Develop Reference

Applying a non aggregating function to a groupby pandas object

I have a dataframe (called cep) with two indexes (Cloud and Mode) and data columns. It looks like this :
The data columns are fitted to a linear function and I'm extracting the residuals to the fit in this way :
import pandas as pd
from scipy.optimize import least_squares
args = [-1, 15] # initial guess for the fit
def residuals(args, x, y):
"""
Residual with respect to a linear function
args : list with 2 arguments
x : array
y : array
"""
return args[0] * x + args[1] - y
def residual_function(df):
"""
Returns the array of the residuals
"""
return least_squares(residuals, args, loss='soft_l1', f_scale=0.5, args=(df.logP1, df.W)).fun
cep.groupby(['Cloud', 'Mode']).apply(lambda grp : residual_function(grp))
This gives the expected result :
Now is my issue : I'd like to insert those residual values each in their respective row in the original dataframe to compare them with other columns.
I checked that the returned arrays are of the right length to be inserted but so far I have no idea how to proceed.
I tried to follow tutorials, but the difference with the textbook problem here is that the function I applied does not aggregate the data. Do you have some hints?
Small sample data here :
Mode;Cloud;W;logP1
F;LMC;14,525;0,4939
F;LMC;13,4954;0,7491
F;LMC;14,5421;0,4249
F;LMC;12,033;1,0215
F;LMC;14,3422;0,5655
F;LMC;13,937;0,6072
F;LMC;13,53;0,737
F;LMC;15,2106;0,2309
F;LMC;14,0813;0,5721
F;LMC;14,5128;0,41
F;LMC;14,1059;0,5469
F;LMC;15,6032;0,1014
F;LMC;13,1088;0,8562
F;LMC;12,3528;1,0513
F;LMC;13,1629;0,8416
F;LMC;14,3114;0,4867
F;LMC;14,4013;0,498
F;LMC;13,5057;0,7131
F;LMC;14,3626;0,464
F;LMC;14,5973;0,4111
F;LMC;13,9286;0,6059
F;LMC;15,066;0,2711
F;LMC;12,7364;0,9466
F;LMC;13,3753;0,7442
F;LMC;13,9748;0,5854
F;LMC;12,8836;0,8946
F;LMC;14,4912;0,4206
F;LMC;14,4131;0,4567
F;LMC;12,183;1,1382
F;LMC;14,5492;0,3686
F;LMC;14,1482;0,5339
F;LMC;13,7062;0,7116
F;LMC;13,0731;0,8682
F;LMC;11,5609;1,353
F;LMC;13,9453;0,5551
F;LMC;14,0072;0,6715
F;LMC;13,9838;0,6021
F;LMC;13,9974;0,5562
F;LMC;14,3898;0,5069
F;LMC;14,4497;0,4433
F;LMC;14,3524;0,5064
F;LMC;12,9604;0,9134
F;LMC;12,9757;0,8548
F;LMC;14,2783;0,4927
F;LMC;13,7148;0,6758
F;LMC;14,2348;0,5142
F;LMC;12,6793;0,9415
F;LMC;14,2241;0,5738
F;LMC;14,472;0,4554
F;LMC;15,1508;0,2076
F;LMC;12,5414;1,0159
F;LMC;14,2102;0,5334
F;LMC;15,6086;0,1116
F;LMC;13,2986;0,8381
F;LMC;13,0136;0,8864
F;LMC;13,9774;0,585
F;LMC;14,4256;0,533
F;LMC;14,3582;0,4578
F;LMC;14,3258;0,4859
F;LMC;14,6646;0,3757
F;LMC;12,733;0,9901
F;LMC;14,6296;0,3839
F;LMC;14,054;0,5766
F;LMC;14,3194;0,4884
F;LMC;12,6602;0,9715
F;LMC;13,5909;0,5675
F;LMC;13,9268;0,6196
F;LMC;12,5813;0,9935
F;LMC;13,0824;0,8591
F;LMC;13,5097;0,7375
F;LMC;13,1938;0,5053
F;LMC;14,7357;0,3253
F;LMC;14,0624;0,6009
F;LMC;14,1528;0,533
F;LMC;14,6709;0,4007
F;LMC;14,2378;0,4875
F;LMC;11,951;1,2004
F;LMC;14,4555;0,4777
F;LMC;14,4001;0,4404
F;LMC;13,7707;0,6311
F;LMC;14,578;0,4175
F;LMC;15,8662;0,0159
F;LMC;14,055;0,5687
F;LMC;13,6238;0,7307
F;LMC;15,2572;0,2171
F;LMC;13,4022;0,7723
F;LMC;14,2392;0,5256
F;LMC;14,2505;0,4977
F;LMC;14,7174;0,3614
F;LMC;14,487;0,418
F;LMC;14,9309;0,3086
F;LMC;13,8352;0,6334
F;LMC;14,5598;0,41
F;LMC;14,5614;0,422
F;LMC;14,1486;0,5149
F;LMC;14,0304;0,4945
F;LMC;13,5781;0,6801
F;LMC;14,79;0,3218
F;LMC;12,376;1,0908
F;LMC;15,3215;0,2176
F;LMC;14,7264;0,3845
F;LMC;14,6276;0,4057
F;LMC;14,1712;0,5313
F;LMC;14,4153;0,483
F;LMC;12,905;0,9356
F;LMC;14,442;0,4309
F;LMC;12,8702;0,9159
F;LMC;12,8963;0,5775
F;LMC;13,8304;0,6467
F;LMC;14,4665;0,4165
F;LMC;13,0756;0,5794
F;LMC;13,841;0,6593
F;LMC;14,0924;0,5671
F;LMC;13,7546;0,6778
F;LMC;14,2828;0,5181
F;LMC;14,2424;0,5082
F;LMC;14,659;0,3989
F;LMC;13,7528;0,6768
F;LMC;13,7743;0,6368
F;LMC;13,2894;0,791
F;LMC;14,7512;0,3187
F;LMC;14,5241;0,4452
F;LMC;14,301;0,5121
F;LMC;13,334;0,7945
F;LMC;13,5052;0,7012
F;LMC;14,3664;0,4549
F;LMC;14,8614;0,3278
F;LMC;13,8612;0,582
F;LMC;14,2668;0,5158
F;LMC;14,3937;0,4457
F;LMC;14,0226;0,582
F;LMC;14,387;0,5565
F;LMC;14,3198;0,4362
F;LMC;14,4404;0,4701
F;LMC;14,2774;0,4939
F;LMC;13,7678;0,6557
F;LMC;14,3212;0,4882
F;LMC;14,6453;0,3696
F;LMC;13,9064;0,6084
F;LMC;13,5167;0,7581
F;LMC;14,1692;0,5134
F;LMC;14,6714;0,4136
F;LMC;14,4332;0,4507
F;LMC;14,705;0,3631
F;LMC;13,6728;0,496
F;LMC;15,358;0,1651
F;LMC;13,7592;0,6278
F;LMC;14,0626;0,5754
F;LMC;13,1127;0,8692
F;LMC;14,2108;0,498
F;LMC;14,4519;0,4449
F;LMC;14,0041;0,5666
F;LMC;14,157;0,5392
F;LMC;14,254;0,5245
F;LMC;15,4844;0,1838
F;LMC;14,0845;0,5626
F;LMC;13,0861;0,838
F;LMC;13,3144;0,831
F;LMC;14,2535;0,4911
F;LMC;14,0256;0,5723
F;LMC;14,3246;0,4938
F;LMC;14,4412;0,4136
F;LMC;14,1043;0,518
F;LMC;14,7512;0,3772
F;LMC;14,3982;0,5039
F;LMC;14,2701;0,5042
F;LMC;13,9166;0,5941
F;LMC;13,0324;0,837
F;LMC;13,4839;0,6331
F;LMC;13,4491;0,7443
F;LMC;14,4702;0,458
F;LMC;14,4814;0,4595
F;LMC;14,3008;0,4575
F;LMC;14,922;0,3313
F;LMC;14,6542;0,4263
F;LMC;14,5007;0,4838
F;LMC;14,4335;0,4829
F;LMC;14,4737;0,4586
F;LMC;14,2537;0,5442
F;LMC;14,038;0,5473
F;LMC;14,1413;0,5523
F;LMC;14,669;0,3505
F;LMC;12,3572;1,1033
F;LMC;13,868;0,6416
F;LMC;13,4292;0,816
F;LMC;11,6771;1,3442
F;LMC;14,5086;0,4654
F;LMC;14,3588;0,4807
F;LMC;14,6915;0,3674
F;LMC;15,6488;0,0647
F;LMC;12,4187;0,9791
F;LMC;14,1555;0,5235
F;LMC;14,5765;0,4281
F;LMC;14,3579;0,4596
F;LMC;13,0932;0,7957
F;LMC;14,4552;0,4216
F;LMC;13,2221;0,8505
F;LMC;14,4465;0,4466
F;LMC;14,2439;0,5032
F;LMC;14,9606;0,6308
F;LMC;14,4774;0,4424
F;LMC;14,1875;0,5361
F;LMC;13,3982;0,7644
F;LMC;13,0973;0,8595
F;LMC;13,8264;0,6334
F;LMC;13,9296;0,6164
F;LMC;14,5778;0,4033
F;LMC;13,579;0,726
F;LMC;14,0054;0,5779
F;LMC;14,1219;0,5451
F;LMC;14,3512;0,4808
F;LMC;14,5058;0,4199
F;LMC;14,598;0,4201
F;LMC;14,9516;0,2498
F;LMC;13,9944;0,6075
F;LMC;13,9462;0,557
F;LMC;14,2576;0,5148
F;LMC;14,9814;0,2929
F;LMC;14,3851;0,4573
F;LMC;14,3474;0,4606
F;LMC;14,4929;0,3882
F;LMC;14,5201;0,4234
F;LMC;13,7677;0,6548
F;LMC;14,3146;0,4695
F;LMC;14,2846;0,507
F;LMC;14,0967;0,5525
F;LMC;14,7976;0,3546
F;LMC;13,7497;0,6362
F;LMC;14,4647;0,4363
F;LMC;14,1924;0,5293
F;LMC;14,588;0,4089
F;LMC;13,4896;0,7329
F;LMC;14,695;0,3737
F;LMC;14,2672;0,4857
F;LMC;14,0784;0,5848
F;LMC;13,879;0,5743
F;LMC;14,2214;0,4988
F;LMC;12,922;0,8487
F;LMC;14,189;0,5238
F;LMC;13,9938;0,5713
F;LMC;14,379;0,4771
F;LMC;11,2308;1,3564
F;LMC;14,4472;0,4205
F;LMC;14,3739;0,4699
F;LMC;14,393;0,4416
F;LMC;13,9108;0,5927
F;LMC;14,0298;0,6058
F;LMC;15,1538;0,1961
F;LMC;13,0393;0,8731
F;LMC;13,7144;0,645
F;LMC;14,2682;0,487
F;LMC;14,3506;0,4927
F;LMC;14,0472;0,5619
F;LMC;15,1418;0,2506
F;LMC;13,1227;0,5998
F;LMC;13,5646;0,7193
F;LMC;14,5872;0,4357
F;LMC;14,2636;0,5007
F;LMC;13,9564;0,5599
F;LMC;12,8576;0,946
F;LMC;12,3042;1,1454
F;LMC;11,8416;1,3675
F;LMC;13,5498;0,7219
F;LMC;12,1976;1,1581
F;LMC;13,8632;0,6202
F;LMC;14,2952;0,4807
F;LMC;14,4349;0,4437
F;LMC;14,2392;0,5445
F;LMC;13,7248;0,7213
F;LMC;14,3395;0,5117
F;LMC;15,3588;0,2253
F;LMC;12,8509;0,9229
F;LMC;15,5192;0,1453
F;LMC;14,2072;0,4975
F;LMC;14,3524;0,4945
F;LMC;14,5152;0,4488
F;LMC;14,5106;0,4558
F;LMC;14,5759;0,3786
F;LMC;11,196;1,2374
F;LMC;14,3736;0,4788
F;LMC;14,1726;0,528
F;LMC;11,7899;1,1995
F;LMC;12,1062;1,1823
F;LMC;13,7113;0,6714
F;LMC;14,3512;0,4815
F;LMC;13,1016;0,8181
F;LMC;14,4968;0,562
F;LMC;12,4557;1,0671
F;LMC;14,0573;0,551
F;LMC;14,5916;0,4066
F;LMC;14,3214;0,488
F;LMC;13,5498;0,4885
F;LMC;14,4679;0,4273
F;LMC;14,2426;0,4816
F;LMC;13,5759;0,7052
F;LMC;14,0081;0,5769
F;LMC;14,0828;0,5379
F;LMC;12,4168;0,7578
F;LMC;14,1624;0,5052
F;LMC;13,8029;0,6621
F;LMC;14,1944;0,5145
