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Cumprod giving -inf in Python - 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
3/30/2007 4.13585074
4/2/2007 2.121894843
4/3/2007 2.85329683
4/4/2007 2.790940846
4/5/2007 2.709380118
4/6/2007 -0.537294118
4/9/2007 1.409591457
4/10/2007 -0.844805873
4/11/2007 -3.199042638
4/12/2007 -5.222678135
4/13/2007 -5.360018022
4/16/2007 -4.086590776
4/17/2007 -6.846401327
4/18/2007 -8.129698699
4/19/2007 -9.43907148
4/20/2007 -9.54782865
4/23/2007 -10.0618768
4/24/2007 -7.849914365
4/25/2007 -8.470629566
4/26/2007 -8.241284473
4/27/2007 -5.784288072
4/30/2007 -6.333447193
5/1/2007 -8.855330907
5/2/2007 -8.631907451
5/3/2007 -9.548866116
5/4/2007 -10.13722709
5/7/2007 -11.32424337
5/8/2007 -13.57236374
5/9/2007 -11.50262124
5/10/2007 -10.80211768
5/11/2007 -10.26422821
5/14/2007 -8.986010695
5/15/2007 -10.63105462
5/16/2007 -12.91613223
5/17/2007 -10.43330955
5/18/2007 -10.07261057
5/21/2007 -12.94116388
5/22/2007 -11.97531943
5/23/2007 -12.58290308
5/24/2007 -12.94199083
5/25/2007 -14.38311469
5/28/2007 -13.28631254
5/29/2007 -12.89413624
5/30/2007 -16.04739138
5/31/2007 -13.83259156
6/1/2007 -14.26299611
6/4/2007 -14.99654576
6/5/2007 -13.24079702
6/6/2007 -12.01283208
6/7/2007 -12.58499189
6/8/2007 -10.50548575
6/11/2007 -13.46398497
6/12/2007 -10.95636348
6/13/2007 -10.58475154
6/14/2007 -8.485156989
6/15/2007 -5.485141673
6/18/2007 -4.746107027
6/19/2007 0.009767635
6/20/2007 -0.673658716
6/21/2007 -1.451419275
6/22/2007 1.972232158
6/25/2007 -0.391243872
6/26/2007 1.451989507
6/27/2007 -1.766451879
6/28/2007 0.161875559
6/29/2007 -3.825996412
7/2/2007 -0.809421674
7/3/2007 -2.246492588
7/4/2007 0.432136714
7/5/2007 0.283847174
7/6/2007 -1.3650488
7/9/2007 -0.811143411
7/10/2007 -1.465879459
7/11/2007 -1.121273878
7/12/2007 -1.860874059
7/13/2007 -3.035369125
7/16/2007 1.281001022
7/17/2007 1.690991572
7/18/2007 1.945639351
7/19/2007 1.069216233
7/20/2007 3.87371198
7/23/2007 3.296879921
7/24/2007 2.591854823
7/25/2007 -3.389010393
7/26/2007 2.137825455
7/27/2007 2.283479857
7/30/2007 5.838696664
7/31/2007 6.113554468
8/1/2007 7.967210681
8/2/2007 -0.726487682
8/3/2007 -0.232469303
8/6/2007 -2.517441263
8/7/2007 -8.435316702
8/8/2007 -4.850705516
8/9/2007 -6.961937954
8/10/2007 -10.44411748
8/13/2007 -15.68807076
8/14/2007 -13.18786164
8/15/2007 -9.272095703
8/16/2007 -4.725484174
8/17/2007 -8.134541843
8/20/2007 -8.873292727
8/21/2007 -10.55565794
8/22/2007 -9.78740166
8/23/2007 -8.392234748
8/24/2007 -11.26803991
8/27/2007 -6.059253076
8/28/2007 -4.431249471
8/29/2007 -3.683881225
8/30/2007 0.740388956
8/31/2007 0.837060926
9/3/2007 3.715197568
9/4/2007 2.014739847
9/5/2007 4.587444167
9/6/2007 3.221048769
9/7/2007 1.226037567
9/10/2007 1.034531855
9/11/2007 1.479676939
9/12/2007 0.556376215
9/13/2007 -1.530067312
9/14/2007 -2.18203607
9/17/2007 -5.475770938
9/18/2007 -6.824996518
9/19/2007 -2.973998241
9/20/2007 3.708296857
9/21/2007 -3.183077897
9/24/2007 -6.546095221
9/25/2007 -11.00709425
9/26/2007 -10.95480863
9/27/2007 -6.680893811
9/28/2007 -3.787250701
10/1/2007 -14.69643306
10/2/2007 -12.37222197
10/3/2007 -5.619971442
10/4/2007 -5.695837937
10/5/2007 -3.872021061
10/8/2007 -5.852564553
10/9/2007 -6.48262714
10/10/2007 -2.398114024
10/11/2007 -3.103357068
10/12/2007 -5.878634994
10/15/2007 -9.082817992
10/16/2007 -6.07416967
10/17/2007 -3.661138214
10/18/2007 -1.840354785
10/19/2007 -1.716032675
10/22/2007 -9.940732878
10/23/2007 -6.779819032
10/24/2007 -9.55117772
10/25/2007 -6.301075251
10/26/2007 -4.727985653
10/29/2007 -3.424320292
10/30/2007 -7.488285236
10/31/2007 -11.8982972
11/1/2007 -15.03925521
11/2/2007 -15.26744844
11/5/2007 -14.74166075
11/6/2007 -17.44941576
11/7/2007 -17.61279378
11/8/2007 -18.86796214
11/9/2007 -16.67827827
11/12/2007 -12.24014836
11/13/2007 -2.85835521
11/14/2007 -4.131716897
11/15/2007 -0.203410622
11/16/2007 8.569314879
11/19/2007 5.520498128
11/20/2007 4.437427097
11/21/2007 2.798799233
11/22/2007 5.190459646
11/23/2007 2.201222838
11/26/2007 7.707810591
11/27/2007 5.20689008
11/28/2007 7.610897036
