Метод pandas.DataFrame.corr вычисляет корреляции по столбцам, возможно, ваши векторы имеют длину 190, и именно поэтому вы видите результат.
Например:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(200, 10))
df.head()
0 1 2 ... 7 8 9
0 0.635328 0.801989 0.928792 ... 0.409299 0.500572 0.096176
1 0.849282 0.947163 0.474262 ... 0.693389 0.260647 0.021113
2 0.057634 0.032594 0.576879 ... 0.954159 0.106100 0.265630
3 0.031656 0.114908 0.902100 ... 0.541662 0.560113 0.148045
4 0.904117 0.878109 0.982493 ... 0.416373 0.038374 0.329338
[5 rows x 10 columns]
df.corr()
0 1 2 ... 7 8 9
0 1.000000 0.158221 -0.027012 ... -0.060187 -0.146682 -0.046548
1 0.158221 1.000000 -0.059599 ... 0.038815 -0.052355 -0.037781
2 -0.027012 -0.059599 1.000000 ... 0.075159 0.015261 0.035054
3 -0.047710 -0.002200 -0.122131 ... -0.060431 0.017568 0.000935
4 -0.029542 -0.036819 0.108064 ... -0.002548 -0.097453 -0.047605
5 -0.007827 0.026060 0.078727 ... 0.009858 0.076280 0.031792
6 0.107026 0.109253 -0.094501 ... -0.069312 -0.045408 -0.103385
7 -0.060187 0.038815 0.075159 ... 1.000000 0.036394 0.028998
8 -0.146682 -0.052355 0.015261 ... 0.036394 1.000000 0.157691
9 -0.046548 -0.037781 0.035054 ... 0.028998 0.157691 1.000000
[10 rows x 10 columns]
# Note 10 x 10 output as we calculated correlation between columns
df.T.corr()
0 1 2 ... 197 198 199
0 1.000000 0.498877 -0.365548 ... 0.332607 -0.334024 -0.167504
1 0.498877 1.000000 -0.206066 ... -0.202328 0.212138 -0.265470
2 -0.365548 -0.206066 1.000000 ... 0.220807 -0.363419 0.006970
3 0.161274 -0.030202 0.350527 ... 0.242371 -0.124993 -0.419660
4 0.413043 0.360164 0.085686 ... 0.299763 -0.057072 -0.354378
5 -0.178747 0.398373 0.122139 ... -0.364762 0.298731 0.096835
6 0.428633 0.332952 0.206733 ... 0.504151 -0.489508 -0.530162
7 0.031738 0.178856 0.671928 ... 0.140511 -0.077814 -0.450641
8 -0.082514 0.217348 -0.263754 ... 0.037616 0.382671 0.078891
9 0.003956 -0.037507 -0.071405 ... 0.416418 -0.364057 -0.422265
10 0.477660 -0.062398 -0.223941 ... 0.052904 -0.005123 0.232310
11 0.064510 0.322448 0.112256 ... 0.037528 -0.122080 -0.149634
12 -0.097806 0.338672 -0.546019 ... -0.220268 0.306534 0.068144
13 -0.031138 0.213664 -0.397962 ... 0.188932 0.180403 -0.378188
14 0.038888 -0.024346 0.238451 ... 0.425915 -0.181613 -0.562234
15 0.465428 0.260079 -0.572854 ... -0.080576 0.082298 -0.261606
16 -0.535678 -0.257890 0.514151 ... -0.259198 -0.053499 0.268481
17 0.308875 -0.186421 -0.391446 ... 0.360044 -0.158955 0.268429
18 -0.275178 0.155606 -0.347488 ... -0.047829 0.533397 -0.412796
19 0.080700 0.391280 0.418628 ... 0.388697 -0.248641 -0.417406
20 -0.423705 -0.664545 0.227255 ... 0.245131 -0.463002 0.577954
21 0.853115 0.356033 -0.445827 ... 0.471394 -0.404416 -0.273944
22 0.307765 -0.038106 0.022683 ... 0.470156 -0.174377 -0.516724
23 0.618258 0.708506 -0.450709 ... -0.002726 0.290722 -0.410717
24 0.469414 0.757931 -0.550096 ... -0.370019 0.310175 0.131973
25 -0.308645 0.494687 -0.153664 ... -0.533062 0.496228 -0.040558
26 0.242789 0.687153 -0.506575 ... -0.448844 0.358878 0.133408
27 -0.069875 -0.473316 0.138600 ... 0.489610 -0.095459 0.139735
28 0.155511 0.238704 0.102230 ... 0.338030 -0.073523 0.182840
29 -0.112128 -0.138975 -0.060637 ... -0.028259 -0.349001 0.490552
.. ... ... ... ... ... ... ...