F;LMC;13,7944;0,6184
F;LMC;15,0234;0,3158
F;LMC;13,0961;0,8282
F;LMC;13,976;0,5889
F;LMC;14,3236;0,4847
F;LMC;14,2618;0,4691
F;LMC;13,4528;0,7349
F;LMC;14,2846;0,507
F;LMC;14,4115;0,446
F;LMC;14,2199;0,5336
F;LMC;14,456;0,4423
F;LMC;14,2938;0,488
F;LMC;14,4109;0,4606
F;LMC;14,2599;0,497
F;LMC;13,9034;0,6384
F;LMC;13,6126;0,7075
F;LMC;14,5036;0,4218
F;LMC;14,0065;0,5741
F;LMC;14,8622;0,3404
F;LMC;14,635;0,3683
F;LMC;14,222;0,5454
F;LMC;14,1501;0,5548
F;LMC;14,0822;0,5705
F;LMC;13,5036;0,7267
F;LMC;14,5528;0,4161
F;LMC;14,3332;0,4614
F;LMC;14,1511;0,5471
F;LMC;14,6113;0,3934
F;LMC;14,2998;0,5031
F;LMC;14,1807;0,5352
F;LMC;13,5114;0,7013
F;LMC;12,2096;1,1344
F;LMC;14,3799;0,4304
F;LMC;12,4526;1,1135
F;LMC;14,5042;0,447
F;LMC;13,4594;0,7336
F;LMC;13,2066;0,8423
F;LMC;14,3734;0,4711
F;LMC;13,945;0,5953
F;LMC;12,9938;0,8969
F;LMC;13,4993;0,7034
F;LMC;13,9466;0,5678
F;LMC;14,1772;0,5077
F;LMC;13,5566;0,6949
F;LMC;14,021;0,5811
F;LMC;14,0264;0,646
F;LMC;12,0242;1,1666
F;LMC;14,3106;0,5027
F;LMC;14,9838;0,3164
F;LMC;14,1718;0,5266
F;LMC;14,2606;0,489
F;LMC;12,6479;1,0206
F;LMC;12,9768;0,8684
F;LMC;14,0837;0,5785
F;LMC;13,7944;0,6609
F;LMC;13,532;0,6911
F;LMC;14,835;0,3375
F;LMC;13,7378;0,6941
F;LMC;14,3618;0,4658
F;LMC;12,4782;1,0176
F;LMC;14,2216;0,4981
F;LMC;14,3958;0,4917
F;LMC;11,3796;1,3161
F;LMC;13,8073;0,6301
F;LMC;14,414;0,4601
F;LMC;12,4266;1,086
F;LMC;14,7974;0,3547
F;LMC;14,3369;0,5189
F;LMC;14,3202;0,4874
F;LMC;14,4614;0,4664
F;LMC;13,8344;0,6339
F;LMC;14,0452;0,5896
F;LMC;11,9134;1,161
F;LMC;14,2492;0,4891
F;LMC;14,1338;0,5139
F;LMC;14,439;0,4476
F;LMC;14,1446;0,5322
F;LMC;14,102;0,549
F;LMC;14,5043;0,4421
F;LMC;14,388;0,4511
F;LMC;12,3812;1,0331
F;LMC;14,5086;0,4294
F;LMC;13,6822;0,671
F;LMC;12,3012;1,0862
F;LMC;14,0848;0,534
F;LMC;14,3381;0,4886
F;LMC;14,5544;0,3908
F;LMC;14,216;0,5226
F;LMC;14,5028;0,4323
F;LMC;12,7769;0,9244
F;LMC;13,6262;0,6984
F;LMC;14,5276;0,4107
F;LMC;13,921;0,5835
F;LMC;14,6279;0,396
F;LMC;14,6304;0,3796
F;LMC;14,2079;0,4722
F;LMC;12,4538;1,0356
F;LMC;14,2662;0,4876
F;LMC;13,8493;0,6217
F;LMC;12,9806;0,8385
F;LMC;14,3148;0,4768
F;LMC;14,2225;0,49
F;LMC;14,3932;0,4084
F;LMC;13,6934;0,5829
F;LMC;14,1702;0,5297
F;LMC;11,7812;1,2435
F;LMC;14,2866;0,4778
F;LMC;15,2824;0,1739
F;LMC;14,451;0,4485
F;LMC;14,4842;0,4222
F;LMC;14,3422;0,449
F;LMC;14,4408;0,4435
F;LMC;12,527;1,0298
F;LMC;12,3746;1,1016
F;LMC;11,4802;1,3276
F;LMC;14,47;0,4643
F;LMC;14,1469;0,5183
F;SMC;14,423;0,4796
F;SMC;15,5626;0,2344
F;SMC;15,6889;0,236
F;SMC;15,3574;0,2926
F;SMC;15,8049;0,1015
F;SMC;12,9034;0,9993
F;SMC;14,0039;0,6867
F;SMC;15,9834;0,1812
F;SMC;15,7707;0,2028
F;SMC;15,777;0,1735
F;SMC;14,7121;0,4973
F;SMC;13,8691;0,7188
F;SMC;14,889;0,4123
F;SMC;14,5322;0,6233
F;SMC;15,6791;0,331
F;SMC;13,9406;0,7262
F;SMC;13,728;0,8514
F;SMC;15,1952;0,3583
F;SMC;16,0921;0,1397
F;SMC;15,6162;0,1532
F;SMC;15,786;0,2563
F;SMC;16,0774;0,1197
F;SMC;14,4397;0,599
F;SMC;15,8693;0,2072
F;SMC;15,6668;0,2452
F;SMC;15,1954;0,3509
F;SMC;14,1387;0,669
F;SMC;15,6928;0,2125
F;SMC;14,6266;0,5017
F;SMC;15,9557;0,1772
F;SMC;15,607;0,2501
F;SMC;15,9632;0,1629
F;SMC;15,7932;0,2325
F;SMC;15,7108;0,1534
F;SMC;13,037;0,9898
F;SMC;15,3998;0,2915
F;SMC;15,1724;0,3675
F;SMC;13,7222;0,7848
F;SMC;14,8296;0,5222
F;SMC;15,704;0,2407
F;SMC;13,5231;0,8378
F;SMC;14,4338;0,5303
F;SMC;14,6202;0,4843
F;SMC;16,2836;0,0473
F;SMC;15,6011;0,1758
F;SMC;16,0037;0,1571
F;SMC;13,9062;0,6286
F;SMC;16,0606;0,0557
F;SMC;13,2924;0,8905
F;SMC;15,9942;0,1997
F;SMC;15,7766;0,2395
F;SMC;10,8462;1,6309
F;SMC;15,956;0,1425
F;SMC;13,857;0,7079
F;SMC;15,3619;0,2696
F;SMC;14,0064;0,6903
F;SMC;15,6531;0,2602
F;SMC;14,9001;0,5001
F;SMC;14,3957;0,6156
F;SMC;15,4414;0,3174
F;SMC;15,8321;0,1822
F;SMC;16,3562;0,1385
F;SMC;15,8812;0,1651
F;SMC;15,1404;0,408
F;SMC;13,7978;0,8055
F;SMC;15,9291;0,132
F;SMC;15,0555;0,507
F;SMC;15,5766;0,2596
F;SMC;13,6006;0,8469
F;SMC;16,455;0,0629
F;SMC;15,8762;0,1072
F;SMC;16,2856;0,0768
F;SMC;15,8521;0,2129
F;SMC;15,7685;0,2374
F;SMC;16,1197;0,1043
F;SMC;16,0851;0,2333
F;SMC;15,8126;0,1777
F;SMC;14,3891;0,6065
F;SMC;14,6419;0,5446
F;SMC;15,3942;0,3101
F;SMC;15,5785;0,2494
F;SMC;15,661;0,2227
F;SMC;15,9648;0,1405
F;SMC;12,7911;1,0845
F;SMC;15,9351;0,1575
F;SMC;14,1764;0,6864
F;SMC;15,153;0,3624
F;SMC;15,9336;0,1232
F;SMC;15,0124;0,3796
F;SMC;16,1231;0,106
F;SMC;14,4362;0,5306
F;SMC;13,1883;0,8354
F;SMC;15,8972;0,1757
F;SMC;14,1612;0,7287
F;SMC;15,3792;0,2869
F;SMC;16,421;0,0329
F;SMC;14,833;0,4543
F;SMC;14,3997;0,5912
F;SMC;15,8797;0,1747
F;SMC;16,0337;0,1565
F;SMC;15,7371;0,2251
F;SMC;13,954;0,7293
F;SMC;14,1691;0,6695
F;SMC;15,6208;0,2211
F;SMC;14,3416;0,6492
F;SMC;14,6636;0,5423
F;SMC;16,0386;0,1506
F;SMC;14,6578;0,5604
F;SMC;15,6368;0,24
F;SMC;14,843;0,4738
F;SMC;14,9818;0,4869
F;SMC;12,4251;1,1641
F;SMC;15,0727;0,4671
F;SMC;14,1448;0,5949
F;SMC;15,2148;0,3644
F;SMC;15,9372;0,117
F;SMC;15,4336;0,3018
F;SMC;14,5416;0,557
F;SMC;16,4654;0,0436
F;SMC;14,934;0,5498
F;SMC;14,3896;0,695
F;SMC;15,3896;0,3492
F;SMC;15,8122;0,1602
F;SMC;13,7822;0,704
F;SMC;15,7938;0,1679
F;SMC;15,4049;0,3059
F;SMC;16,0742;0,1187
F;SMC;15,704;0,2036
F;SMC;14,9947;0,3748
F;SMC;15,1374;0,4001
F;SMC;13,2254;0,7136
F;SMC;14,3267;0,577
F;SMC;12,7772;1,0317
F;SMC;15,5302;0,3074
F;SMC;16,12;0,1395
F;SMC;15,9826;0,1873
F;SMC;15,9196;0,2025
F;SMC;15,5396;0,2888
F;SMC;14,0063;0,7543
F;SMC;14,6752;0,542
F;SMC;14,3782;0,6365
F;SMC;15,8015;0,2321
F;SMC;15,4898;0,0235
F;SMC;15,6376;0,2499
F;SMC;15,527;0,2697
F;SMC;15,2883;0,3324
F;SMC;15,1014;0,3996
F;SMC;14,435;0,5827
F;SMC;16,1522;0,0832
F;SMC;13,3787;0,8974
F;SMC;16,6258;0,0226
F;SMC;14,0421;0,8043
F;SMC;15,4764;0,2719
F;SMC;14,1377;0,6069
F;SMC;15,3654;0,3461
F;SMC;16,3063;0,0677
F;SMC;15,5912;0,2227
F;SMC;14,555;0,5143
F;SMC;16,2947;0,0824
F;SMC;15,2208;0,3488
F;SMC;16,8052;-0,0287
F;SMC;15,8592;0,1835
F;SMC;15,6349;0,2632
F;SMC;16,522;0,0581
F;SMC;15,7794;0,3351
F;SMC;16,095;0,1574
F;SMC;16,0564;0,1818
F;SMC;16,4614;0,0897
F;SMC;16,1351;0,1332
F;SMC;14,4711;0,5808
F;SMC;13,8768;0,6795
F;SMC;16,2458;0,1273
F;SMC;16,1994;0,0372
F;SMC;15,3434;0,3072
F;SMC;15,5384;0,2442
F;SMC;14,5322;0,5703
F;SMC;15,7762;0,3507
F;SMC;14,3793;0,5628
F;SMC;15,4777;0,3139
F;SMC;15,9216;0,1764
F;SMC;14,3758;0,5278
F;SMC;15,2363;0,3313
F;SMC;14,3224;0,3258
F;SMC;15,2266;0,3656
F;SMC;15,6305;0,174
F;SMC;14,046;0,7832
F;SMC;14,8704;0,507
F;SMC;16,0267;0,2357
F;SMC;16,0671;0,154
F;SMC;13,8434;0,6901
F;SMC;14,4167;0,5992
F;SMC;15,9808;0,125
F;SMC;16,0696;0,1131
F;SMC;15,166;0,166
F;SMC;14,1023;0,6447
F;SMC;13,9666;0,6979
F;SMC;15,64;0,2577
F;SMC;15,6974;0,2429
F;SMC;15,1257;0,3877
F;SMC;15,186;0,3295
F;SMC;14,87;0,4651
F;SMC;16,0943;0,1807
F;SMC;15,7421;0,1809
F;SMC;14,6085;0,5253
F;SMC;14,6912;0,4777
F;SMC;14,1322;0,71
F;SMC;15,3319;0,2937
F;SMC;14,9283;0,4639
F;SMC;15,3753;0,2732
F;SMC;15,0886;0,3989
F;SMC;15,3778;0,3028
F;SMC;16,4933;0,0274
F;SMC;14,7944;0,4336
F;SMC;13,7806;0,7397
F;SMC;14,1895;0,6325
F;SMC;15,947;0,1084
F;SMC;15,9606;0,1665
F;SMC;15,417;0,0976
F;SMC;15,2905;0,3652
F;SMC;14,7712;0,4453
F;SMC;14,6692;0,5412
F;SMC;16,1936;0,0286
F;SMC;15,6136;0,2097
F;SMC;15,8061;0,078
F;SMC;15,3243;0,3385
F;SMC;15,2366;0,3669
F;SMC;16,1653;0,0573
F;SMC;15,916;0,1591
F;SMC;15,2422;0,3216
F;SMC;12,2583;1,2107
F;SMC;15,6361;0,1766
F;SMC;16,0818;0,1771
F;SMC;15,6966;0,2147
F;SMC;16,193;0,0657
F;SMC;14,8256;0,4574
F;SMC;15,7214;0,2185
F;SMC;15,5803;0,2725
F;SMC;14,7322;0,4754
F;SMC;15,8964;0,1898
F;SMC;14,5428;0,4732
F;SMC;16,1362;0,1396
F;SMC;16,2832;0,0473
F;SMC;15,6508;0,2232
F;SMC;14,725;0,4998
F;SMC;16,1585;0,1106
F;SMC;15,2284;0,3727
