Python: dot product shapes not aligned?

Question

I have two data frames that I want to produce dot products with. I think it is fairly straightforward. But I keep getting the error messages. 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)

Since the second and third columns are the values that I want to process I read the first_y and second_y that way. But I get an error message as the following.

shapes (200,42) and (200,42) not aligned: 42 (dim 1) != 200 (dim 0)

I think my data sets have the same shape. I am not sure what is causing this problem..

Edit: the original data set had two columns but I added more portion of the data..

jpp · Accepted Answer

Since the second columns are the values that I want to process I read the first_y and second_y that way.

This isn't true, you've read the second column onwards, as indicated by the second colon in firt_df.iloc[:, 1:]. Instead, just select the second column. You can also use pd.Series.values instead of explicitly calling np.array:

first_y = firt_df.iloc[:, 1].values
second_y = second_df.iloc[:, 1].values

dot_product = np.dot(first_y, second_y)

The convention is common to Python lists and NumPy arrays.

Python: dot product shapes not aligned?

Answers (1)

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