11/29/2007 6.703331237
11/30/2007 13.92928613
12/3/2007 18.05895477
12/4/2007 17.24261674
12/5/2007 14.18520991
12/6/2007 17.69996813
12/7/2007 21.95404938
12/10/2007 18.57414126
12/11/2007 18.83884342
12/12/2007 22.41801362
12/13/2007 23.54638194
12/14/2007 25.76433638
12/17/2007 16.69320167
12/18/2007 10.81895687
12/19/2007 10.9753503
12/20/2007 11.33091315
12/21/2007 12.53094339
12/24/2007 14.45641754
12/25/2007 13.19699079
12/26/2007 13.87942316
12/27/2007 11.46173706
12/28/2007 7.770676488
12/31/2007 8.700784022
1/1/2008 14.19019359
1/2/2008 11.50921642
1/3/2008 9.467085019
1/4/2008 8.492617897
1/7/2008 5.371302597
1/8/2008 6.499502146
1/9/2008 3.022727189
1/10/2008 5.927807015
1/11/2008 5.001251384
1/14/2008 0.091578188
1/15/2008 6.806397951
1/16/2008 2.248016175
1/17/2008 5.749742329
1/18/2008 1.095388248
1/21/2008 -0.368023403
1/22/2008 8.237992918
1/23/2008 -0.780766435
1/24/2008 -0.233793677
1/25/2008 -5.104415967
1/28/2008 -5.638624718
1/29/2008 -2.511924819
1/30/2008 -2.133339717
1/31/2008 -3.014162532
2/1/2008 4.847211419
2/4/2008 1.90534583
2/5/2008 -0.089019867
2/6/2008 -3.930920283
2/7/2008 -7.596390602
2/8/2008 -5.358477963
2/11/2008 -3.588291865
2/12/2008 -1.031121178
2/13/2008 4.176705555
2/14/2008 4.100989501
2/15/2008 3.685820292
2/18/2008 3.918671965
2/19/2008 4.302039738
2/20/2008 8.143021699
2/21/2008 7.475478645
2/22/2008 2.322549449
2/25/2008 5.058904324
2/26/2008 0.911568809
2/27/2008 -1.720405845
2/28/2008 -4.566637975
2/29/2008 -7.874776755
3/3/2008 -7.949385894
3/4/2008 -7.987067299
3/5/2008 -12.40581822
3/6/2008 -9.860569783
3/7/2008 -12.72461433
3/10/2008 -11.14026753
3/11/2008 -6.222506019
3/12/2008 -2.083816629
3/13/2008 -1.722946088
3/14/2008 -5.537069436
3/17/2008 -6.716071481
3/18/2008 -6.693944594
3/19/2008 5.819147828
3/20/2008 9.65059264
3/21/2008 14.9915837
3/24/2008 14.39795771
3/25/2008 12.093442
3/26/2008 9.296061707
3/27/2008 12.99072309
3/28/2008 9.836902201
3/31/2008 9.921155178
4/1/2008 7.310883267
4/2/2008 14.15477406
4/3/2008 15.48242329
4/4/2008 7.464041165
4/7/2008 11.33508368
4/8/2008 15.59010551
4/9/2008 12.58380558
4/10/2008 14.61160039
4/11/2008 12.68080501
4/14/2008 11.06353488
4/15/2008 7.092636692
4/16/2008 7.592291792
4/17/2008 3.699266022
4/18/2008 5.928888483
4/21/2008 9.133551847
4/22/2008 6.211128385
4/23/2008 5.59943155
4/24/2008 11.19533502
4/25/2008 8.286268131
4/28/2008 10.85713699
4/29/2008 10.96304626
4/30/2008 9.392330251
5/1/2008 0.459398303
5/2/2008 6.634717801
5/5/2008 7.475896399
5/6/2008 6.934371007
5/7/2008 5.570514268
5/8/2008 6.270557437
5/9/2008 7.889956448
5/12/2008 1.34341901
5/13/2008 -1.824946933
5/14/2008 -1.801607848
5/15/2008 -2.846939297
5/16/2008 -2.62131888
5/19/2008 -2.663214031
5/20/2008 -3.774870813
5/21/2008 -5.138174533
5/22/2008 -5.480107155
5/23/2008 -6.670996157
5/26/2008 -4.64561419
5/27/2008 -4.145292498
5/28/2008 0.355057164
5/29/2008 -2.939764523
5/30/2008 -6.63747287
6/2/2008 -6.098950421
6/3/2008 -7.13882023
6/4/2008 -5.604616952
6/5/2008 -4.549857994
6/6/2008 5.087697562
6/9/2008 6.579771782
6/10/2008 0.060955293
6/11/2008 2.445348204
6/12/2008 0.317989372
6/13/2008 3.251865221
6/16/2008 6.862732095
6/17/2008 -0.134582166
6/18/2008 -6.537950835
6/19/2008 -8.448799147
6/20/2008 -6.732406995
6/23/2008 -7.738394975
6/24/2008 -8.206195168
6/25/2008 -5.548307148
6/26/2008 -7.002765304
6/27/2008 -6.495980763
6/30/2008 -8.8477976
7/1/2008 -1.679587243
7/2/2008 0.128504075
7/3/2008 -0.255570561
7/4/2008 3.108701207
7/7/2008 5.492404862
7/8/2008 3.133532773
7/9/2008 2.385568794
7/10/2008 -3.712475585
7/11/2008 -2.11874535
7/14/2008 -7.791149245
7/15/2008 -9.785882826
7/16/2008 -16.1405786
7/17/2008 -14.74751549
7/18/2008 -13.27703399
7/21/2008 -14.20650424
7/22/2008 -16.05642567
7/23/2008 -10.40258214
7/24/2008 -7.895783574
7/25/2008 -6.064576168
7/28/2008 -7.555469117
7/29/2008 -7.550365786
7/30/2008 -4.41096581
7/31/2008 0.972694457
8/1/2008 -3.909922199
8/4/2008 -2.854671378
8/5/2008 -4.752925064
8/6/2008 7.894333599
8/7/2008 6.952958366
8/8/2008 6.803337004
8/11/2008 12.5598522
8/12/2008 17.02711995