F;SMC;15,1728;0,3718
F;SMC;14,5354;0,5431
F;SMC;15,8224;0,1256
F;SMC;15,5462;0,2633
F;SMC;14,942;0,455
F;SMC;16,02;0,1426
F;SMC;15,2292;0,2965
F;SMC;14,6639;0,4402
F;SMC;14,887;0,4365
F;SMC;15,8288;0,1924
F;SMC;14,4903;0,5274
F;SMC;15,9464;0,1638
F;SMC;15,8069;0,1999
F;SMC;14,9924;0,3985
F;SMC;15,6917;0,1355
F;SMC;15,5414;0,1628
F;SMC;15,6168;0,2157
F;SMC;15,8006;0,177
F;SMC;14,9294;0,4732
F;SMC;14,5272;0,599
F;SMC;15,7318;0,2691
F;SMC;14,5181;0,5782
F;SMC;15,8524;0,2074
F;SMC;13,773;0,747
F;SMC;15,7608;0,1586
F;SMC;13,947;0,688
F;SMC;14,9774;0,4224
F;SMC;14,5288;0,4912
F;SMC;12,4944;1,2355
F;SMC;13,8683;0,6944
F;SMC;15,7118;0,186
F;SMC;15,7392;0,2081
F;SMC;12,292;1,1395
F;SMC;14,7918;0,4632
F;SMC;15,4428;0,3367
F;SMC;14,7542;0,4279
F;SMC;15,2914;0,3575
F;SMC;14,7332;0,4836
F;SMC;14,566;0,5553
F;SMC;15,9406;0,1167
F;SMC;15,6304;0,2296
F;SMC;14,0478;0,7063
F;SMC;15,5402;0,2821
F;SMC;15,6019;0,2443
F;SMC;15,6554;0,1979
F;SMC;14,7736;0,1631
F;SMC;16,1684;0,119
F;SMC;14,5113;0,5073
F;SMC;15,5466;0,134
F;SMC;15,1128;0,3919
F;SMC;13,4782;0,8109
F;SMC;15,8534;0,2208
F;SMC;13,1824;0,9072
F;SMC;15,8466;0,1901
1;LMC;13,9452;0,4076
1;LMC;14,3302;0,3149
1;LMC;12,9682;0,6984
1;LMC;15,0586;0,1023
1;LMC;14,328;0,304
1;LMC;15,024;0,0882
1;LMC;14,0594;0,3924
1;LMC;17,2026;-0,5304
1;LMC;14,327;0,3192
1;LMC;13,8748;0,4361
1;LMC;17,155;-0,4783
1;LMC;14,3154;0,3197
1;LMC;14,3376;0,2943
1;LMC;14,462;0,3461
1;LMC;14,139;0,3647
1;LMC;16,764;-0,4451
1;LMC;15,1618;0,1008
1;LMC;14,2229;0,3328
1;LMC;13,8046;0,4946
1;LMC;14,4268;0,2703
1;LMC;15,5032;-0,0368
1;LMC;15,9052;-0,1647
1;LMC;13,908;0,4434
1;LMC;14,3352;0,2986
1;LMC;13,6286;0,5326
1;LMC;13,7934;0,4842
1;LMC;14,3979;0,2817
1;LMC;14,0496;0,4238
1;LMC;14,4368;0,2939
1;LMC;14,3242;0,3164
1;LMC;12,6825;0,7719
1;LMC;13,846;0,4483
1;LMC;14,5746;0,2727
1;LMC;14,5171;0,2641
1;LMC;14,9218;0,1209
1;LMC;14,2248;0,3411
1;LMC;14,3478;0,3109
1;LMC;14,0999;0,357
1;LMC;14,5558;0,2632
1;LMC;13,7602;0,4936
1;LMC;14,5354;0,2775
1;LMC;13,5663;0,5364
1;LMC;17,0694;-0,4754
1;LMC;14,2915;0,3346
1;LMC;14,7311;0,218
1;LMC;13,6888;0,5417
1;LMC;14,627;0,2133
1;LMC;13,4404;0,597
1;LMC;14,7168;0,2212
1;LMC;15,0594;0,3161
1;LMC;15,0425;0,1061
1;LMC;16,815;-0,4438
1;LMC;16,001;-0,1914
1;LMC;14,4216;0,2488
1;LMC;14,4748;0,286
1;LMC;13,8631;0,466
1;LMC;14,676;0,2098
1;LMC;14,4089;0,3046
1;LMC;14,2384;0,3559
1;LMC;14,2154;0,3397
1;LMC;14,059;0,3829
1;LMC;14,7006;0,2089
1;LMC;13,2151;0,6923
1;LMC;14,5228;0,2442
1;LMC;14,1972;0,3233
1;LMC;14,7161;0,2052
1;LMC;14,4328;0,2944
1;LMC;14,4018;0,2906
1;LMC;14,7142;0,2083
1;LMC;14,5522;0,2311
1;LMC;13,6784;0,5121
1;LMC;14,396;0,31
1;LMC;14,5408;0,2582
1;LMC;13,9204;0,4699
1;LMC;14,3842;0,308
1;LMC;13,9161;0,4451
1;LMC;14,5161;0,2751
1;LMC;16,6794;-0,4003
1;LMC;14,2213;0,3356
1;LMC;14,0804;0,3867
1;LMC;14,3438;0,2957
1;LMC;16,7434;-0,4476
1;LMC;14,4333;0,2808
1;LMC;14,3312;0,2889
1;LMC;14,504;0,247
1;LMC;13,2101;0,6412
1;LMC;13,8247;0,4442
1;LMC;13,962;0,4153
1;LMC;14,0806;0,3598
1;LMC;14,4793;0,2675
1;LMC;14,8813;0,1499
1;LMC;14,5757;0,2212
1;LMC;14,409;0,2996
1;LMC;13,8864;0,4335
1;LMC;14,1462;0,3252
1;LMC;13,4634;0,5562
1;LMC;14,034;0,4077
1;LMC;17,5882;-0,6029
1;LMC;13,7698;0,4653
1;LMC;14,3287;0,3083
1;LMC;13,2086;0,6234
1;LMC;13,5732;0,546
1;LMC;15,48;-0,014
1;LMC;13,1248;0,6751
1;LMC;17,1166;-0,528
1;LMC;13,9133;0,4573
1;LMC;15,0072;0,1038
1;LMC;14,1087;0,3766
1;LMC;17,1206;-0,5551
1;LMC;14,6866;0,2054
1;LMC;13,4114;0,5868
1;LMC;15,8548;-0,1511
1;LMC;12,2802;0,6877
1;LMC;17,1984;-0,5196
1;LMC;13,2713;0,6421
1;LMC;14,537;0,2466
1;LMC;15,4264;0,0006
1;LMC;15,5466;-0,0351
1;LMC;14,5549;0,3135
1;LMC;14,8506;0,1502
1;LMC;15,1214;0,0971
1;LMC;14,0284;0,3934
1;LMC;13,0608;0,6455
1;LMC;14,4624;0,2676
1;LMC;15,2442;0,0527
1;LMC;13,9045;0,4276
1;LMC;14,0536;0,3947
1;LMC;14,0503;0,3833
1;LMC;14,2145;0,3506
1;LMC;14,3653;0,2799
1;LMC;12,2534;0,6564
1;LMC;13,4538;0,5395
1;LMC;16,7458;-0,3898
1;LMC;13,799;0,4515
1;LMC;14,3382;0,2787
1;LMC;13,6368;0,5072
1;LMC;13,4912;0,5308
1;LMC;14,8163;0,1739
1;LMC;13,8256;0,4412
1;LMC;14,3908;0,2858
1;LMC;14,9267;0,0972
1;LMC;14,5064;0,2072
1;LMC;13,899;0,4303
1;LMC;14,0764;0,3825
1;LMC;14,871;0,1848
1;LMC;14,8902;0,1544
1;LMC;14,1546;0,3697
1;LMC;14,7806;0,1531
1;LMC;15,3816;0,0162
1;LMC;14,1212;0,3378
1;LMC;14,6768;0,1847
1;LMC;14,229;0,3145
1;LMC;14,3439;0,2859
1;LMC;14,5225;0,183
1;LMC;14,222;0,3029
1;LMC;14,6786;0,2644
1;LMC;14,2882;0,3067
1;LMC;17,304;-0,4965
1;LMC;13,2234;0,6359
1;LMC;14,1998;0,341
1;LMC;16,9782;-0,4488
1;SMC;14,2801;0,5215
1;SMC;16,7184;-0,1413
1;SMC;15,6902;0,0745
1;SMC;16,1686;-0,057
1;SMC;14,6436;0,3746
1;SMC;16,573;-0,1489
1;SMC;15,4925;0,1575
1;SMC;15,0159;0,3255
1;SMC;15,5657;0,1226
1;SMC;14,3219;0,4484
1;SMC;16,5712;-0,1446
1;SMC;16,1988;-0,0829
1;SMC;15,4376;0,1613
1;SMC;13,6344;0,5874
1;SMC;14,3778;0,4716
1;SMC;14,2394;0,5057
1;SMC;15,8777;0,0206
1;SMC;16,7138;-0,1735
1;SMC;15,7367;0,0683
1;SMC;14,7922;0,3067
1;SMC;17,9934;-0,5486
1;SMC;14,1358;0,5249
1;SMC;14,8562;0,3176
1;SMC;15,5588;0,1312
1;SMC;14,3;0,5272
1;SMC;15,6038;0,0537
1;SMC;14,5812;0,4347
1;SMC;14,8804;0,3115
1;SMC;14,3614;0,4934
1;SMC;16,4298;-0,0449
1;SMC;15,8712;0,0365
1;SMC;14,3527;0,5141
1;SMC;15,639;0,0993
1;SMC;14,0709;0,4997
1;SMC;16,0837;0,0029
1;SMC;14,7445;0,4165
1;SMC;16,23;-0,0246
1;SMC;15,1252;0,2608
1;SMC;16,255;-0,043
1;SMC;15,4152;0,2079
1;SMC;15,6954;0,0998
1;SMC;14,8665;0,3692
1;SMC;15,7832;0,0378
1;SMC;14,8404;-0,2293
1;SMC;15,9228;0,0104
1;SMC;16,1484;0,0015
1;SMC;15,8728;0,0054
1;SMC;14,8986;0,2908
1;SMC;16,731;-0,2169
1;SMC;15,2766;0,1077
1;SMC;15,5933;0,0706
1;SMC;14,6399;0,3879
1;SMC;16,4613;-0,0989
1;SMC;15,1788;0,1832
1;SMC;16,2002;-0,0848
1;SMC;15,0008;0,2784
1;SMC;14,7586;0,2794
1;SMC;16,3034;-0,118
1;SMC;16,4006;-0,1251
1;SMC;15,849;-0,0155
1;SMC;16,3728;-0,0437
1;SMC;13,959;0,5954
1;SMC;15,9233;0,0135
1;SMC;15,1752;0,2438
1;SMC;14,8222;0,3179
1;SMC;16,0276;0,0558
1;SMC;15,2084;0,1235
1;SMC;16,3546;-0,1292
1;SMC;14,5508;0,4422
1;SMC;15,656;0,1128
1;SMC;15,2515;0,2473
1;SMC;15,8121;0,0231
1;SMC;15,6758;0,0838
1;SMC;16,729;-0,1389
1;SMC;16,2468;-0,126
1;SMC;13,9121;0,5834
1;SMC;14,368;0,4634
1;SMC;15,7206;0,0583
1;SMC;15,6693;0,0931
1;SMC;16,2687;-0,0599
1;SMC;15,0676;0,227
1;SMC;15,5143;0,1668
1;SMC;15,7076;0,0811
1;SMC;15,566;0,0386
1;SMC;16,1032;-0,0477
1;SMC;16,2852;-0,0936
1;SMC;13,9415;0,5344
1;SMC;13,7318;0,6038
1;SMC;14,6932;0,2731
1;SMC;17,5597;-0,4531
1;SMC;15,6816;0,0183
1;SMC;16,6984;-0,0744
1;SMC;15,0062;0,2869
1;SMC;15,8423;0,0837
1;SMC;15,6786;0,1166
1;SMC;14,6876;0,3651
1;SMC;15,5642;0,1374
1;SMC;16,8114;-0,1078
1;SMC;14,795;0,2782
1;SMC;14,2601;0,4012
1;SMC;16,4018;-0,1529
1;SMC;14,9727;0,2929
1;SMC;15,5267;0,1388
1;SMC;15,0455;0,2939
1;SMC;16,1594;-0,0279
1;SMC;15,6552;0,0574
1;SMC;14,4008;0,4278
1;SMC;16,1806;-0,0993
1;SMC;15,8383;0,0532
1;SMC;15,4704;0,1488
1;SMC;16,3872;-0,0714
1;SMC;14,7915;0,3349
1;SMC;13,9011;0,5528
1;SMC;16,5788;-0,1133
1;SMC;13,9728;0,5471
1;SMC;15,8312;0,048
1;SMC;15,696;0,0947
1;SMC;16,378;-0,0909
1;SMC;15,3721;0,1404
1;SMC;14,9808;0,2511
1;SMC;15,7881;0,0277
1;SMC;15,7657;0,0796
1;SMC;15,9406;0,0803
1;SMC;15,5712;0,1499
1;SMC;15,4664;0,1231
1;SMC;16,3175;-0,0522
1;SMC;15,4929;0,1124
1;SMC;13,5586;0,3835
1;SMC;16,205;-0,0705
1;SMC;15,55;0,08
1;SMC;17,5096;-0,2768
1;SMC;15,8832;0,0417
1;SMC;17,738;-0,542
1;SMC;14,5475;0,4257
1;SMC;15,4079;0,0751
1;SMC;16,2626;0,0103
1;SMC;14,5742;0,3754
1;SMC;16,521;-0,1554
1;SMC;16,791;-0,1832
1;SMC;15,4673;0,1727
1;SMC;14,2996;0,4629
1;SMC;13,6418;0,6525
1;SMC;15,7457;0,0729
1;SMC;15,4886;0,1447
1;SMC;14,7568;0,3357
1;SMC;15,482;0,1373
