问题
import pandas as pd
import numpy as np
df = pd.read_csv("test.txt",
sep='\t',
names= ["ElementID","Load"])
df.insert(0, 'count', df.groupby('ElementID').cumcount())
df2 = df.pivot(index='count',columns='ElementID', values='Load')
df2_norm = df2.apply(lambda x: (x / x.abs().max()))
allowableCorr = df2_norm.corr(method = 'pearson')
slope = allowableCorr * (df2_norm.std().values /
df2_norm.std().values[:, np.newaxis])
I have a lot of datasets which I use the .corr method to get the r^2 value. However , the code above does not always match the slope results. I am using excel to test the slope value. What am i doing wrong?
test.txt
716865 -301.70224
716865 -1258.647095
716865 126.767021
716865 701.922852
716865 742.309021
716865 -150.711334
716865 392.487152
716865 -91.858528
716865 33.217159
716865 883.334717
716865 143.003952
716865 -127.804535
716865 340.573639
716865 -911.843262
716865 210.663971
716865 -337.319061
716865 -574.554688
716865 -85.879967
716865 55.362972
716865 -598.517029
716865 -507.735901
716865 -806.568481
716865 -768.94989
716865 815.82489
716865 -780.519714
716865 1077.297729
716865 -1882.155151
716865 -21.309252
716865 -14.403346
716865 100.049232
716865 125.137939
716865 94.089989
716865 -36.337738
716865 104.333527
716865 -433.559021
716865 4.860066
716865 -190.261063
716865 -216.324509
716865 -13.302347
716865 15.41076
716865 -23.88785
716865 4.825286
716865 6.271163
716865 9.85747
716865 -11.765738
716865 -8.179381
716865 11.288673
716865 7.702322
716865 -10.334545
716865 -6.748201
716865 -32.338951
716865 0.034022
716865 -7.783642
716865 1.344555
716865 1.008542
716865 7.51763
716865 4.278917
716865 13.613921
716865 0.657209
716865 0.004726
716865 -0.008553
716865 0.321643
716865 -28.621054
716865 -25.886494
716865 1.211354
716865 -7.711781
716865 -17.332674
716865 1.641209
716865 0.726812
716865 0.740472
716865 -17.371124
716865 1.453994
716865 7.715975
716865 -0.276754
716865 -31.113707
716865 -17.472322
716865 0.543816
716865 -5.014119
716865 -18.576653
716865 1.347938
716865 0.565348
716865 0.883058
716865 -18.696476
716865 1.037493
716865 5.044785
716865 0.102762
716865 -30.731455
716865 -21.54364
716865 0.843278
716865 -6.287022
716865 -18.482023
716865 1.480709
716865 0.587757
716865 0.770442
716865 -18.555861
716865 1.274093
716865 6.298359
716865 -0.032861
716865 -16.837095
716865 -39.867527
716865 3.341571
716865 -12.108704
716865 -13.806955
716865 2.490994
716865 1.773459
716865 0.826912
716865 -13.895108
716865 1.067095
716865 12.295836
716865 0.017253
716865 -0.017011
716865 -0.03412
716865 0.012205
716865 0.011363
716865 -0.011869
716865 0.025908
716865 0.005893
716865 -0.001441
716865 0.023322
716865 -0.011226
716865 -0.019583
716865 0.006
716865 -0.00641
716865 324.205536
716865 133.731796
716865 -203.469788
716865 422.581543
716865 150.133972
716865 -371.098114
716865 282.197632
716865 79.024696
716865 -181.467117
716865 279.187439
716865 150.074112
716865 99.900131
716865 263.611481
716865 59.430237
716865 -177.68248
716865 88.130081
716865 19.783554
716865 156.729797
716865 159.243668
716865 -96.671829
716865 99.567726
716865 153.336472
716865 -65.270882
716865 -8.313629
716865 -1.822799
716865 14.163381
716865 -0.027411
716865 -7.346685
716865 2.321136
716865 0.553517
716865 7.307581
716865 7.573683
716865 3.40268
716865 0.237121
716865 0.243131
716873 -116.165497
716873 -934.009277
716873 121.008446
716873 674.810791
716873 699.712646
716873 -32.448597
716873 404.385559
716873 44.291641
716873 154.319611
716873 1004.978394
716873 109.746536
716873 9.751794
716873 291.211395
716873 -873.794312
716873 197.930389
716873 -371.370331
716873 -506.104797
716873 -62.006485
716873 42.805466
716873 -530.312378
716873 -530.244873
716873 -762.327087
716873 -760.558655
716873 753.133484
716873 -768.190125
716873 993.777344
716873 -1522.421875
716873 -45.851994
716873 -16.078772
716873 102.985107
716873 112.955887
716873 84.928909
716873 -31.930878
716873 79.560143
716873 -386.377625
716873 1.69816
716873 -171.38002
716873 -186.184341
716873 -9.310679
716873 11.53948
716873 -28.820801
716873 -7.970621
716873 -3.674594
716873 14.181245
716873 -17.635487
716873 0.220536
716873 16.77195
716873 -1.084055
716873 -15.044891
716873 2.811084
716873 -22.310444
716873 0.070187
716873 -6.526618
716873 0.519358
716873 0.93114
716873 6.270535
716873 3.045897
716873 14.72409
716873 0.496607
716873 0.003259
716873 -0.008807