8/13/2008 11.21457308
8/14/2008 9.870340669
8/15/2008 13.52048273
8/18/2008 15.36663294
8/19/2008 12.06804629
8/20/2008 10.90150334
8/21/2008 9.740240549
8/22/2008 5.754842321
8/25/2008 5.094710468
8/26/2008 4.298242012
8/27/2008 3.633974039
8/28/2008 2.662183974
8/29/2008 2.274767726
9/1/2008 3.049897094
9/2/2008 3.728150019
9/3/2008 3.568469208
9/4/2008 3.118192613
9/5/2008 0.208336992
9/8/2008 -7.955346446
9/9/2008 3.034657287
9/10/2008 0.212924163
9/11/2008 -5.039089706
9/12/2008 -4.206315664
9/15/2008 -12.47329445
9/16/2008 -23.87744975
9/17/2008 -26.40484165
9/18/2008 -25.38241597
9/19/2008 -23.36121528
9/22/2008 -21.42741971
9/23/2008 -17.99846112
9/24/2008 -28.57986509
9/25/2008 -29.16609299
9/26/2008 -27.08038434
9/29/2008 -20.97454917
9/30/2008 -30.86583486
10/1/2008 -11.34463183
10/2/2008 -12.48376615
10/3/2008 -6.026656168
10/6/2008 -6.990288682
10/7/2008 -8.932722825
10/8/2008 -9.613027881
10/9/2008 -9.639879581
10/10/2008 -9.585152864
10/13/2008 -3.406342217
10/14/2008 8.128268033
10/15/2008 -0.84337937
10/16/2008 3.021563746
10/17/2008 -3.052518135
10/20/2008 -2.400985986
10/21/2008 3.0230433
10/22/2008 10.5804596
10/23/2008 18.09790623
10/24/2008 12.13621658
10/27/2008 14.20662326
10/28/2008 21.01635274
10/29/2008 17.2248841
10/30/2008 -4.561706872
10/31/2008 -18.90903769
11/3/2008 -4.608843616
11/4/2008 -20.06787313
11/5/2008 -18.5941511
11/6/2008 -21.3287201
11/7/2008 -18.90428072
11/10/2008 -17.15139132
11/11/2008 -11.95485141
11/12/2008 -10.52108832
11/13/2008 -3.751031577
11/14/2008 -7.025476067
11/17/2008 -0.484754776
11/18/2008 -7.565724121
11/19/2008 -3.494100907
11/20/2008 1.878346526
11/21/2008 8.620226332
11/24/2008 -2.178445463
11/25/2008 -6.594895809
11/26/2008 -13.80582831
11/27/2008 -1.718399396
11/28/2008 -2.690040888
12/1/2008 0.045615029
12/2/2008 -8.037898361
12/3/2008 -6.694808104
12/4/2008 -1.105754753
12/5/2008 1.02570118
12/8/2008 -4.239560375
12/9/2008 -5.393379841
12/10/2008 -2.751561124
12/11/2008 -2.779615506
12/12/2008 -3.187906517
12/15/2008 -1.152777608
12/16/2008 -8.701242176
12/17/2008 -17.74161255
12/18/2008 -25.91607496
12/19/2008 -23.78881822
12/22/2008 -21.24675823
12/23/2008 -11.96460548
12/24/2008 -15.00820918
12/25/2008 -18.71378165
12/26/2008 -15.66724025
12/29/2008 -9.785209077
12/30/2008 -8.555974407
12/31/2008 -8.218782102
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()

Related

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
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1;SMC;15,6954;0,0998
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1;SMC;16,1484;0,0015
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1;SMC;15,5933;0,0706
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1;SMC;15,2084;0,1235
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1;SMC;16,2687;-0,0599
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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
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1;SMC;16,6984;-0,0744
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1;SMC;15,8423;0,0837
1;SMC;15,6786;0,1166
1;SMC;14,6876;0,3651
1;SMC;15,5642;0,1374
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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
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1;SMC;14,4008;0,4278
1;SMC;16,1806;-0,0993
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1;SMC;14,7915;0,3349
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1;SMC;15,8312;0,048
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1;SMC;15,9238;-0,016
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1;SMC;14,9996;0,3125
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1;SMC;14,9974;0,3175
1;SMC;16,1114;-0,0793
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1;SMC;15,5506;0,0998
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1;SMC;17,6762;-0,4719
1;SMC;15,512;0,1091
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1;SMC;14,8618;0,2606
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1;SMC;13,9661;0,5273