1;SMC;16,1634;-0,0447
1;SMC;15,7054;0,1234
1;SMC;14,5147;0,4154
1;SMC;15,0815;0,2683
1;SMC;15,992;-0,0153
1;SMC;14,3333;0,4373
1;SMC;15,3798;0,1507
1;SMC;15,957;-0,0025
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1;SMC;16,3458;-0,0707
1;SMC;15,565;0,17
1;SMC;15,0304;0,273
1;SMC;14,0869;0,4998
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1;SMC;16,144;-0,0551
1;SMC;15,5166;0,1347
1;SMC;14,3772;0,4966
1;SMC;15,8712;0,0196
1;SMC;14,6147;0,3938
1;SMC;16,7266;-0,1534
1;SMC;15,6266;0,1039
1;SMC;14,3126;0,4288
1;SMC;15,9238;-0,016
1;SMC;16,1556;-0,0916
1;SMC;14,6832;0,3555
1;SMC;14,9996;0,3125
1;SMC;14,8072;0,313
1;SMC;17,2238;-0,2249
1;SMC;14,2168;0,4893
1;SMC;16,0782;-0,0494
1;SMC;15,9124;0,0302
1;SMC;14,6897;0,3772
1;SMC;14,8998;0,317
1;SMC;14,3068;0,4708
1;SMC;14,9732;0,2529
1;SMC;16,1034;-0,0252
1;SMC;15,2416;0,2186
1;SMC;15,9578;-0,0056
1;SMC;14,605;0,3675
1;SMC;15,3892;0,1909
1;SMC;14,1306;0,5392
1;SMC;14,2198;0,4472
1;SMC;15,9806;0,1076
1;SMC;17,3222;-0,3888
1;SMC;14,8756;0,3077
1;SMC;16,4862;-0,1431
1;SMC;15,453;0,1643
1;SMC;15,719;0,105
1;SMC;15,0462;0,2544
1;SMC;14,3558;0,4541
1;SMC;13,7118;0,6472
1;SMC;14,9858;0,3054
1;SMC;14,7582;0,3293
1;SMC;15,8872;0,0343
1;SMC;14,2318;0,4783
1;SMC;15,7902;0,1023
1;SMC;15,7548;0,0084
1;SMC;16,3536;-0,1291
1;SMC;15,7356;0,0787
1;SMC;15,0988;0,2505
1;SMC;15,007;0,1926
1;SMC;15,0572;0,2629
1;SMC;15,4202;0,1177
1;SMC;14,5873;0,4062
1;SMC;14,274;0,472
1;SMC;15,953;0,032
1;SMC;15,1688;0,1666
1;SMC;15,4486;0,1694
1;SMC;16,2714;-0,084
1;SMC;14,1066;0,444
1;SMC;14,1883;0,4876
1;SMC;14,6876;0,3783
1;SMC;16,2804;-0,0307
1;SMC;16,004;0,0296
1;SMC;15,5427;0,0665
1;SMC;15,2691;0,1932
1;SMC;15,0723;0,2626
1;SMC;16,4086;-0,135
1;SMC;16,1279;-0,0629
1;SMC;14,6822;0,3247
1;SMC;16,1232;-0,1099
1;SMC;14,3967;0,4784
1;SMC;16,1678;-0,019
1;SMC;14,3868;0,4022
1;SMC;14,738;0,3264
1;SMC;15,8982;0,0036
1;SMC;16,0884;-0,0763
1;SMC;14,7889;0,3277
1;SMC;15,5037;0,1452
1;SMC;14,9974;0,3175
1;SMC;16,1114;-0,0793
1;SMC;15,5855;0,0736
1;SMC;15,1194;0,2507
1;SMC;15,1229;0,2498
1;SMC;15,5506;0,0998
1;SMC;15,8262;0,0085
1;SMC;17,6762;-0,4719
1;SMC;15,512;0,1091
1;SMC;15,1242;0,2304
1;SMC;14,8618;0,2606
1;SMC;15,8314;-0,0355
1;SMC;13,9661;0,5273
1;SMC;15,7528;0,0473
1;SMC;15,4834;0,1461
1;SMC;16,1654;0,0084
1;SMC;17,02;-0,0819
1;SMC;15,7764;0,0479
1;SMC;15,1877;0,2523
1;SMC;15,2879;0,1914
1;SMC;16,2964;-0,0454
1;SMC;15,5908;0,1223
1;SMC;15,6662;0,0394
1;SMC;15,5124;0,1418
1;SMC;14,876;0,2962
1;SMC;16,015;-0,0057
1;SMC;14,6491;0,4071
1;SMC;16,5376;-0,1862
1;SMC;16,4474;-0,1131
1;SMC;16,0558;0,0361
1;SMC;16,6338;-0,2435
1;SMC;18,2798;-0,5471
1;SMC;15,7256;0,0648
1;SMC;16,963;-0,2991
1;SMC;15,5069;0,1115
1;SMC;15,0298;0,1803
1;SMC;16,3346;-0,1174
1;SMC;14,794;0,3238
1;SMC;14,271;0,4877
1;SMC;15,9154;0,0438
1;SMC;16,5047;-0,1339
1;SMC;16,65;-0,1978
1;SMC;14,8017;0,3421
1;SMC;15,397;0,1778
1;SMC;16,8134;-0,2104
1;SMC;14,3519;0,421
1;SMC;14,6731;0,3168
1;SMC;15,2232;0,2349
1;SMC;14,6852;0,3608
1;SMC;14,9719;0,1979
1;SMC;15,1469;0,2306
1;SMC;15,2132;0,1439
1;SMC;14,788;0,3559
1;SMC;15,638;0,131
1;SMC;15,1227;0,1846
1;SMC;15,7846;-0,0333
1;SMC;16,1864;-0,0533
1;SMC;16,4067;-0,0201
1;SMC;16,7493;-0,236
1;SMC;16,5681;-0,2147
1;SMC;15,6974;0,0783
1;SMC;16,1395;-0,074
1;SMC;14,7655;0,3273
1;SMC;14,5638;0,3947
1;SMC;16,6594;-0,1952
1;SMC;16,1283;-0,0393
1;SMC;15,9034;0,0257
1;SMC;15,8515;0,0495
1;SMC;15,0717;0,3022
1;SMC;15,3598;0,1681
1;SMC;14,4274;0,4869
1;SMC;16,2396;-0,0553
1;SMC;16,082;-0,0294
1;SMC;14,8533;0,2512
1;SMC;14,6503;0,3586
1;SMC;16,1;-0,0353
1;SMC;15,6848;0,1708
1;SMC;15,9834;0,0201
1;SMC;14,3646;0,4274
1;SMC;15,285;0,1942
1;SMC;15,1247;0,2598
1;SMC;15,7448;0,0919
1;SMC;15,6758;0,1366
1;SMC;15,0902;0,226
1;SMC;14,0126;0,5439
1;SMC;15,9319;-0,082
1;SMC;15,0558;0,2398
1;SMC;14,5532;0,4375
1;SMC;14,8176;0,3557
1;SMC;15,1869;0,2378
1;SMC;14,5042;0,3989
1;SMC;14,7118;0,2721
1;SMC;14,5803;0,3939
1;SMC;15,4836;0,1186
1;SMC;15,2548;0,2071
1;SMC;15,5388;0,1499
1;SMC;15,507;0,1285
1;SMC;13,958;0,5414
1;SMC;16,4458;-0,0405
1;SMC;15,6919;0,0892
1;SMC;14,4196;0,4557
1;SMC;15,7577;0,03
1;SMC;16,382;-0,1317
1;SMC;14,456;0,4701
1;SMC;15,5165;0,0565
1;SMC;16,198;-0,0138
1;SMC;16,1511;-0,0355
1;SMC;14,3661;0,4568
1;SMC;15,088;0,2109
1;SMC;14,3802;0,4206
1;SMC;14,7786;0,2707
1;SMC;15,2855;0,3013
1;SMC;15,3114;0,1119
1;SMC;15,43;0,1134
1;SMC;16,1082;-0,0503
1;SMC;16,2348;-0,022
1;SMC;15,9953;-0,0417
1;SMC;15,2678;0,1952
1;SMC;15,1298;0,2325
1;SMC;15,1712;0,2456
1;SMC;15,5435;0,1342
1;SMC;15,8772;0,0307

A simple solution:
arrays=df.groupby(['Mode','Cloud']).apply(lambda grp : residual_function(grp))
residuals_value=[]
[residuals_value.extend(elem.tolist()) for elem in arrays]
df["residuals"]=residuals_value

How to convert independent output lists to a dataframe

Hope you are having a great weekend. My problem is as follows:
For my designed model i am getting the following predictions:
[0.3182012736797333, 0.6817986965179443, 0.5067878365516663, 0.49321213364601135, 0.4795221984386444, 0.520477831363678, 0.532780110836029, 0.46721988916397095, 0.3282901346683502, 0.6717098355293274]
[0.362120658159256, 0.6378793120384216, 0.5134761929512024, 0.4865237772464752, 0.46048662066459656, 0.539513349533081, 0.5342788100242615, 0.4657211899757385, 0.34932515025138855, 0.6506748199462891]
[0.3647380471229553, 0.6352618932723999, 0.5087167620658875, 0.49128326773643494, 0.4709164798259735, 0.5290834903717041, 0.5408024787902832, 0.4591975510120392, 0.37024226784706116, 0.6297577023506165]
[0.43765324354171753, 0.5623468160629272, 0.505147397518158, 0.49485257267951965, 0.45281311869621277, 0.5471869111061096, 0.5416161417961121, 0.45838382840156555, 0.3789178133010864, 0.6210821866989136]
[0.44772887229919434, 0.5522711277008057, 0.5119441151618958, 0.48805591464042664, 0.46322566270828247, 0.5367743372917175, 0.5402485132217407, 0.45975151658058167, 0.4145151972770691, 0.5854847431182861]
[0.35674020648002625, 0.6432597637176514, 0.48104971647262573, 0.5189502835273743, 0.4554695188999176, 0.54453045129776, 0.5409557223320007, 0.45904430747032166, 0.3258989453315735, 0.6741010546684265]
[0.3909384310245514, 0.609061598777771, 0.4915180504322052, 0.5084819793701172, 0.45033228397369385, 0.5496677160263062, 0.5267384052276611, 0.47326159477233887, 0.34493446350097656, 0.6550655364990234]
[0.32971733808517456, 0.6702827215194702, 0.5224012732505798, 0.47759872674942017, 0.4692566692829132, 0.5307433605194092, 0.5360044836997986, 0.4639955163002014, 0.41811054944992065, 0.5818894505500793]
[0.37096619606018066, 0.6290338039398193, 0.5165190100669861, 0.4834809899330139, 0.4739859998226166, 0.526013970375061, 0.5340168476104736, 0.46598318219184875, 0.3438771069049835, 0.6561229228973389]
[0.4189890921115875, 0.5810109376907349, 0.52749103307724, 0.47250890731811523, 0.44485437870025635, 0.5551456212997437, 0.5398098230361938, 0.46019014716148376, 0.3739124536514282, 0.6260875463485718]
[0.3979812562465668, 0.6020187139511108, 0.5050275325775146, 0.49497246742248535, 0.4653399884700775, 0.5346599817276001, 0.537341833114624, 0.4626581072807312, 0.33742010593414307, 0.6625799536705017]
[0.368088960647583, 0.631911039352417, 0.49925288558006287, 0.5007471442222595, 0.4547160863876343, 0.545283854007721, 0.5408452749252319, 0.45915472507476807, 0.4053747355937958, 0.5946252346038818]
As you can see they are independent lists. I want to convert these lists into a dataframe. Although they are independent, they are coming out of a for loop, so i cannot append them because they are not coming at once.