716873 -0.961357
716873 -22.897137
716873 -15.484076
716873 1.015296
716873 -2.042436
716873 -13.44498
716873 1.506008
716873 1.075834
716873 1.359961
716873 -13.63349
716873 1.321194
716873 2.128678
716873 -1.336552
716873 -25.49177
716873 -8.724606
716873 0.443453
716873 0.254901
716873 -14.597396
716873 1.296078
716873 0.919975
716873 1.449683
716873 -14.847031
716873 1.014634
716873 -0.157101
716873 -1.07669
716873 -24.886087
716873 -11.84578
716873 0.696825
716873 -0.714679
716873 -14.426558
716873 1.391136
716873 0.949331
716873 1.36829
716873 -14.641159
716873 1.194632
716873 0.800537
716873 -1.58931
716873 -11.3484
716873 -26.018322
716873 2.909623
716873 -5.166547
716873 -9.892995
716873 2.156415
716873 2.01736
716873 1.533689
716873 -10.137015
716873 0.863195
716873 5.458185
716873 0.013926
716873 -0.011724
716873 -0.030154
716873 0.009871
716873 0.007602
716873 -0.00715
716873 0.019485
716873 0.002956
716873 0.006034
716873 0.016784
716873 -0.010138
716873 -0.015378
716873 0.013079
716873 0.044271
716873 299.691345
716873 168.10495
716873 -137.186401
716873 398.893036
716873 178.197784
716873 -308.874573
716873 259.335876
716873 114.582466
716873 -116.212494
716873 272.913727
716873 195.083252
716873 170.973419
716873 242.900848
716873 92.730392
716873 -115.781334
716873 68.182755
716873 64.158295
716873 206.475113
716873 138.817078
716873 -59.660057
716873 170.986923
716873 140.095535
716873 -17.554855
716873 66.050301
716873 -2.712554
716873 27.134666
716873 0.025514
716873 -6.399142
716873 1.683879
716873 0.518694
716873 6.318477
716873 6.471766
716873 2.251963
716873 0.245339
716873 0.184508
716884 124.174957
716884 -543.34314
716884 102.258514
716884 622.506165
716884 618.796204
716884 132.756882
716884 405.259003
716884 206.318359
716884 301.309723
716884 1106.663208
716884 67.389801
716884 180.681015
716884 221.693985
716884 -792.332825
716884 175.221024
716884 -375.52652
716884 -444.379303
716884 -19.328663
716884 26.917398
716884 -468.37439
716884 -515.50531
716884 -679.881104
716884 -713.56427
716884 652.319946
716884 -716.638184
716884 869.534119
716884 -1034.596313
716884 -84.932793
716884 -29.275459
716884 116.723412
716884 95.447914
716884 71.762871
716884 -25.168804
716884 51.524746
716884 -322.065582
716884 -2.099667
716884 -145.098633
716884 -145.8358
716884 -2.416266
716884 5.303814
716884 -30.934532
716884 -23.214443
716884 -15.070092
716884 16.879007
716884 -22.298216
716884 9.651199
716884 20.94346
716884 -11.005925
716884 -18.233974
716884 13.715357
716884 -8.202349
716884 0.089903
716884 -4.866776
716884 -0.381067
716884 0.791291
716884 4.6311
716884 1.777163
716884 14.526459
716884 0.23941
716884 0.000919
716884 -0.009061
716884 -2.354227
716884 -13.142694
716884 -5.054462
716884 1.041731
716884 3.568333
716884 -7.150105
716884 1.36466
716884 1.518771
716884 2.044704
716884 -7.480896
716884 1.212836
716884 -3.389907
716884 -2.454151
716884 -15.93776
716884 -0.349828
716884 0.577748
716884 5.338133
716884 -8.199544
716884 1.254575
716884 1.369443
716884 2.070532
716884 -8.562329
716884 1.042838
716884 -5.167105
716884 -2.343827
716884 -14.996009
716884 -2.326019
716884 0.778757
716884 4.732598
716884 -7.909725
716884 1.304329
716884 1.408948
716884 2.02876
716884 -8.252971
716884 1.153241
716884 -4.564247
716884 -3.33852
716884 -1.51058
716884 -11.481308
716884 2.67605
716884 1.928831
716884 -3.384082
716884 1.771827
716884 2.34195
716884 2.333492
716884 -3.787011
716884 0.652604
716884 -1.514875
716884 0.009002
716884 -0.003013
716884 -0.027728
716884 0.006013
716884 0.000874
716884 0.00072
716884 0.009466
716884 -0.002151
716884 0.020411
716884 0.007046
716884 -0.006607
716884 -0.017869
716884 0.019977
716884 0.093917
716884 266.815857
716884 218.236526
716884 -23.038584
716884 362.145142
716884 227.943802
716884 -174.718979
716884 228.348831
716884 164.749985
716884 -7.250031
716884 254.411575
716884 256.743378
716884 276.647736
716884 212.703583
716884 138.865784
716884 -13.510377
716884 39.944756
716884 133.330185
716884 291.995575
716884 111.045929
716884 -0.637947
716884 282.187897
716884 116.875557
716884 56.029514
716884 180.233658
716884 -3.815804
716884 39.750874
716884 0.065434
716884 -5.083665
716884 0.926333
716884 0.42528
716884 4.953602
716884 5.192181
716884 1.061818
716884 0.225265
716884 0.09564
来源：https://stackoverflow.com/questions/62643979/find-slope-from-correlation-coefficient-of-pearson-method