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1;SMC;15,4834;0,1461
1;SMC;16,1654;0,0084
1;SMC;17,02;-0,0819
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1;SMC;15,9034;0,0257
1;SMC;15,8515;0,0495
1;SMC;15,0717;0,3022
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1;SMC;14,4274;0,4869
1;SMC;16,2396;-0,0553
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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
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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
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1;SMC;14,5532;0,4375
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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

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:

I have a dictionary of some stock price for the last 252 business days. This dictionary contains 252 items with the structure timestamp=price.
year_prices = {'1584662400000': 85.5059967041, '1584921600000': 86.858001709, '1585008000000': 101.0, '1585094400000': 107.8499984741, '1585180800000': 105.6320037842, '1585267200000': 102.8720016479, '1585526400000': 100.4260025024, '1585612800000': 104.8000030518, '1585699200000': 96.31199646, '1585785600000': 90.8939971924, '1585872000000': 96.0019989014, '1586131200000': 103.2480010986, '1586217600000': 109.0899963379, '1586304000000': 109.7679977417, '1586390400000': 114.5999984741, '1586736000000': 130.1900024414, '1586822400000': 141.9779968262, '1586908800000': 145.966003418, '1586995200000': 149.0420074463, '1587081600000': 150.7779998779, '1587340800000': 149.2720031738, '1587427200000': 137.3439941406, '1587513600000': 146.4219970703, '1587600000000': 141.1260070801, '1587686400000': 145.0299987793, '1587945600000': 159.75, '1588032000000': 153.824005127, '1588118400000': 160.1020050049, '1588204800000': 156.3760070801, '1588291200000': 140.2640075684, '1588550400000': 152.2380065918, '1588636800000': 153.641998291, '1588723200000': 156.5160064697, '1588809600000': 156.0079956055, '1588896000000': 163.8840026855, '1589155200000': 162.2579956055, '1589241600000': 161.8820037842, '1589328000000': 158.1920013428, '1589414400000': 160.6660003662, '1589500800000': 159.8339996338, '1589760000000': 162.7259979248, '1589846400000': 161.6020050049, '1589932800000': 163.1119995117, '1590019200000': 165.5200042725, '1590105600000': 163.3760070801, '1590451200000': 163.7740020752, '1590537600000': 164.046005249, '1590624000000': 161.1620025635, '1590710400000': 167.0, '1590969600000': 179.6199951172, '1591056000000': 176.31199646, '1591142400000': 176.5919952393, '1591228800000': 172.8760070801, '1591315200000': 177.1320037842, '1591574400000': 189.9839935303, '1591660800000': 188.1340026855, '1591747200000': 205.0099945068, '1591833600000': 194.5679931641, '1591920000000': 187.0559997559, '1592179200000': 198.1799926758, '1592265600000': 196.425994873, '1592352000000': 198.358001709, '1592438400000': 200.7920074463, '1592524800000': 200.1799926758, '1592784000000': 198.8639984131, '1592870400000': 200.3560028076, '1592956800000': 192.1699981689, '1593043200000': 197.1959991455, '1593129600000': 191.9479980469, '1593388800000': 201.8699951172, '1593475200000': 215.9620056152, '1593561600000': 223.925994873, '1593648000000': 241.7319946289, '1593993600000': 274.3160095215, '1594080000000': 277.9719848633, '1594166400000': 273.175994873, '1594252800000': 278.8559875488, '1594339200000': 308.9299926758, '1594598400000': 299.4119873047, '1594684800000': 303.3599853516, '1594771200000': 309.2019958496, '1594857600000': 300.1279907227, '1594944000000': 300.1679992676, '1595203200000': 328.6000061035, '1595289600000': 313.6719970703, '1595376000000': 318.466003418, '1595462400000': 302.6140136719, '1595548800000': 283.3999938965, '1595808000000': 307.9200134277, '1595894400000': 295.2980041504, '1595980800000': 299.8219909668, '1596067200000': 297.4979858398, '1596153600000': 286.1520080566, '1596412800000': 297.0, '1596499200000': 297.3999938965, '1596585600000': 297.0039978027, '1596672000000': 297.9159851074, '1596758400000': 