170 0.226813 0.031162 -0.205408 ... -0.613621 0.117084 0.357222
171 0.451682 0.491021 -0.474939 ... 0.372531 -0.272856 0.008240
172 0.158155 -0.228592 -0.352837 ... -0.118192 0.223844 0.010163
173 0.450049 0.319076 0.158477 ... 0.245050 -0.308017 -0.241448
174 0.190380 0.228906 0.417048 ... 0.143381 0.104080 -0.405662
175 -0.357893 -0.159158 -0.289425 ... -0.371118 0.579670 -0.413831
176 0.078708 0.165983 0.324744 ... 0.180002 -0.146310 -0.329281
177 -0.450046 -0.180979 -0.414211 ... -0.289672 0.616857 -0.062768
178 0.549715 0.408558 -0.055886 ... -0.170083 -0.294178 0.081205
179 -0.111201 0.472122 -0.075174 ... -0.518823 0.470020 0.047267
180 0.098447 -0.201907 0.119621 ... 0.019750 -0.268528 -0.056763
181 0.234285 0.144266 0.631389 ... 0.329869 -0.395606 0.139690
182 0.394321 0.239157 -0.043077 ... 0.423926 -0.695812 0.273296
183 0.634644 0.097258 -0.171962 ... -0.047281 -0.256005 0.162903
184 0.103204 -0.192399 -0.777966 ... 0.056135 -0.034793 0.034549
185 0.339759 0.239913 0.582344 ... 0.467792 -0.378750 -0.201036
186 -0.548353 -0.769797 0.067698 ... 0.085241 0.141845 0.210096
187 -0.167574 0.437010 0.178703 ... 0.036527 0.076168 -0.510175
188 0.048067 -0.586393 0.489235 ... 0.390122 -0.686588 0.234376
189 0.217725 0.049377 -0.477302 ... 0.089802 0.266892 -0.336807
190 -0.767265 -0.684753 0.213544 ... -0.010338 0.242734 0.254413
191 -0.428090 -0.898531 0.180227 ... 0.286884 -0.255007 0.141773
192 0.163057 -0.110622 -0.067697 ... 0.047667 -0.149770 0.577512
193 -0.174333 -0.612754 0.272226 ... 0.238010 -0.688222 0.725651
194 0.282385 0.153783 -0.218055 ... -0.519361 -0.136575 0.517739
195 0.533939 0.235234 -0.050181 ... 0.186320 -0.342353 -0.430962
196 -0.186149 -0.546681 0.344461 ... 0.130606 -0.057928 -0.451576
197 0.332607 -0.202328 0.220807 ... 1.000000 -0.718022 -0.250786
198 -0.334024 0.212138 -0.363419 ... -0.718022 1.000000 -0.193411
199 -0.167504 -0.265470 0.006970 ... -0.250786 -0.193411 1.000000
[200 rows x 200 columns]
# Transposing the dataframe calculates the correlations for each vector by making them the columns
Стоит также отметить, что если вы используете scipy.stats.spaermanr, то поведение будет таким же.Он рассчитывает корреляции для векторов столбцов.