Use:
data = [[0.3182012736797333, 0.6817986965179443, 0.5067878365516663, 0.49321213364601135, 0.4795221984386444, 0.520477831363678, 0.532780110836029, 0.46721988916397095, 0.3282901346683502, 0.6717098355293274],
[0.362120658159256, 0.6378793120384216, 0.5134761929512024, 0.4865237772464752, 0.46048662066459656, 0.539513349533081, 0.5342788100242615, 0.4657211899757385, 0.34932515025138855, 0.6506748199462891],
[0.3647380471229553, 0.6352618932723999, 0.5087167620658875, 0.49128326773643494, 0.4709164798259735, 0.5290834903717041, 0.5408024787902832, 0.4591975510120392, 0.37024226784706116, 0.6297577023506165],
[0.43765324354171753, 0.5623468160629272, 0.505147397518158, 0.49485257267951965, 0.45281311869621277, 0.5471869111061096, 0.5416161417961121, 0.45838382840156555, 0.3789178133010864, 0.6210821866989136],
[0.44772887229919434, 0.5522711277008057, 0.5119441151618958, 0.48805591464042664, 0.46322566270828247, 0.5367743372917175, 0.5402485132217407, 0.45975151658058167, 0.4145151972770691, 0.5854847431182861],
[0.35674020648002625, 0.6432597637176514, 0.48104971647262573, 0.5189502835273743, 0.4554695188999176, 0.54453045129776, 0.5409557223320007, 0.45904430747032166, 0.3258989453315735, 0.6741010546684265],
[0.3909384310245514, 0.609061598777771, 0.4915180504322052, 0.5084819793701172, 0.45033228397369385, 0.5496677160263062, 0.5267384052276611, 0.47326159477233887, 0.34493446350097656, 0.6550655364990234],
[0.32971733808517456, 0.6702827215194702, 0.5224012732505798, 0.47759872674942017, 0.4692566692829132, 0.5307433605194092, 0.5360044836997986, 0.4639955163002014, 0.41811054944992065, 0.5818894505500793],
[0.37096619606018066, 0.6290338039398193, 0.5165190100669861, 0.4834809899330139, 0.4739859998226166, 0.526013970375061, 0.5340168476104736, 0.46598318219184875, 0.3438771069049835, 0.6561229228973389],
[0.4189890921115875, 0.5810109376907349, 0.52749103307724, 0.47250890731811523, 0.44485437870025635, 0.5551456212997437, 0.5398098230361938, 0.46019014716148376, 0.3739124536514282, 0.6260875463485718],
[0.3979812562465668, 0.6020187139511108, 0.5050275325775146, 0.49497246742248535, 0.4653399884700775, 0.5346599817276001, 0.537341833114624, 0.4626581072807312, 0.33742010593414307, 0.6625799536705017],
[0.368088960647583, 0.631911039352417, 0.49925288558006287, 0.5007471442222595, 0.4547160863876343, 0.545283854007721, 0.5408452749252319, 0.45915472507476807, 0.4053747355937958, 0.5946252346038818]]
# Create this before your for loop
df = pd.DataFrame(columns = range(10))
for pred_list in data:
#Add this within your for loop
df = df.append(pd.Series(pred_list), ignore_index=True)
output:

Error while loading model weights in pytorch

My model was in a .pth file and for loading the model i wrote this code.
model = torch.jit.load('/content/drive/MyDrive/fod.pth')
torch.save(model.state_dict(), 'weights.pt')
u2net = U2NETP()
u2net.eval()
u2net.load_state_dict(torch.load('/content/weights.pt'), strict = False)
U2NETP is the network architecture, but the problem here is that I am getting an error which goes like this
_IncompatibleKeys(missing_keys=['stage1.rebnconvin.bn_s1.running_mean', 'stage1.rebnconvin.bn_s1.running_var', 'stage1.rebnconv1.bn_s1.running_mean', 'stage1.rebnconv1.bn_s1.running_var', 'stage1.rebnconv2.bn_s1.running_mean', 'stage1.rebnconv2.bn_s1.running_var', 'stage1.rebnconv3.bn_s1.running_mean', 'stage1.rebnconv3.bn_s1.running_var', 'stage1.rebnconv4.bn_s1.running_mean', 'stage1.rebnconv4.bn_s1.running_var', 'stage1.rebnconv5.bn_s1.running_mean', 'stage1.rebnconv5.bn_s1.running_var', 'stage1.rebnconv6.bn_s1.running_mean', 'stage1.rebnconv6.bn_s1.running_var', 'stage1.rebnconv7.bn_s1.running_mean', 'stage1.rebnconv7.bn_s1.running_var', 'stage1.rebnconv6d.bn_s1.running_mean', 'stage1.rebnconv6d.bn_s1.running_var', 'stage1.rebnconv5d.bn_s1.running_mean', 'stage1.rebnconv5d.bn_s1.running_var', 'stage1.rebnconv4d.bn_s1.running_mean', 'stage1.rebnconv4d.bn_s1.running_var', 'stage1.rebnconv3d.bn_s1.running_mean', 'stage1.rebnconv3d.bn_s1.running_var', 'stage1.rebnconv2d.bn_s1.running_mean', 'stage1.rebnconv2d.bn_s1.running_var', 'stage1.rebnconv1d.bn_s1.running_mean', 'stage1.rebnconv1d.bn_s1.running_var', 'stage2.rebnconvin.bn_s1.running_mean', 'stage2.rebnconvin.bn_s1.running_var', 'stage2.rebnconv1.bn_s1.running_mean', 'stage2.rebnconv1.bn_s1.running_var', 'stage2.rebnconv2.bn_s1.running_mean', 'stage2.rebnconv2.bn_s1.running_var', 'stage2.rebnconv3.bn_s1.running_mean', 'stage2.rebnconv3.bn_s1.running_var', 'stage2.rebnconv4.bn_s1.running_mean', 'stage2.rebnconv4.bn_s1.running_var', 'stage2.rebnconv5.bn_s1.running_mean', 'stage2.rebnconv5.bn_s1.running_var', 'stage2.rebnconv6.bn_s1.running_mean', 'stage2.rebnconv6.bn_s1.running_var', 'stage2.rebnconv5d.bn_s1.running_mean', 'stage2.rebnconv5d.bn_s1.running_var', 'stage2.rebnconv4d.bn_s1.running_mean', 'stage2.rebnconv4d.bn_s1.running_var', 'stage2.rebnconv3d.bn_s1.running_mean', 'stage2.rebnconv3d.bn_s1.running_var', 'stage2.rebnconv2d.bn_s1.running_mean', 'stage2.rebnconv2d.bn_s1.running_var', 'stage2.rebnconv1d.bn_s1.running_mean', 'stage2.rebnconv1d.bn_s1.running_var', 'stage3.rebnconvin.bn_s1.running_mean', 'stage3.rebnconvin.bn_s1.running_var', 'stage3.rebnconv1.bn_s1.running_mean', 'stage3.rebnconv1.bn_s1.running_var', 'stage3.rebnconv2.bn_s1.running_mean', 'stage3.rebnconv2.bn_s1.running_var', 'stage3.rebnconv3.bn_s1.running_mean', 'stage3.rebnconv3.bn_s1.running_var', 'stage3.rebnconv4.bn_s1.running_mean', 'stage3.rebnconv4.bn_s1.running_var', 'stage3.rebnconv5.bn_s1.running_mean', 'stage3.rebnconv5.bn_s1.running_var', 'stage3.rebnconv4d.bn_s1.running_mean', 'stage3.rebnconv4d.bn_s1.running_var', 'stage3.rebnconv3d.bn_s1.running_mean', 'stage3.rebnconv3d.bn_s1.running_var', 'stage3.rebnconv2d.bn_s1.running_mean', 'stage3.rebnconv2d.bn_s1.running_var', 'stage3.rebnconv1d.bn_s1.running_mean', 'stage3.rebnconv1d.bn_s1.running_var', 'stage4.rebnconvin.bn_s1.running_mean', 'stage4.rebnconvin.bn_s1.running_var', 'stage4.rebnconv1.bn_s1.running_mean', 'stage4.rebnconv1.bn_s1.running_var', 'stage4.rebnconv2.bn_s1.running_mean', 'stage4.rebnconv2.bn_s1.running_var', 'stage4.rebnconv3.bn_s1.running_mean', 'stage4.rebnconv3.bn_s1.running_var', 'stage4.rebnconv4.bn_s1.running_mean', 'stage4.rebnconv4.bn_s1.running_var', 'stage4.rebnconv3d.bn_s1.running_mean', 'stage4.rebnconv3d.bn_s1.running_var', 'stage4.rebnconv2d.bn_s1.running_mean', 'stage4.rebnconv2d.bn_s1.running_var', 'stage4.rebnconv1d.bn_s1.running_mean', 'stage4.rebnconv1d.bn_s1.running_var', 'stage5.rebnconvin.bn_s1.running_mean', 'stage5.rebnconvin.bn_s1.running_var', 'stage5.rebnconv1.bn_s1.running_mean', 'stage5.rebnconv1.bn_s1.running_var', 'stage5.rebnconv2.bn_s1.running_mean', 'stage5.rebnconv2.bn_s1.running_var', 'stage5.rebnconv3.bn_s1.running_mean', 'stage5.rebnconv3.bn_s1.running_var', 'stage5.rebnconv4.bn_s1.running_mean', 'stage5.rebnconv4.bn_s1.running_var', 'stage5.rebnconv3d.bn_s1.running_mean', 'stage5.rebnconv3d.bn_s1.running_var', 'stage5.rebnconv2d.bn_s1.running_mean', 'stage5.rebnconv2d.bn_s1.running_var', 'stage5.rebnconv1d.bn_s1.running_mean', 'stage5.rebnconv1d.bn_s1.running_var', 'stage6.rebnconvin.bn_s1.running_mean', 'stage6.rebnconvin.bn_s1.running_var', 'stage6.rebnconv1.bn_s1.running_mean', 'stage6.rebnconv1.bn_s1.running_var', 'stage6.rebnconv2.bn_s1.running_mean', 'stage6.rebnconv2.bn_s1.running_var', 'stage6.rebnconv3.bn_s1.running_mean', 'stage6.rebnconv3.bn_s1.running_var', 'stage6.rebnconv4.bn_s1.running_mean', 'stage6.rebnconv4.bn_s1.running_var', 'stage6.rebnconv3d.bn_s1.running_mean', 'stage6.rebnconv3d.bn_s1.running_var', 'stage6.rebnconv2d.bn_s1.running_mean', 'stage6.rebnconv2d.bn_s1.running_var', 'stage6.rebnconv1d.bn_s1.running_mean', 'stage6.rebnconv1d.bn_s1.running_var', 'stage5d.rebnconvin.bn_s1.running_mean', 'stage5d.rebnconvin.bn_s1.running_var', 'stage5d.rebnconv1.bn_s1.running_mean', 'stage5d.rebnconv1.bn_s1.running_var', 'stage5d.rebnconv2.bn_s1.running_mean', 'stage5d.rebnconv2.bn_s1.running_var', 'stage5d.rebnconv3.bn_s1.running_mean', 'stage5d.rebnconv3.bn_s1.running_var', 'stage5d.rebnconv4.bn_s1.running_mean', 'stage5d.rebnconv4.bn_s1.running_var', 'stage5d.rebnconv3d.bn_s1.running_mean', 'stage5d.rebnconv3d.bn_s1.running_var', 'stage5d.rebnconv2d.bn_s1.running_mean', 'stage5d.rebnconv2d.bn_s1.running_var', 'stage5d.rebnconv1d.bn_s1.running_mean', 'stage5d.rebnconv1d.bn_s1.running_var', 'stage4d.rebnconvin.bn_s1.running_mean', 'stage4d.rebnconvin.bn_s1.running_var', 'stage4d.rebnconv1.bn_s1.running_mean', 'stage4d.rebnconv1.bn_s1.running_var', 'stage4d.rebnconv2.bn_s1.running_mean', 'stage4d.rebnconv2.bn_s1.running_var', 'stage4d.rebnconv3.bn_s1.running_mean', 'stage4d.rebnconv3.bn_s1.running_var', 'stage4d.rebnconv4.bn_s1.running_mean', 'stage4d.rebnconv4.bn_s1.running_var', 'stage4d.rebnconv3d.bn_s1.running_mean', 'stage4d.rebnconv3d.bn_s1.running_var', 'stage4d.rebnconv2d.bn_s1.running_mean', 'stage4d.rebnconv2d.bn_s1.running_var', 'stage4d.rebnconv1d.bn_s1.running_mean', 'stage4d.rebnconv1d.bn_s1.running_var', 'stage3d.rebnconvin.bn_s1.running_mean', 'stage3d.rebnconvin.bn_s1.running_var', 'stage3d.rebnconv1.bn_s1.running_mean', 'stage3d.rebnconv1.bn_s1.running_var', 'stage3d.rebnconv2.bn_s1.running_mean', 'stage3d.rebnconv2.bn_s1.running_var', 'stage3d.rebnconv3.bn_s1.running_mean', 'stage3d.rebnconv3.bn_s1.running_var', 'stage3d.rebnconv4.bn_s1.running_mean', 'stage3d.rebnconv4.bn_s1.running_var', 'stage3d.rebnconv5.bn_s1.running_mean', 'stage3d.rebnconv5.bn_s1.running_var', 'stage3d.rebnconv4d.bn_s1.running_mean', 'stage3d.rebnconv4d.bn_s1.running_var', 'stage3d.rebnconv3d.bn_s1.running_mean', 'stage3d.rebnconv3d.bn_s1.running_var', 