290.5419921875, '1597017600000': 283.7139892578, '1597104000000': 274.8779907227, '1597190400000': 310.9519958496, '1597276800000': 324.200012207, '1597363200000': 330.141998291, '1597622400000': 367.1279907227, '1597708800000': 377.4179992676, '1597795200000': 375.7059936523, '1597881600000': 400.3659973145, '1597968000000': 409.9960021973, '1598227200000': 402.8399963379, '1598313600000': 404.6679992676, '1598400000000': 430.6340026855, '1598486400000': 447.75, '1598572800000': 442.6799926758, '1598832000000': 498.3200073242, '1598918400000': 475.049987793, '1599004800000': 447.3699951172, '1599091200000': 407.0, '1599177600000': 418.3200073242, '1599523200000': 330.2099914551, '1599609600000': 366.2799987793, '1599696000000': 371.3399963379, '1599782400000': 372.7200012207, '1600041600000': 419.6199951172, '1600128000000': 449.7600097656, '1600214400000': 441.7600097656, '1600300800000': 423.4299926758, '1600387200000': 442.1499938965, '1600646400000': 449.3900146484, '1600732800000': 424.2300109863, '1600819200000': 380.3599853516, '1600905600000': 387.7900085449, '1600992000000': 407.3399963379, '1601251200000': 421.200012207, '1601337600000': 419.0700073242, '1601424000000': 429.0100097656, '1601510400000': 448.1600036621, '1601596800000': 415.0899963379, '1601856000000': 425.6799926758, '1601942400000': 413.9800109863, '1602028800000': 425.299987793, '1602115200000': 425.9200134277, '1602201600000': 434.0, '1602460800000': 442.299987793, '1602547200000': 446.6499938965, '1602633600000': 461.299987793, '1602720000000': 448.8800048828, '1602806400000': 439.6700134277, '1603065600000': 430.8299865723, '1603152000000': 421.9400024414, '1603238400000': 422.6400146484, '1603324800000': 425.7900085449, '1603411200000': 420.6300048828, '1603670400000': 420.2799987793, '1603756800000': 424.6799926758, '1603843200000': 406.0199890137, '1603929600000': 410.8299865723, '1604016000000': 388.0400085449, '1604275200000': 400.5100097656, '1604361600000': 423.8999938965, '1604448000000': 420.9800109863, '1604534400000': 438.0899963379, '1604620800000': 429.950012207, '1604880000000': 421.2600097656, '1604966400000': 410.3599853516, '1605052800000': 417.1300048828, '1605139200000': 411.7600097656, '1605225600000': 408.5, '1605484800000': 408.0899963379, '1605571200000': 441.6099853516, '1605657600000': 486.6400146484, '1605744000000': 499.2699890137, '1605830400000': 489.6099853516, '1606089600000': 521.8499755859, '1606176000000': 555.3800048828, '1606262400000': 574.0, '1606435200000': 585.7600097656, '1606694400000': 567.5999755859, '1606780800000': 584.7600097656, '1606867200000': 568.8200073242, '1606953600000': 593.3800048828, '1607040000000': 599.0399780273, '1607299200000': 641.7600097656, '1607385600000': 649.8800048828, '1607472000000': 604.4799804688, '1607558400000': 627.0700073242, '1607644800000': 609.9899902344, '1607904000000': 639.8300170898, '1607990400000': 633.25, '1608076800000': 622.7700195312, '1608163200000': 655.9000244141, '1608249600000': 695.0, '1608508800000': 649.8599853516, '1608595200000': 640.3400268555, '1608681600000': 645.9799804688, '1608768000000': 661.7700195312, '1609113600000': 663.6900024414, '1609200000000': 665.9899902344, '1609286400000': 694.7800292969, '1609372800000': 705.6699829102, '1609718400000': 729.7700195312, '1609804800000': 735.1099853516, '1609891200000': 755.9799804688, '1609977600000': 816.0399780273, '1610064000000': 880.0200195312, '1610323200000': 811.1900024414, '1610409600000': 849.4400024414, '1610496000000': 854.4099731445, '1610582400000': 845.0, '1610668800000': 826.1599731445, '1611014400000': 844.549987793, '1611100800000': 850.450012207, '1611187200000': 844.9899902344, '1611273600000': 846.6400146484, '1611532800000': 880.799987793, '1611619200000': 883.0900268555, '1611705600000': 864.1599731445, '1611792000000': 835.4299926758, '1611878400000': 793.5300292969, '1612137600000': 839.8099975586, '1612224000000': 872.7899780273, '1612310400000': 854.6900024414, '1612396800000': 849.9899902344, '1612483200000': 852.2299804688, '1612742400000': 