user7852656
Reputation: 735
Python: getting dot product for two multidimensional arrays
I am realizing that numpy.dot does not process multidimensional matrices. My data looks like the following. I want to subject all the columns (42 in total) to calculate the dot product except the first column.
Here is how my data look like (data simplified) Data 1:
0 4 6
-0.276 4403 4403
-0.138 4640 4640
0 0 0
0.138 12 0
0.276 0 0
0.414 0 0
0.552 0 0
0.69 0 0
0.828 0 12
0.966 0 0
1.104 0 12
1.242 0 0
1.38 0 0
1.518 0 0
1.656 0 0
1.794 0 0
1.932 0 0
2.07 0 0
2.208 0 0
2.346 0 0
2.484 0 0
2.622 0 12
2.76 0 0
2.898 0 0
3.036 0 0
3.174 0 0
3.312 0 0
3.45 0 0
3.588 0 0
3.726 0 0
3.864 12 0
4.002 0 0
4.14 0 0
4.278 12 0
4.416 0 0
4.554 0 12
4.692 0 0
4.83 0 0
4.968 0 0
5.106 0 0
5.244 0 0
5.382 12 0
5.52 0 0
5.658 0 0
5.796 127 60
5.934 357 275
6.072 1882 2144
6.21 6726 6609
6.348 9398 11180
6.486 12784 18389
6.624 15863 20111
6.762 6739 10202
6.9 1684 1921
7.038 249 376
7.176 47 103
7.314 0 26
7.452 17 0
7.59 0 0
7.728 0 0
7.866 0 0
8.004 0 0
8.142 0 0
8.28 0 0
8.418 0 0
8.556 0 0
8.694 0 0
8.832 0 0
8.97 0 0
9.108 0 0
9.246 0 0
9.384 0 0
9.522 0 0
9.66 0 0
9.798 0 0
9.936 0 0
10.074 0 0
10.212 0 0
10.35 0 12
10.488 0 0
10.626 0 0
10.764 0 0
10.902 0 0
11.04 0 0
11.178 0 0
11.316 0 0
11.454 0 0
11.592 0 0
11.73 0 0
11.868 0 0
12.006 0 0
12.144 0 0
12.282 0 0
12.42 0 0
12.558 0 0
12.696 12 0
12.834 0 0
12.972 0 0
13.11 0 0
13.248 0 0
13.386 12 0
13.524 0 0
13.662 0 12
13.8 0 0
13.938 0 0
14.076 0 0
14.214 0 0
14.352 0 0
14.49 0 0
14.628 12 0
14.766 0 0
14.904 12 0
15.042 0 0
15.18 0 0
15.318 0 0
15.456 0 0
15.594 0 0
15.732 0 0
15.87 0 0
16.008 0 0
16.146 0 0
16.284 0 0
16.422 0 0
16.56 12 0
16.698 0 0
16.836 0 0
16.974 0 0
17.112 0 0
17.25 0 0
17.388 0 0
17.526 0 0
17.664 0 12
17.802 0 0
17.94 0 0
18.078 0 0
18.216 0 0
18.354 0 0
18.492 0 0
18.63 12 0
18.768 0 0
18.906 0 0
19.044 0 0
19.182 0 0
19.32 0 0
19.458 0 0
19.596 0 0
19.734 0 0
19.872 0 0
20.01 0 0
20.148 0 12
20.286 12 0
20.424 0 12
20.562 0 0
20.7 0 0
20.838 0 0
20.976 0 0
21.114 0 0
21.252 0 0
21.39 0 12
21.528 0 0
21.666 0 0
21.804 12 0
21.942 0 0
22.08 0 0
22.218 0 0
22.356 0 0
22.494 0 0
22.632 0 0
22.77 0 0
22.908 0 0
23.046 0 0
23.184 0 0
23.322 0 0
23.46 12 0
23.598 0 12
23.736 0 0
23.874 0 0
24.012 0 0
24.15 0 0
24.288 0 0
24.426 0 0
24.564 0 0
24.702 0 0
24.84 0 0
24.978 0 0
25.116 0 0
25.254 0 0
25.392 0 0
25.53 0 0
25.668 0 0
25.806 12 0
25.944 12 0
26.082 0 0
26.22 0 0
26.358 0 12
26.496 0 0
26.634 0 0
26.772 0 0
26.91 0 0
27.048 13 0
27.186 0 0
27.324 0 0
27.462 0 0
Data 2:
0 4 6
-0.276 4400 4400
-0.138 4750 4750
0 0 0
0.138 12 0
0.276 0 0
0.414 0 12
0.552 0 0
0.69 0 25
0.828 0 0
0.966 12 13
1.104 0 0
1.242 0 12
1.38 0 0
1.518 12 0
1.656 0 0
1.794 0 12
1.932 0 0
2.07 12 0
2.208 0 0
2.346 0 0
2.484 12 0
2.622 0 0
2.76 24 0
2.898 0 0
3.036 0 0
3.174 12 0
3.312 0 0
3.45 0 0
3.588 0 12
3.726 39 0
3.864 0 12
4.002 0 0
4.14 0 12
4.278 0 0
4.416 0 0
4.554 0 0
4.692 0 0
4.83 0 0
4.968 0 0
5.106 0 0
5.244 0 0
5.382 0 0
5.52 0 12
5.658 0 0
5.796 0 0
5.934 0 0
6.072 43 46
6.21 6711 11323
6.348 91043 116679