'stage3d.rebnconv2d.bn_s1.running_mean', 'stage3d.rebnconv2d.bn_s1.running_var', 'stage3d.rebnconv1d.bn_s1.running_mean', 'stage3d.rebnconv1d.bn_s1.running_var', 'stage2d.rebnconvin.bn_s1.running_mean', 'stage2d.rebnconvin.bn_s1.running_var', 'stage2d.rebnconv1.bn_s1.running_mean', 'stage2d.rebnconv1.bn_s1.running_var', 'stage2d.rebnconv2.bn_s1.running_mean', 'stage2d.rebnconv2.bn_s1.running_var', 'stage2d.rebnconv3.bn_s1.running_mean', 'stage2d.rebnconv3.bn_s1.running_var', 'stage2d.rebnconv4.bn_s1.running_mean', 'stage2d.rebnconv4.bn_s1.running_var', 'stage2d.rebnconv5.bn_s1.running_mean', 'stage2d.rebnconv5.bn_s1.running_var', 'stage2d.rebnconv6.bn_s1.running_mean', 'stage2d.rebnconv6.bn_s1.running_var', 'stage2d.rebnconv5d.bn_s1.running_mean', 'stage2d.rebnconv5d.bn_s1.running_var', 'stage2d.rebnconv4d.bn_s1.running_mean', 'stage2d.rebnconv4d.bn_s1.running_var', 'stage2d.rebnconv3d.bn_s1.running_mean', 'stage2d.rebnconv3d.bn_s1.running_var', 'stage2d.rebnconv2d.bn_s1.running_mean', 'stage2d.rebnconv2d.bn_s1.running_var', 'stage2d.rebnconv1d.bn_s1.running_mean', 'stage2d.rebnconv1d.bn_s1.running_var', 'stage1d.rebnconvin.bn_s1.running_mean', 'stage1d.rebnconvin.bn_s1.running_var', 'stage1d.rebnconv1.bn_s1.running_mean', 'stage1d.rebnconv1.bn_s1.running_var', 'stage1d.rebnconv2.bn_s1.running_mean', 'stage1d.rebnconv2.bn_s1.running_var', 'stage1d.rebnconv3.bn_s1.running_mean', 'stage1d.rebnconv3.bn_s1.running_var', 'stage1d.rebnconv4.bn_s1.running_mean', 'stage1d.rebnconv4.bn_s1.running_var', 'stage1d.rebnconv5.bn_s1.running_mean', 'stage1d.rebnconv5.bn_s1.running_var', 'stage1d.rebnconv6.bn_s1.running_mean', 'stage1d.rebnconv6.bn_s1.running_var', 'stage1d.rebnconv7.bn_s1.running_mean', 'stage1d.rebnconv7.bn_s1.running_var', 'stage1d.rebnconv6d.bn_s1.running_mean', 'stage1d.rebnconv6d.bn_s1.running_var', 'stage1d.rebnconv5d.bn_s1.running_mean', 'stage1d.rebnconv5d.bn_s1.running_var', 'stage1d.rebnconv4d.bn_s1.running_mean', 'stage1d.rebnconv4d.bn_s1.running_var', 'stage1d.rebnconv3d.bn_s1.running_mean', 'stage1d.rebnconv3d.bn_s1.running_var', 'stage1d.rebnconv2d.bn_s1.running_mean', 'stage1d.rebnconv2d.bn_s1.running_var', 'stage1d.rebnconv1d.bn_s1.running_mean', 'stage1d.rebnconv1d.bn_s1.running_var'], unexpected_keys=[])
for param_tensor in sd:
print(param_tensor, "\t", model.state_dict()[param_tensor].size())
I used this code for printing the weights. Seems like it contains weights and bias keys but not running mean/ variance

Why values is not normalized in certain array?

I have an array with certain values that I want normalize from 0 to 1. When I try to do it with minmax_scale function it returns the same set.
[2.80756379e-01 9.47215085e-01 2.98665545e-01 2.71729701e-01
9.53844447e-01 4.09155122e-01 7.73782687e-01 4.04866838e-01...]
If I slice it to 200 values (set has 400 values) function returns normalized values:
[0.28024599 0.94726674 0.29817026 0.2712117 0.95390169 0.40875301
0.77368808 0.40446111 0.0427401 0.98420903...]
if I generate for example 600 test values, it works fine.
[0. 0.00166945 0.0033389 0.00500835 0.0066778 … 0.9933222 0.99499165
0.9966611 0.99833055 1.]
So I assume that something wrong with my input, but it doesn’t raise any error that I could understand what is happening and why values haven’t been normalized.
So if someone could shed light on this and explain what I am doing wrong.
import numpy as np
from sklearn.preprocessing import minmax_scale
x = [3.4983787694629807, 6.8238036546277545, 3.5877400241270765, 3.4533384061573176, 6.856882141805892, 4.139049184316268, 5.958429090197188, 4.11765195576695, 2.314295679864955, 7.007979076325537, 4.591575092528983, 6.500165465269498, 2.8914665615540556, 3.543340276730918, 6.561891009978831, 3.6289541084867656, 3.5071777128342223, 7.056680192241884, 3.4788374494994114, 3.4464924916748862, 7.014214072992434, 3.4916155718006134, 3.5360046270365486, 7.086001703906676, 3.612050358553576, 3.896165015866581, 6.09922605707944, 3.3460547945703287, 3.910944920634269, 5.971772677577327, 5.178500485785331, 6.6090028576236675, 2.841807831193129, 5.149220347017544, 4.735677896132761, 3.8354921976610226, 7.038466835269509, 3.587747218864995, 3.555693227090981, 6.954475444783193, 3.437365707196104, 3.4325883202929894, 7.020900371706621, 3.4368379490007386, 3.475833945795732, 6.8688992156695585, 3.583554218078379, 3.520770727926182, 6.92133265995129, 4.044292336171232, 5.602564848428526, 4.411557629422499, 2.45924872382912, 7.086402867274765, 4.7372741724391965, 6.374350060657951, 2.6542178607167273, 3.508325152205329, 6.461025102348145, 3.6812426559272033, 3.5307800915694383, 7.066468458976694, 3.493276512558132, 3.4568759587746243, 7.0176506092750595, 3.478361485227409, 3.5201937331883713, 7.080979349338214, 3.5795505246747212, 3.8031183328096256, 6.245609213027018, 3.4165971380697018, 2.1723738636075023, 6.126159977852875, 5.013068437451677, 6.904030395006403, 2.7075477902159992, 4.895577020527206, 5.020958429692195, 3.9029736137362567, 7.005502917949908, 3.5789396294713756, 3.5676476014611818, 6.929131913377069, 3.4472779998622487, 3.4308686427915935, 7.018771879565203, 3.432009619429056, 3.457840956435269, 6.905877831610167, 3.5755167560389163, 3.5618219034900966, 6.97358473363722, 3.9600759412714175, 5.260824000465443, 4.692774833624142, 2.602129482124068, 7.035148649249085, 4.8915972720677825, 6.2369642563916825, 2.3878063740977975, 3.4610250988299063, 6.344643530169994, 3.745077609377894, 3.557344813692928, 7.0753891572282885, 3.5084174545346944, 3.4687392098349816, 7.018578929608326, 3.4657417965161157, 3.504674479158238, 7.073344198878297, 3.5505846727930717, 3.728275930579232, 6.374512212337401, 3.473738663253011, 2.455000320545692, 6.271460848569801, 4.853259564210921, 7.060300163815499, 2.5675976179261544, 4.625519502757723, 5.34193581659188, 3.9796698149770413, 6.961928522596542, 3.553928370489732, 3.577760192959362, 6.897549440276217, 3.4618068657264773, 3.4328376059178547, 7.019069392542759, 3.430955309222881, 3.4444356247624213, 6.935848551405397, 3.5648686589574554, 3.5823723667040626, 7.0144769584373465, 3.885706919437764, 4.947869760566333, 4.958988699542243, 2.740882010237675, 6.844670575188505, 5.052957262016582, 6.08929327319099, 2.1012153486394873, 3.4006432713737014, 6.211570326896137, 3.823951658625252, 3.5871290770943087, 7.082486008656476, 3.524032336877465, 3.481547522774735, 7.017048732257519, 3.454184925027273, 3.4896816808661573, 7.064127863850646, 3.5247589190924855, 3.6675470038864804, 6.486998043891378, 3.5179550925822936, 2.7150317741174828, 6.406130756762303, 4.700905963998837, 7.0786411289315705, 2.424024165788486, 4.340976405808333, 5.68871874308043, 4.066399458564197, 6.906772355480829, 3.5069849107646553, 3.584982183669687, 6.8586979941775175, 3.4809101839812273, 3.4385861350276734, 7.021776708035995, 3.433539453335249, 3.435646282061486, 6.959814531248965, 3.5525938762287192, 3.5880328691774226, 7.04501753482764, 3.8203341873417145, 4.672129393281222, 5.208396760624669, 2.873447040265478, 6.517042667648001, 5.219450800175008, 5.932960908282111, 4.071807320428156, 3.32681370883367, 6.060786017704781, 3.922140073470534, 3.6205701108946693, 7.086704780969265, 3.5398647239459082, 3.4949072952266786, 7.012978275363125, 3.4442572447205126, 3.4754567104312315, 7.054260858195003, 3.501860081901249, 3.6176350355084987, 6.584272251688416, 3.550139011669634, 2.9441212137915196, 6.528971419532436, 4.557509169353122, 6.971230117604573, 2.2788693414108083, 4.044384223064448, 6.045075381817579, 4.16380702973696, 6.8392444953122915, 3.4322587777495, 3.588015280869961, 6.811444662244569, 3.504571583212776, 3.448038447176144, 7.0267448030100095, 3.4394438861946495, 3.4313813610293606, 6.978663067682745, 3.539410445197603, 3.5836697468529928, 7.066336743389743, 3.763013423804524, 4.436678180031973, 5.439712464353549, 2.9978564390796802, 6.06598407921124, 5.38892971887227, 5.769885855777033, 4.7305353792164615, 3.2396389326858333, 5.891487403995336, 4.044576070907139, 3.658273834104114, 7.086908629948878, 3.5554434237079002, 3.508553789136474, 7.006154398318428, 3.4366257440898904, 3.462343969119664, 7.044561860711188, 3.4818481341401735, 3.576035272214573, 6.667629115969775, 3.5715246418069957, 3.1369130300148993, 6.639150212621939, 4.424195703276443, 6.759495560732593, 2.134069612887043, 3.7386525746914123, 6.388620586154337, 4.272297018941626, 6.7587843489580965, 3.324494111518356, 3.5853639214122595, 6.754568772053236, 3.5329516319997514, 3.4610002607293953, 7.033692354375017, 3.4481972797191154, 3.4313661148467935, 6.993158859550646, 3.5257969569299723, 3.5731476929367916, 7.07964197799761, 3.712767027132367, 4.2405670182736745, 5.652147101651347, 3.112331986409608, 5.515185072672031, 5.559088312172956, 5.602219157421699, 5.362655122965983, 3.1396940731308915, 5.703149958832117, 4.196548195898355, 3.700992351744346, 7.081897811873345, 3.569845801358458, 3.5223021303817146, 6.996232651966579, 3.4319885789163944, 3.450813363928631, 7.035729967483905, 3.464829989158296, 3.5409776462526583, 6.738403993391335, 3.583589535854453, 3.2912893051822354, 6.7362012208338795, 4.301697490919809, 6.470822564075378, 3.659773459738205, 2.0974903963829354, 6.692161174433521, 4.3919729995424195, 6.665106087684514, 3.1798600950044777, 3.5754169970262786, 6.686782075167818, 3.566547285071714, 3.47722596516465, 7.042206860585631, 3.459238171506453, 3.435134332592052, 7.003939734793729, 3.512049727586488, 3.559270908983708, 7.086176708003556, 3.6686375261134057, 4.080241002256502, 5.84537422915953, 3.215380734121881, 4.89528857023289, 5.727559855711503, 5.432265203060416, 5.934141424807943, 3.027998394822877, 5.495590110107973, 4.3831413048066254, 