863.4199829102, '1612828800000': 849.4600219727, '1612915200000': 804.8200073242, '1613001600000': 811.6599731445, '1613088000000': 816.1199951172, '1613433600000': 796.2199707031, '1613520000000': 798.1500244141, '1613606400000': 787.3800048828, '1613692800000': 781.299987793, '1613952000000': 714.5, '1614038400000': 698.8400268555, '1614124800000': 742.0200195312, '1614211200000': 682.2199707031, '1614297600000': 675.5, '1614556800000': 718.4299926758, '1614643200000': 686.4400024414, '1614729600000': 653.200012207, '1614816000000': 621.4400024414, '1614902400000': 597.950012207, '1615161600000': 563.0, '1615248000000': 673.5800170898, '1615334400000': 668.0599975586, '1615420800000': 699.5999755859, '1615507200000': 693.7299804688, '1615766400000': 707.9400024414, '1615852800000': 676.8800048828, '1615939200000': 701.8099975586, '1616025600000': 653.1599731445, '1616112000000': 654.8699951172}
Now I need to process this dict and group their values by week, so that I get a 52 items long dictionary (of structure timestamp:price) being price the average price for that week from the original dict.
I can't figure out a way of getting the last 52 weeks dates in python, and also I have dubts on how to go from having the dates to getting the prices by week on a pythonic way.

There are (at least) two possible solutions that will work.
Solution 1
By using pandas you can use the following steps:
Convert the data to a DataFrame
Cast the timestamp columns to a datetime object
Filter the dates (filter/select rows of pandas dataframe by timestamp column)
Code
import pandas as pd
df = pd.DataFrame(year_prices.items(), columns=['timestamp', 'price'])
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
# Example filtering times
df = df[(df['timestamp'] > '2020-07-01') & (df['timestamp'] < '2021-03-21')]
Solution 2
You can iterate over the data yourself, and use the built in datetime libary.
See:
How do I create a datetime in Python from milliseconds?
Code
import datetime
from pprint import pprint
start_date = datetime.datetime(2020, 7, 2)
end_date = datetime.datetime(2021, 3, 21)
records = {}
for milliseconds, price in year_prices.items():
date = datetime.datetime.fromtimestamp(int(milliseconds) / 1000.)
if start_date < date < end_date:
records[date] = price
pprint(records)

In the process of doing data fitting, I got 300 data, but the difference between the data is too big, I want 30 value an last, is there has any method? The following is a simple analysis of these 300 data, I have 300 numbers, and I am searching for a long time on net. But no use. Please help or try to give some ideas how to achieve this：
mean 159.4604181
std 14.08847471
min 125.115372
25% 149.8761805
50% 159.13718
75% 168.4387325
max 206.164806
my data
179.810669 175.676689 184.907814 185.026245 176.046089 180.488594 162.373795 142.625153156.730606 171.185516182.506538 171.279892 157.094376 175.523239 138.663803 154.037315 146.47934 166.05497 154.187614 160.262428165.520088 159.510735 168.477242 155.714188 159.907532 154.23027 148.736519 168.688332 160.950378 150.446991154.586983 169.485733 179.650826177.456154 164.688042 158.481346 157.529823 177.155075 195.534782 185.3591 166.674973 169.619331 163.814583 171.758568 178.962212 166.57563 168.160606 175.033401 166.368645 155.475143 164.136368 129.815918 171.380379 139.883484 155.503197 144.567993 148.340202 146.3424 159.094955 138.338203 140.137421 171.319687 153.106209 165.617142 165.526573 162.551674 149.410728 169.361961 169.951523 167.23304 154.108025 147.144096 146.922226 143.736519 174.33152 178.938164 174.354897 168.984444 169.659035 159.179405 164.230553 174.717148 168.7015 168.840828 165.545853 149.285713 149.012978 156.370644 171.694534 160.497284 152.712379 162.705696 150.347458 173.261192 147.494514 175.424751 178.045708 171.828514 165.673157 160.281517 159.184944 149.384384 156.638893 173.703499 162.53994 154.150589 161.535164 162.097717 166.458252 152.737953 152.43277 164.732224 161.109459 161.410538 151.811913 144.878899 151.292107 174.201546 179.860199 156.777939 