工作答案 1 :(得分：2)
import pandas as pd
import numpy as np
np.random.seed([3,1415])
df1 = pd.DataFrame(np.random.choice(range(10), (20, 10)))
df2 = pd.Series(np.random.choice(range(10), (10,)))
尝试：
1 * (df1 > df2) - (df1 < df2)
0 1 2 3 4 5 6 7 8 9
0 -1 1 1 -1 -1 0 -1 -1 1 -1
1 -1 1 -1 -1 0 0 -1 -1 -1 -1
2 -1 1 1 -1 -1 -1 -1 -1 -1 1
3 0 1 1 -1 -1 -1 -1 -1 0 -1
4 -1 1 1 -1 -1 -1 1 -1 -1 -1
5 -1 1 1 -1 -1 -1 1 -1 -1 -1
6 -1 1 1 -1 0 1 -1 -1 -1 -1
7 -1 1 1 0 -1 -1 1 -1 -1 -1
8 -1 1 1 -1 -1 -1 -1 -1 -1 1
9 -1 1 1 -1 -1 -1 0 -1 1 1
10 -1 1 1 -1 -1 -1 -1 -1 -1 1
11 -1 1 1 -1 -1 -1 1 -1 1 0
12 -1 1 0 -1 -1 -1 -1 0 1 -1
13 1 1 1 -1 -1 1 -1 -1 -1 -1
14 -1 1 -1 -1 -1 0 1 -1 1 -1
15 -1 1 1 -1 -1 1 0 -1 -1 -1
16 -1 0 1 1 -1 -1 1 -1 1 0
17 -1 1 1 -1 -1 1 -1 -1 -1 1
18 -1 1 1 -1 -1 -1 -1 -1 1 1
19 -1 1 1 -1 -1 -1 -1 0 -1 -1