3.7495930218165427, 7.070434254364885, 3.5814467824887473, 3.535972176884382, 6.982737736524654, 3.43098967364103, 3.441422724579513, 7.028339316715845, 3.451013794888052, 3.5113341674962717, 6.797932388511367, 3.587942414572208, 3.4081496049032274, 6.82001018784346, 4.190357260509626, 6.134736264429092, 3.967315538885193, 2.2419098299323275, 6.926226800256952, 4.522587633421751, 6.558238517691441, 2.9967711785804605, 3.556550927861764, 6.606754741200447, 3.6063414285217674, 3.496491069547794, 7.05174661557621, 3.471997346906242, 3.4420693538840723, 7.011514159064076, 3.4983614083674994, 3.5438804663925714, 7.0871842215570755, 3.629734022986171, 3.9507935039967084, 6.019481038594121, 3.3058784855515917, 4.240129283449884, 5.892015134812656, 5.262390436269602, 6.413168227114006, 2.905958254165465, 5.269023998291438, 4.608362850137262, 3.8050205047007704, 7.051271296451422, 3.5877015700240324, 3.5493020337345564, 6.965064567979088, 3.434139087088657, 3.4347436167612146, 7.022835521591056, 3.4406465746514856, 3.4864976824344196, 6.847515844073905, 3.58620796770146, 3.4908273530643696, 6.890786354350282, 4.090156575701794, 5.77929297599503, 4.2665273948822655, 2.387152222353216, 7.062521670699347, 4.663509517553511, 6.438556051230168, 2.776539669077205, 3.52724382177749, 6.513147765418067, 3.6539307251684234, 3.5186578165204065, 7.061643896805739, 3.4859770491431257, 3.4514785509234778, 7.016259943745782, 3.4849078166839633, 3.528054396333257]
xa = np.array(x)
q = minmax_scale(xa)
# Not normalized
print(q)
d = minmax_scale(xa[:200])
# Normalized
print(d)
za = np.arange(600)
z = minmax_scale(za)
# Normalized
print(z)

You're probably making some wrong assumptions here, I can show you:
>> import numpy as np
>> from sklearn.preprocessing import minmax_scale
>> x = [3.4983787694629807, 6.8238036546277545, 3.5877400241270765, 3.4533384061573176, 6.856882141805892, 4.139049184316268, 5.958429090197188, 4.11765195576695, 2.314295679864955, 7.007979076325537, 4.591575092528983, 6.500165465269498, 2.8914665615540556, 3.543340276730918, 6.561891009978831, 3.6289541084867656, 3.5071777128342223, 7.056680192241884, 3.4788374494994114, 3.4464924916748862, 7.014214072992434, 3.4916155718006134, 3.5360046270365486, 7.086001703906676, 3.612050358553576, 3.896165015866581, 6.09922605707944, 3.3460547945703287, 3.910944920634269, 5.971772677577327, 5.178500485785331, 6.6090028576236675, 2.841807831193129, 5.149220347017544, 4.735677896132761, 3.8354921976610226, 7.038466835269509, 3.587747218864995, 3.555693227090981, 6.954475444783193, 3.437365707196104, 3.4325883202929894, 7.020900371706621, 3.4368379490007386, 3.475833945795732, 6.8688992156695585, 3.583554218078379, 3.520770727926182, 6.92133265995129, 4.044292336171232, 5.602564848428526, 4.411557629422499, 2.45924872382912, 7.086402867274765, 4.7372741724391965, 6.374350060657951, 2.6542178607167273, 3.508325152205329, 6.461025102348145, 3.6812426559272033, 3.5307800915694383, 7.066468458976694, 3.493276512558132, 3.4568759587746243, 7.0176506092750595, 3.478361485227409, 3.5201937331883713, 7.080979349338214, 3.5795505246747212, 3.8031183328096256, 6.245609213027018, 3.4165971380697018, 2.1723738636075023, 6.126159977852875, 5.013068437451677, 6.904030395006403, 2.7075477902159992, 4.895577020527206, 5.020958429692195, 3.9029736137362567, 7.005502917949908, 3.5789396294713756, 3.5676476014611818, 6.929131913377069, 3.4472779998622487, 3.4308686427915935, 7.018771879565203, 3.432009619429056, 3.457840956435269, 6.905877831610167, 3.5755167560389163, 3.5618219034900966, 6.97358473363722, 3.9600759412714175, 5.260824000465443, 4.692774833624142, 2.602129482124068, 7.035148649249085, 4.8915972720677825, 6.2369642563916825, 2.3878063740977975, 3.4610250988299063, 6.344643530169994, 3.745077609377894, 3.557344813692928, 7.0753891572282885, 3.5084174545346944, 3.4687392098349816, 7.018578929608326, 3.4657417965161157, 3.504674479158238, 7.073344198878297, 3.5505846727930717, 3.728275930579232, 6.374512212337401, 3.473738663253011, 2.455000320545692, 6.271460848569801, 4.853259564210921, 7.060300163815499, 2.5675976179261544, 4.625519502757723, 5.34193581659188, 3.9796698149770413, 6.961928522596542, 3.553928370489732, 3.577760192959362, 6.897549440276217, 3.4618068657264773, 3.4328376059178547, 7.019069392542759, 3.430955309222881, 3.4444356247624213, 6.935848551405397, 3.5648686589574554, 3.5823723667040626, 7.0144769584373465, 3.885706919437764, 4.947869760566333, 4.958988699542243, 2.740882010237675, 6.844670575188505, 5.052957262016582, 6.08929327319099, 2.1012153486394873, 3.4006432713737014, 6.211570326896137, 3.823951658625252, 3.5871290770943087, 7.082486008656476, 3.524032336877465, 3.481547522774735, 7.017048732257519, 3.454184925027273, 3.4896816808661573, 7.064127863850646, 3.5247589190924855, 3.6675470038864804, 6.486998043891378, 3.5179550925822936, 2.7150317741174828, 6.406130756762303, 4.700905963998837, 7.0786411289315705, 2.424024165788486, 4.340976405808333, 5.68871874308043, 4.066399458564197, 6.906772355480829, 3.5069849107646553, 3.584982183669687, 6.8586979941775175, 3.4809101839812273, 3.4385861350276734, 7.021776708035995, 3.433539453335249, 3.435646282061486, 6.959814531248965, 3.5525938762287192, 3.5880328691774226, 7.04501753482764, 3.8203341873417145, 4.672129393281222, 5.208396760624669, 2.873447040265478, 6.517042667648001, 5.219450800175008, 5.932960908282111, 4.071807320428156, 3.32681370883367, 6.060786017704781, 3.922140073470534, 3.6205701108946693, 7.086704780969265, 3.5398647239459082, 3.4949072952266786, 7.012978275363125, 3.4442572447205126, 3.4754567104312315, 7.054260858195003, 3.501860081901249, 3.6176350355084987, 6.584272251688416, 3.550139011669634, 2.9441212137915196, 6.528971419532436, 4.557509169353122, 6.971230117604573, 2.2788693414108083, 4.044384223064448, 6.045075381817579, 4.16380702973696, 6.8392444953122915, 3.4322587777495, 3.588015280869961, 6.811444662244569, 3.504571583212776, 3.448038447176144, 7.0267448030100095, 3.4394438861946495, 3.4313813610293606, 6.978663067682745, 3.539410445197603, 3.5836697468529928, 7.066336743389743, 3.763013423804524, 4.436678180031973, 5.439712464353549, 2.9978564390796802, 6.06598407921124, 5.38892971887227, 5.769885855777033, 4.7305353792164615, 3.2396389326858333, 5.891487403995336, 4.044576070907139, 3.658273834104114, 7.086908629948878, 3.5554434237079002, 3.508553789136474, 7.006154398318428, 3.4366257440898904, 3.462343969119664, 7.044561860711188, 3.4818481341401735, 3.576035272214573, 6.667629115969775, 3.5715246418069957, 3.1369130300148993, 6.639150212621939, 4.424195703276443, 6.759495560732593, 2.134069612887043, 3.7386525746914123, 6.388620586154337, 4.272297018941626, 6.7587843489580965, 3.324494111518356, 3.5853639214122595, 6.754568772053236, 3.5329516319997514, 3.4610002607293953, 7.033692354375017, 3.4481972797191154, 3.4313661148467935, 6.993158859550646, 3.5257969569299723, 3.5731476929367916, 7.07964197799761, 3.712767027132367, 4.2405670182736745, 5.652147101651347, 3.112331986409608, 5.515185072672031, 5.559088312172956, 5.602219157421699, 5.362655122965983, 3.1396940731308915, 5.703149958832117, 4.196548195898355, 3.700992351744346, 7.081897811873345, 3.569845801358458, 3.5223021303817146, 6.996232651966579, 3.4319885789163944, 3.450813363928631, 7.035729967483905, 3.464829989158296, 3.5409776462526583, 6.738403993391335, 3.583589535854453, 3.2912893051822354, 6.7362012208338795, 4.301697490919809, 6.470822564075378, 3.659773459738205, 2.0974903963829354, 6.692161174433521, 4.3919729995424195, 6.665106087684514, 3.1798600950044777, 3.5754169970262786, 6.686782075167818, 3.566547285071714, 3.47722596516465, 7.042206860585631, 3.459238171506453, 3.435134332592052, 7.003939734793729, 3.512049727586488, 3.559270908983708, 7.086176708003556, 3.6686375261134057, 4.080241002256502, 5.84537422915953, 3.215380734121881, 4.89528857023289, 5.727559855711503, 5.432265203060416, 5.934141424807943, 3.027998394822877, 5.495590110107973, 4.3831413048066254, 3.7495930218165427, 7.070434254364885, 3.5814467824887473, 3.535972176884382, 6.982737736524654, 3.43098967364103, 3.441422724579513, 7.028339316715845, 3.451013794888052, 3.5113341674962717, 6.797932388511367, 3.587942414572208, 3.4081496049032274, 6.82001018784346, 4.190357260509626, 6.134736264429092, 3.967315538885193, 2.2419098299323275, 6.926226800256952, 4.522587633421751, 6.558238517691441, 2.9967711785804605, 3.556550927861764, 6.606754741200447, 3.6063414285217674, 3.496491069547794, 7.05174661557621, 3.471997346906242, 3.4420693538840723, 7.011514159064076, 3.4983614083674994, 3.5438804663925714, 7.0871842215570755, 3.629734022986171, 3.9507935039967084, 6.019481038594121, 3.3058784855515917, 4.240129283449884, 5.892015134812656, 5.262390436269602, 6.413168227114006, 2.905958254165465, 5.269023998291438, 4.608362850137262, 3.8050205047007704, 7.051271296451422, 3.5877015700240324, 3.5493020337345564, 6.965064567979088, 3.434139087088657, 3.4347436167612146, 7.022835521591056, 3.4406465746514856, 3.4864976824344196, 6.847515844073905, 3.58620796770146, 3.4908273530643696, 6.890786354350282, 4.090156575701794, 5.77929297599503, 4.2665273948822655, 2.387152222353216, 7.062521670699347, 4.663509517553511, 6.438556051230168, 2.776539669077205, 3.52724382177749, 6.513147765418067, 3.6539307251684234, 3.5186578165204065, 7.061643896805739, 3.4859770491431257, 3.4514785509234778, 7.016259943745782, 3.4849078166839633, 3.528054396333257]
>> xa = np.array(x)
>> q = minmax_scale(xa)
>> x_set = set(x)
>> q_set = set(q)
>> x_set.intersection(q_set) == set()
True
As you can see, there's no intersection, it's definitely not returning the same set...