157.412254 128.193626 134.273026 165.721443 151.169197 146.080826 158.473038 156.739098 164.361488 165.708656 168.370171 131.646339 138.349174 140.834816 140.425423 141.339821 153.862488 132.983589 143.058212 157.537552 140.514938 165.444321 164.64386 152.068802 164.700546 157.977196 161.475334 152.682129 159.353088 181.302414 177.709511 174.072334 161.575821 153.542702 167.670769 177.191048 161.461784 163.927948 166.825462 147.836052 155.826508 165.665619 147.922226 150.064796 142.898941 154.046188 176.772972 162.681747 133.64357 153.721153 183.379425 170.639091 174.363014 152.349274 171.839104 154.853706 160.088364 152.339348 147.243919 167.363869 163.696533 169.445465 149.822212 188.664185 149.297455 149.38324 162.856377 167.585457 149.559311 142.964882 129.458916 160.373032 162.140373 155.924423 155.679741 154.898079 139.470657 155.763046 156.382004 149.876038 134.911156 134.708656 157.063934 143.389061 138.854088 146.451187 143.490692 170.697395 165.124054 171.297455 148.049622 156.399467 149.876228 162.300697 149.619713 143.149063 148.450333 155.154694 168.817543 169.427711 161.739861 149.329041 137.551292 162.045517 155.713234 167.576202 147.337914 156.440407 169.3153 170.732796 158.153931 148.641853 160.371506 152.678215 151.62735 144.289818 150.412636 135.979866 158.555397 144.935394 152.542038 131.922989 125.718201 160.387344 150.361771 159.63842 150.993797 141.396507 161.954765 125.115372 138.610939 156.862579 155.175972 146.390205 185.087791 164.873558 160.305084 194.200592 168.425896 168.09037 147.101822 161.535454 151.740341 162.898369 169.418549 143.773643 164.353752 175.510162 206.164806 198.906193 204.26844 173.567131 177.433823 187.072243 187.453857 180.494034 202.663326 198.192097 145.617523 148.858864 174.640137 159.404617 153.754082 150.244583 133.0271 156.841972 152.707985 172.945221 155.878998 150.713615 162.749596 151.863152 153.257278 133.326752 147.612083 151.167763 158.199638 161.290581 149.243629 155.955055

if you are using sklearn
you can use StandardScaler
only transform the data you think the difference is too big
from sklearn.preprocessing import StandardScaler
X_scaler = StandardScaler()
X_train = X_scaler.fit_transform(X_train)
X_test = X_scaler.transform(X_test)
FYI:http://scikit-learn.org/stable/auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py

My code is
montly_ratio = [0.046644865594960915, 0.060708276768318435, 0.07706787106693493, 0.09332535512669753, 0.10693770921153412, 0.11542007554770539, 0.11681104031351618, 0.11013791562902592, 0.09588202154576116, 0.07644396803642538, 0.056608936984611016, 0.044011964174509026]
len = 12
production_by_yr = [12129.0, 24197.0, 36205.0, 48152.0, 60038.0, 71863.0, 83628.0, 95331.0, 106974.0, 118556.0, 130077.0, 141538.0, 152938.0, 164277.0, 175555.0, 186772.0, 197929.0, 209024.0]
len = 18
I want to do
for yr in production_by_yr:
for month in montly_ratio:
print yr * month,
Which will give me 216 values
I did the same by list comprehension as following:
[yr * month for yr in production_by_yr for month in montly_ratio]
This gives me 216 values like
565.7555748012809, 736.3306889229343, 934.7562081708537, 1131.9432323317144, 1297.0474750266974, 1399.9300963181188, 1416.8011079626376, 1335.8627786644554, 1162.953039328537, 927.1888883138034, 686.6097966863471, 533.82111347262, 1128.6658128012693, 1468.9581729630013, 1864.8112762066244, 2258.1936180007, 2587.571749791491, 2792.8195680278272, 2826.476742466151, 2665.00714447554, 2320.0572753427828, 1849.7146945773848, 1369.7664482166329, 1064.957497130595, 1688.77735886556, 2197.943160396969, 2790.242271978379, 3378.844482362084, 3871.6797620035927, 4178.783835204674, 4229.1437145508535, 3987.543235348883, 3471.4085900642826, 2767.6538627587806, 2049.526563527842, 1593.4531629380992, 2246.043568128558, 2923.2249429480694, 3710.9721276150503, 4493.8025000607395, 5149.264573953791, 5557.70747777311, 5624.685213176431, 5303.360913368856, 4616.911101471491, 3680.9299488899546, 2725.8335336829896, 2119.2640989309584, 2800.464440590263, 3644.803520616302, 4627.000843116639, 