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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'Engine_Condition'}) and 6 missing columns ({'Engine_rpm', 'lub_oil_temp', 'Coolant_temp', 'Fuel_pressure', 'Coolant_pressure', 'Lub_oil_pressure'}).

This happened while the csv dataset builder was generating data using

hf://datasets/CodingBuddy/Predictive-maintenance/y_train.csv (at revision 92ff18ba0bff25ceafe348d79644978c48644aa5)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Engine_Condition: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 392
              to
              {'Engine_rpm': Value('int64'), 'Lub_oil_pressure': Value('float64'), 'Fuel_pressure': Value('float64'), 'Coolant_pressure': Value('float64'), 'lub_oil_temp': Value('float64'), 'Coolant_temp': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'Engine_Condition'}) and 6 missing columns ({'Engine_rpm', 'lub_oil_temp', 'Coolant_temp', 'Fuel_pressure', 'Coolant_pressure', 'Lub_oil_pressure'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/CodingBuddy/Predictive-maintenance/y_train.csv (at revision 92ff18ba0bff25ceafe348d79644978c48644aa5)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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Engine_rpm
int64
Lub_oil_pressure
float64
Fuel_pressure
float64
Coolant_pressure
float64
lub_oil_temp
float64
Coolant_temp
float64
489
2.134818
5.674162
3.370571
76.291865
74.695573
910
2.10541
5.381185
1.714422
75.479919
79.92753
523
2.991344
4.781397
2.687113
76.939586
76.40191
727
2.928145
8.276127
1.318319
78.202955
71.288606
666
3.527191
8.625874
2.096356
74.028029
66.678025
645
2.370529
7.621057
1.390177
86.239597
78.358652
1,029
2.402729
7.245764
2.314968
76.984773
80.014393
899
2.463034
5.814229
1.671851
78.5851
69.051388
984
3.692439
5.295042
1.662781
78.401538
65.332227
877
2.031846
4.135845
2.454267
76.259382
75.972441
808
5.054628
11.762847
1.498095
78.148476
81.133974
985
3.680778
4.997288
3.280425
76.478806
76.403894
543
5.243985
8.415203
2.606358
75.916037
75.886057
1,213
2.615431
12.880649
2.189643
76.892099
73.269793
1,000
2.737609
7.050306
2.978769
77.640835
70.935765
844
2.12148
4.123632
3.392019
76.47297
79.47549
385
4.87901
9.545393
2.856513
75.122606
69.434771
1,023
5.043413
12.864281
2.84669
76.326291
79.175506
537
4.156876
4.950165
2.970651
76.293586
80.982348
568
1.694807
12.348968
2.549711
73.736636
74.466254
956
3.801883
5.681047
3.222322
77.899396
73.157387
531
2.752722
3.742765
2.564416
77.006995
67.639895
833
2.168215
10.163609
1.516405
77.252448
79.425184
689
2.689093
3.075331
1.867306
74.255208
73.180785
838
2.186042
2.300803
1.804424
82.053763
79.710716
848
2.649552
5.354388
2.206415
75.003057
68.549884
693
4.783418
7.651037
2.477349
77.079288
76.570091
594
3.084191
6.167154
2.095289
77.894048
81.404784
752
3.58646
7.361105
1.490917
78.823805
71.034738
558
4.443161
5.610185
3.112912
76.664141
67.082136
1,158
3.106533
5.283098
2.08261
83.492547
91.43866
556
4.521859
4.856588
1.095585
76.816848
89.99239
632
4.480879
6.195419
2.586983
74.782881
73.497605
521
2.571298
6.797906
3.069017
76.508946
86.484412
603
2.834343
8.813425
2.705946
74.443287
82.7921
589
3.896935
5.126121
1.950193
80.748987
84.953357
757
4.524118
11.335988
1.808347
74.961748