6.486 241572 307822
6.624 250588 309749
6.762 105123 139651
6.9 16143 21264
7.038 2521 3648
7.176 1042 1022
7.314 576 910
7.452 482 552
7.59 229 416
7.728 210 227
7.866 120 149
8.004 69 55
8.142 47 0
8.28 26 65
8.418 0 20
8.556 0 0
8.694 0 0
8.832 0 12
8.97 12 38
9.108 0 0
9.246 18 0
9.384 0 0
9.522 0 13
9.66 0 0
9.798 0 18
9.936 16 0
10.074 12 0
10.212 0 0
10.35 12 0
10.488 0 0
10.626 0 23
10.764 0 0
10.902 0 0
11.04 20 0
11.178 0 0
11.316 0 0
11.454 0 0
11.592 0 0
11.73 0 12
11.868 14 12
12.006 0 0
12.144 0 0
12.282 0 0
12.42 0 0
12.558 0 12
12.696 0 0
12.834 0 0
12.972 12 0
13.11 0 0
13.248 0 0
13.386 0 18
13.524 0 0
13.662 12 0
13.8 12 0
13.938 13 0
14.076 0 0
14.214 0 0
14.352 0 0
14.49 0 0
14.628 24 0
14.766 0 15
14.904 0 16
15.042 0 12
15.18 12 0
15.318 0 12
15.456 0 0
15.594 0 0
15.732 14 13
15.87 0 23
16.008 0 0
16.146 0 0
16.284 0 16
16.422 0 12
16.56 0 0
16.698 0 0
16.836 0 0
16.974 0 13
17.112 0 0
17.25 0 0
17.388 16 0
17.526 0 12
17.664 0 0
17.802 0 0
17.94 0 12
18.078 0 0
18.216 0 0
18.354 0 19
18.492 0 0
18.63 0 0
18.768 0 12
18.906 0 0
19.044 0 12
19.182 0 12
19.32 0 0
19.458 0 0
19.596 12 24
19.734 0 0
19.872 0 0
20.01 0 0
20.148 0 0
20.286 0 0
20.424 0 12
20.562 12 0
20.7 0 0
20.838 0 0
20.976 0 0
21.114 0 0
21.252 0 0
21.39 0 12
21.528 12 12
21.666 0 0
21.804 12 0
21.942 0 0
22.08 0 0
22.218 0 0
22.356 0 12
22.494 0 0
22.632 12 0
22.77 0 0
22.908 0 0
23.046 12 0
23.184 0 0
23.322 12 0
23.46 0 0
23.598 13 16
23.736 24 17
23.874 0 0
24.012 12 0
24.15 0 0
24.288 0 0
24.426 12 0
24.564 0 0
24.702 0 0
24.84 0 0
24.978 0 0
25.116 0 0
25.254 0 0
25.392 14 12
25.53 25 0
25.668 0 12
25.806 0 0
25.944 0 15
26.082 0 0
26.22 12 0
26.358 0 0
26.496 0 0
26.634 0 0
26.772 27 0
26.91 0 12
27.048 0 22
27.186 0 0
27.324 0 0
27.462 0 0
Then I have the following code
import pandas as pd
import numpy as np
first_y= np.array(firt_df.iloc[:,1:])
second_y= np.array(second_df.iloc[:,1:])
#dot product
dot_product_both=np.dot(first_y, second_y)
Using the code above, I get the following error
shapes (200,42) and (200,42) not aligned: 42 (dim 1) != 200 (dim 0)
I get this error because np.dot cannot process greater than 1D array.
I am thinking maybe creating a numpy function so that it can deconstruct my dataframe and process everything one by one. Before I do that, I wanted to see if any of you has a clever way to go about it..
My desired outcome would be.. (for the second column) 4403*4400+4640*4750+0*0+12*12....
Upvotes: 2
Views: 3922
Answers (1)
rafaelc
Reputation: 59284
IIUC, you want
(x*y).sum(axis=0)
which translates to 4403*4400+4640*4750+0*0+12*12 + ...
Take a look at this reproducible example:
x = np.array([
[1,2,3],
[4,5,6]
])
y = np.array([
[1,10,1],
[2,2,3]
])
Then
>>> (x*y)
array([[ 1, 20, 3],
[ 8, 10, 18]])
And
>>> (x*y).sum(axis=1)
array([24, 36])
>>> (x*y).sum(axis=0)
array([ 9, 30, 21])
Notice that these values are actually only the diagonal of the np.dot product
>>> np.dot(x,y.T)
array([[24, 15],
[60, 36]])
>>> np.dot(x.T,y)
array([[ 9, 18, 13],
[12, 30, 17],
[15, 42, 21]])
Upvotes: 2
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