Cumprod giving -inf in Python

I am trying to compute cumulative product of the following data set.
Date Random data
1/2/2006 2.372388507
1/3/2006 2.792095479
1/4/2006 4.153345633
1/5/2006 1.209302413
1/6/2006 3.308908843
1/9/2006 5.609288688
1/10/2006 5.148763856
1/11/2006 4.963421605
1/12/2006 4.031740124
1/13/2006 5.475643588
1/16/2006 5.310478512
1/17/2006 5.231183268
1/18/2006 7.33295124
1/19/2006 7.086467341
1/20/2006 6.953441702
1/23/2006 6.881551417
1/24/2006 6.720592121
1/25/2006 4.375483647
1/26/2006 2.824165469
1/27/2006 2.830542833
1/30/2006 3.735049499
1/31/2006 3.147491688
2/1/2006 1.414748374
2/2/2006 -0.051161849
2/3/2006 -0.180186506
2/6/2006 1.660894524
2/7/2006 2.709409323
2/8/2006 1.972035231
2/9/2006 -0.782625682
2/10/2006 -1.901299484
2/13/2006 -2.141229007
2/14/2006 -2.639233019
2/15/2006 -4.95219641
2/16/2006 -6.568204721
2/17/2006 -5.671892621
2/20/2006 -5.989308797
2/21/2006 -5.519832515
2/22/2006 -4.123507939
2/23/2006 -4.840716254
2/24/2006 -3.393895281
2/27/2006 -1.579450628
2/28/2006 -5.715894843
3/1/2006 -4.818584424
3/2/2006 -5.306398625
3/3/2006 -3.773552658
3/6/2006 -1.782726837
3/7/2006 -2.421770003
3/8/2006 -2.032466154
3/9/2006 -3.24379646
3/10/2006 0.267982805
3/13/2006 0.014589559
3/14/2006 1.343058431
3/15/2006 1.539251495
3/16/2006 -0.350651804
3/17/2006 -0.215041321
3/20/2006 0.578951429
3/21/2006 -0.576824159
3/22/2006 1.881264415
3/23/2006 2.714386498
3/24/2006 4.111298817
3/27/2006 5.020309083
3/28/2006 4.532650354
3/29/2006 7.245341261
3/30/2006 8.111802803
3/31/2006 4.558323469
4/3/2006 6.252751308
4/4/2006 8.314806951
4/5/2006 5.777692349
4/6/2006 6.725161553
4/7/2006 4.794367906
4/10/2006 5.743532122
4/11/2006 7.290548166
4/12/2006 5.903857018
4/13/2006 4.77936565
4/14/2006 5.674446806
4/17/2006 5.88485792
4/18/2006 6.078651917
4/19/2006 4.917405394
4/20/2006 4.868584712
4/21/2006 3.526253732
4/24/2006 5.124797759
4/25/2006 3.884862865
4/26/2006 4.369885748
4/27/2006 1.234703037
4/28/2006 -1.67674986
5/1/2006 -2.711339347
5/2/2006 -2.574835748
5/3/2006 -3.532974512
5/4/2006 -4.361912086
5/5/2006 -9.136912315
5/8/2006 -8.945826752
5/9/2006 -7.804639384
5/10/2006 -10.05905437
5/11/2006 -9.254733416
5/12/2006 -8.382467816
5/15/2006 -7.718500019
5/16/2006 -10.04179082
5/17/2006 -10.90960283
5/18/2006 -7.538484374
5/19/2006 -6.915045472
5/22/2006 -8.49018374
5/23/2006 -10.84341146
5/24/2006 -4.739280009
5/25/2006 -8.906757979
5/26/2006 -10.61262457
5/29/2006 -9.636827323
5/30/2006 -8.353511534
5/31/2006 -10.1389515
6/1/2006 -10.0339179
6/2/2006 -10.84551313
6/5/2006 -8.628081538
6/6/2006 -6.657905529
6/7/2006 -6.395791873
6/8/2006 -7.676135515
6/9/2006 -7.225332776
6/12/2006 -5.721847599
6/13/2006 -9.168934478
6/14/2006 -8.522434172
6/15/2006 -9.344608517
6/16/2006 -9.492790802
6/19/2006 -6.27304367
6/20/2006 -7.748707965
6/21/2006 -5.216536389
6/22/2006 -5.866333313
6/23/2006 -3.421767661
6/26/2006 -0.817150639
6/27/2006 1.566919066
6/28/2006 2.1756715
6/29/2006 2.003892417
6/30/2006 0.145706902
7/3/2006 4.825841191
7/4/2006 2.984194983
7/5/2006 2.733606852
7/6/2006 3.990344988
7/7/2006 4.464159978
7/10/2006 2.181922905
7/11/2006 4.207532649
7/12/2006 5.893857763
7/13/2006 6.696591003
7/14/2006 8.02397588
7/17/2006 7.18005379
7/18/2006 7.110823813
7/19/2006 4.604122492
7/20/2006 4.383075987
7/21/2006 4.734463235
7/24/2006 5.60625391
7/25/2006 7.453657745
7/26/2006 6.7147771
7/27/2006 5.255477178
7/28/2006 6.638942489
7/31/2006 5.514850947
8/1/2006 6.666282084
8/2/2006 6.037577365
8/3/2006 6.434382521
8/4/2006 5.80948075
8/7/2006 5.667054317
8/8/2006 5.175715003
8/9/2006 4.94937506
8/10/2006 3.558925269
8/11/2006 4.031802401
8/14/2006 3.272287286
8/15/2006 4.289470879
8/16/2006 3.538103725
8/17/2006 2.762386707
8/18/2006 2.114880041
8/21/2006 5.068950919
8/22/2006 2.483874694
8/23/2006 1.730699516
8/24/2006 -0.675212673
8/25/2006 0.187110629
8/28/2006 0.344282156
8/29/2006 0.01723009
8/30/2006 -0.327127005
8/31/2006 0.016483468
9/1/2006 -0.973496098
9/4/2006 -1.218588549
9/5/2006 -0.20940671
9/6/2006 0.25023559
9/7/2006 -2.986442703
9/8/2006 -2.073033591
9/11/2006 1.390003709
9/12/2006 2.940760338
9/13/2006 2.403386183
9/14/2006 2.349487863
9/15/2006 1.899995646
9/18/2006 3.50536463
9/19/2006 2.83392064
9/20/2006 2.571588424
9/21/2006 3.118297653
9/22/2006 -0.377687298
9/25/2006 -2.391993686
9/26/2006 0.712594429
9/27/2006 1.457682028
9/28/2006 1.474114727
9/29/2006 0.446453108
10/2/2006 3.007973689
10/3/2006 -2.43263121
10/4/2006 0.86295345
10/5/2006 4.664733649
10/6/2006 4.558573046
10/9/2006 4.680665577
10/10/2006 4.575158956
10/11/2006 6.425144162
10/12/2006 8.372432637
10/13/2006 8.182474544
10/16/2006 8.968786366
10/17/2006 9.463661551
10/18/2006 8.512907068
10/19/2006 5.873743873
10/20/2006 3.369445264
10/23/2006 1.030307363
10/24/2006 5.528218034
10/25/2006 4.772900213
10/26/2006 4.780839053
10/27/2006 4.908377081
10/30/2006 1.949064709
10/31/2006 1.237048868
11/1/2006 -0.784592691
11/2/2006 2.737788889
11/3/2006 0.575772221
11/6/2006 0.756429404
11/7/2006 3.470072539
11/8/2006 3.162250037
11/9/2006 3.530282875
11/10/2006 3.101909259
11/13/2006 3.850635629
11/14/2006 5.765932269
11/15/2006 6.872396495
11/16/2006 7.65256188
11/17/2006 7.665129818
11/20/2006 7.688611466
11/21/2006 10.98556762
11/22/2006 10.3474519
11/23/2006 8.307676877
11/24/2006 6.809710616
11/27/2006 3.833060531
11/28/2006 2.194899225
11/29/2006 2.753858429
11/30/2006 7.843689893
12/1/2006 7.960285607
12/4/2006 8.693168009
12/5/2006 6.942631629
12/6/2006 7.571515106
12/7/2006 9.703434772
12/8/2006 9.330900226
12/11/2006 10.07080936
12/12/2006 8.823865383
12/13/2006 9.142372346
12/14/2006 11.4249828
12/15/2006 13.4976679
12/18/2006 16.02891813
12/19/2006 13.57689804
12/20/2006 13.08135113
12/21/2006 11.35585478
12/22/2006 11.56407075
12/25/2006 12.55729202
12/26/2006 12.74006864
12/27/2006 12.80879851
12/28/2006 12.78104782
12/29/2006 10.84853655
1/1/2007 12.34247778
1/2/2007 12.4083186
1/3/2007 12.05157619
1/4/2007 13.31470937
1/5/2007 13.08023063
1/8/2007 11.8083914
1/9/2007 12.14102299
1/10/2007 12.78561441
1/11/2007 10.5599935
1/12/2007 9.670640578
1/15/2007 7.5265463
1/16/2007 5.785317873
1/17/2007 6.421764885
1/18/2007 6.13308998
1/19/2007 4.502378909
1/22/2007 5.18285115
1/23/2007 6.651267567
1/24/2007 9.669499091
1/25/2007 9.873389316
1/26/2007 8.512393515
1/29/2007 8.17935067
1/30/2007 7.565247724
1/31/2007 10.26027855
2/1/2007 12.21138996
2/2/2007 11.0873071
2/5/2007 15.28502878
2/6/2007 13.68842955
2/7/2007 13.27807961
2/8/2007 12.83276901
2/9/2007 13.80840316
2/12/2007 10.40760837
2/13/2007 8.706916548
2/14/2007 7.062821439
2/15/2007 6.720750572
2/16/2007 5.181412914
2/19/2007 6.377711852
2/20/2007 6.777151257
2/21/2007 7.213968623
2/22/2007 5.717975255
2/23/2007 7.535619266
2/26/2007 6.226846924
2/27/2007 6.420469572
2/28/2007 7.825909152
3/1/2007 9.322928614
3/2/2007 10.09251084
3/5/2007 10.01940332
3/6/2007 8.500303192
3/7/2007 8.276245994
3/8/2007 8.618637579
3/9/2007 9.130646064
3/12/2007 5.00975105
3/13/2007 3.332393527
3/14/2007 1.483827461
3/15/2007 3.638824916
3/16/2007 3.461569024
3/19/2007 2.163572694
3/20/2007 2.369195247
3/21/2007 -3.015080521
3/22/2007 -2.584981113
3/23/2007 1.616297026
3/26/2007 3.424344952
3/27/2007 3.512629879
3/28/2007 1.565564369
3/29/2007 3.155875359
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import pandas as pd
df=read_csv("Randomdata.csv")
df2=df.add(1).cumprod()
I tried another method too.
df3=1+df.cumprod()
Both are yielding -inf values. It's happening for this data set specifically.
Please suggest the way forward.

It looks like you are trying to calculate cumulative returns. If so, those numbers are not in returns space and need to be divided by 100 first
import pandas as pd
df = pd.read_csv("Randomdata.csv")
df2 = df.div(100).add(1).cumprod().sub(1).mul(100)
Comment from OP
I made the following change, removed subtraction.
df2 = df.div(100).add(1).cumprod().mul(100)
df2['returns_from_price_recovered'] = 100 * df2.pct_change()