5603.067671096666, 6420.326185642085, 6929.590495733136, 7013.101238342884, 6612.460178535458, 5756.564809564408, 4589.542952970907, 3398.687358682076, 2642.390305109173, 3352.0399762506763, 4362.678893401668, 5538.328418483145, 6706.6399954698645, 7684.8645970684765, 8294.432889084752, 8394.391790050213, 7914.841030848689, 6890.369714343034, 5493.492875001637, 4068.0880385251016, 3162.831781472742, 3900.8168199753914, 5076.911769580934, 6445.031921585634, 7804.612798535461, 8942.986745942175, 9652.350077903506, 9768.67367933873, 9210.61360822418, 8018.421697828914, 6392.856158950181, 4734.09218214905, 3680.6325399858406, 4446.701682033219, 5787.380732600564, 7346.9572166819735, 8896.799429583201, 10194.47875684476, 11003.111222038302, 11135.71328412781, 10499.55763483067, 9140.528995978957, 7287.4799168804675, 5396.586571679953, 4195.70455672012, 4989.787852155348, 6494.207199014097, 8244.258439514297, 9983.386539323341, 11439.55450519465, 12346.947161640237, 12495.74422649808, 11781.893386499418, 10256.883372836253, 8177.517036728568, 6055.6844249917785, 4708.135855603929, 5530.028685476186, 7197.33046054476, 9136.858522211536, 11064.280802400752, 12678.107053282638, 13683.74247663376, 13848.649695409224, 13057.510725314796, 11367.38894637926, 9062.891074526447, 6711.329133147544, 5217.882424673092, 6067.424181995731, 7896.750517192557, 10024.757464773695, 12139.482218815434, 13910.136401108723, 15013.497167018873, 15194.429690861243, 14326.409651276805, 12472.045716607974, 9943.602030274104, 7363.520696147247, 5724.94426392761, 6602.020986579578, 8592.528077234254, 10908.032335071835, 13209.084113922514, 15135.749486382116, 16336.326652871126, 16533.201023894453, 15588.700302301071, 13570.949565543942, 10819.726347939575, 8012.315722927874, 6229.365385331658, 7133.7724543621325, 9284.602432393085, 11786.606065234893, 14272.993162366865, 16354.839371393604, 17652.11551411497, 17864.846883468537, 16844.272540471964, 14664.00461116562, 11691.187583554824, 8657.65760455244, 6731.101776921061, 7662.678585343394, 9972.973582669048, 12660.478655262868, 15331.20936414849, 17567.40605614319, 18960.8637507504, 19189.367269583498, 18093.126365789492, 15751.210853473005, 12557.985737119852, 9299.546341020943, 7230.153438695819, 8188.739379523363, 10657.641528062142, 13529.65010515576, 16383.732719267384, 18773.44954063087, 20262.57136277742, 20506.762182239334, 19335.261778253644, 16832.5682924661, 13420.120808634658, 9937.981932333387, 7726.520370655932, 8711.95483690204, 11338.60626857237, 14394.12041491357, 17430.56322772355, 19972.96982485665, 21557.238350196032, 21817.031621436043, 20570.67877786443, 17908.076928144903, 14277.59279809924, 10572.964378489769, 8220.2025728014, 9232.37160234502, 12015.9285124765, 15253.966652407364, 18471.794214872116, 21166.073846529736, 22844.98013308178, 23120.292398213944, 21799.48750253747, 18977.83264253096, 15130.478149481638, 11204.550288427074, 8711.244057096397, 9749.89638612111, 12689.486843220993, 16109.034681895006, 19507.239030002824, 22352.54773023171, 24125.565871283572, 24416.310890492405, 23021.467676441513, 20041.64367158118, 15978.623974845777, 11832.626444271333, 9199.556799612574]
so I thought to validate the last 12 numbers which is
production_yr = 209024.0 and ratio being same and I did
[20924.0 * r for r in montly_ratio]
[975.9971677089621, 1270.259983100295, 1612.5681342045464, 1952.739730671019,
2237.56462754214, 2415.0496607601876, 2444.1542075200123, 2304.525746621738, 2006.2354188235065, 1599.5135871941645, 1184.485397466001, 920.9063383874269]
and then I realized that the numbers I achieved with nested for loop are quite big and might not be right
9749.89638612111, 12689.486843220993, 16109.034681895006, 19507.239030002824, 22352.54773023171, 24125.565871283572, 24416.310890492405, 23021.467676441513, 20041.64367158118, 15978.623974845777, 11832.626444271333, 9199.556799612574
What is that I am not doing right here?

Err, but your code for validation is wrong:
209024.0 is the correct value, and you used:
[20924.0 * r for r in montly_ratio]
^^^^^