89.43613
1,380
5.140655
4.036824
1.861603
76.861163
85.013412
710
2.290683
6.53077
2.855587
76.449853
75.029542
1,244
4.236204
15.711606
3.18144
78.799186
75.097707
906
3.898434
7.295919
2.113603
87.370751
90.313624
604
5.292656
10.988485
1.4564
84.777872
84.538346
496
2.325607
2.679709
1.871161
75.694329
78.923878
826
3.715461
11.30668
1.731784
77.546953
82.096057
948
5.008724
7.620823
2.250333
79.519331
73.950295
633
4.99797
5.827523
2.56296
76.948409
73.27514
594
3.650876
3.246766
1.366778
76.623788
83.849694
855
4.242377
16.198058
1.158095
78.500516
85.4262
1,683
2.986699
6.596885
1.947343
77.199041
72.605844
701
2.538212
7.602349
2.77368
77.34683
86.642983
1,060
0.310808
6.537004
1.504135
74.827478
74.969447
727
2.621025
9.518806
2.889201
78.010075
82.206493
495
1.820325
6.070955
1.221021
78.249489
80.443738
823
3.873634
4.861404
2.744469
77.245628
83.367299
777
3.874779
7.434013
2.388319
83.481586
84.044351
1,468
2.815471
5.701884
2.791582
82.063526
82.61145
778
2.651913
16.946016
3.665664
75.135771
68.249702
853
1.938875
15.150643
2.819533
76.217566
77.071431
1,436
3.481701
10.054304
2.654589
77.812445
79.376655
594
3.339114
5.30206
2.254715
77.67948
78.421579
883
1.190749
4.536156
3.36081
76.661334
70.908686
463
3.109449
7.312216
1.436549
76.345621
77.44584
403
4.012811
8.609491
1.427517
84.111247
73.147049
455
3.699408
0.717386
1.409716
77.780797
82.321307
537
2.490573
3.850438
2.279635
84.10091
81.94767
1,315
4.249952
7.116549
3.214132
75.83607
78.187722
580
4.721039
2.671087
2.0272
77.962609
68.344758
428
4.817375
7.381478
3.067389
74.598112
68.051277
652
3.294514
4.747837
1.840367
75.169404
73.216696
1,480
3.186473
8.622551
2.020006
77.036818
77.269761
1,342
2.208707
5.953657
1.644254
76.476507
93.28803
808
1.897994
4.01273
2.255445
74.421318
70.875362
816
3.665968
6.333247
1.805788
75.425167
80.748074
701
4.724991
4.956874
3.010533
77.925225
78.402405
488
0.325005
8.190476
1.503244
76.150753
77.308195
844
2.36649
6.591311
1.726951
75.328941
76.681337
549
1.870282
6.813985
2.254254
73.541845
73.567809
566
3.513361
7.180617
2.113465
77.832655
71.197034
691
4.023225
1.460063
1.92407
74.952104
79.825513
927
2.210405
4.883065
1.54425
77.206829
86.704436
768
4.143359
6.123134
3.160531
76.770211
75.668186
378
2.714733
9.066225
1.408376
88.041242
84.072589
937
2.871735
7.049813
1.437773
77.831682
86.01488
845
3.267148
12.860714
2.847952
74.36889
66.976588
985
3.73398
6.217013
1.565098
85.800246
84.725522
569
1.973369
7.309184
4.738955
79.158689
85.848807
804
1.945053
6.96565
1.376613
76.083099
77.798293
895
2.965773
2.963021
2.57942
78.025097
91.338478
717
1.886188
6.228442
1.83487
76.279511
84.215192
688
1.909407
5.671502
1.135106
78.187591
71.210084
979
2.687821
6.667786
2.381332
76.309745
84.924999
581
2.796343
12.182567
2.968462
74.740083
88.09517
518
2.005426
7.492081
2.94091
77.953207
70.749355
709
0.662906
12.694415
2.543064
75.361472
74.6047
583
2.923504
4.92118
1.49076
77.592957
73.453402
894
3.717891
6.656079
1.703451
76.233256
74.884571
810
1.948652
7.204751
1.307335
75.849186
84.839054
443
2.628596
6.531509
3.187268
73.701782
89.508966
1,062
3.773597
5.449411
2.931592
76.849347
78.085262
761
2.984737
5.410648
1.292547
75.113445
71.657825
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