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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 ({'Coolant pressure', 'lub oil temp', 'Lub oil pressure', 'Engine rpm', 'Coolant temp', 'Fuel pressure'}).

This happened while the csv dataset builder was generating data using

hf://datasets/sudha1726/predictive-maintainanace/y_train.csv (at revision 41f46cfd6966f9c2b86fcaaed2dedd914a250587)

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 1455, 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 1054, 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 ({'Coolant pressure', 'lub oil temp', 'Lub oil pressure', 'Engine rpm', 'Coolant temp', 'Fuel pressure'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/sudha1726/predictive-maintainanace/y_train.csv (at revision 41f46cfd6966f9c2b86fcaaed2dedd914a250587)
              
              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
493
3.962344
6.331438
1.552196
74.842183
83.683952
525
3.96838
5.55898
1.551743
76.86155
84.501699
1,024
2.965898
5.425383
2.703365
75.55213
84.191483
667
2.5034
6.063999
1.846347
74.967748
92.878094
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2.718334
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2.936233
77.23312
69.413782
554
4.13539
4.492539
1.709781
81.371618
72.442962
452
3.444236
5.624988
2.314415
74.08515
78.498373
733
1.910396
5.468187
2.262233
78.098826
85.189804
902
2.405149
5.656221
1.518731
76.989336
78.316051
653
2.475161
6.625957
2.479978
76.734286
82.029732
1,064
5.890638
3.596829
3.646584
78.551553
74.595603
627
3.195913
8.27961
2.452966
75.604219
77.604118
972
3.124232
6.554847
1.894652
80.572759
76.736655
586
3.201429
6.238381
2.001394
75.470123
83.80766
724
2.533632
2.091088
1.567528
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557
2.776649
4.354502
1.696256
78.291703
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476
2.649686
4.161549
1.703113
78.773388
75.599068
799
3.523895
9.54977
1.500989
75.616042
75.922404
567
4.941227
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1.680248
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71.482574
426
4.075708
3.017942
0.869609
74.223857
72.51437
625
1.895039
7.779939
2.220259
76.738191
84.123107
1,282
2.361006
9.796271
2.154175
76.151516
85.371106
503
4.31111
5.25437
2.327144
76.990353
79.438234
725
3.877439
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1.862608
77.244127
91.933004
387
4.610814
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1.243252
74.921241
74.986672
757
5.542156
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2.924863
77.076799
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447
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5.506936
3.03644
75.440778
80.237137
1,077
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3.548356
77.168801
83.397346
1,046
2.299535
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2.041204
76.687789
76.233353
706
3.621096
6.412525
1.778912
76.301357
76.943549
765
3.668062
11.881688
1.998997
75.488175
76.669005
655
3.049225
8.558617
2.135557
74.355159
79.545372
953
3.782185
9.728329
2.883632
75.485327
78.793678
868
3.617018
6.6987
0.998094
78.230045
74.70599
952
4.848723
9.970973
1.269681
75.162439
71.103928
813
2.776082
6.992621
1.834924
77.987906
79.860005
918
2.729961
5.481438
2.350554
75.981169
78.129019
658
3.686021
6.034731
2.220792
75.808248
72.471546
873
2.578445
6.660782
2.403702
76.670656
86.479299
1,113
2.58823
8.252509
2.978393
78.472517
86.36104
767
4.52045
4.59382
2.720206
78.358162
81.526177
755
2.854142
7.044979
1.041464
75.786777
73.031879
1,059
3.427111
7.15235
1.716316
75.647299
80.853617
876
4.212847
6.616102
1.767898
76.09046
79.820441
902
4.31507
9.896735
2.120898
76.263232
80.062084
979
2.815908
3.574738
0.903005
75.811307
83.480193
958
4.006515
7.951343
3.16831
78.424014
89.089093
894
2.577116
7.534326
2.154862
78.703941
82.958127
686
2.281505
4.581041
2.43873
74.081288
69.072213
443
2.513208
10.573309
2.979241
77.189589
73.451002
895
3.743431
4.018061
2.700538
76.395981
86.87131
651
4.051047
6.534119
1.440997
76.693779
83.677409
598
1.166303
8.209485
2.452161
77.886492
72.897615
804
1.922464
3.879554
1.753843
76.863065
80.887817
1,232
2.019945
6.066875
2.794574
76.121508
86.518034
794
5.05797
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2.21297
76.136397
72.946943
1,283
3.177311
3.229891
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75.357501
84.393269
621
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700
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613
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765
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80.6567
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527
2.59868
6.134264
1.183945
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799
4.164921
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2.763638
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555
2.391767
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585
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2.471571
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84.135194
406
2.936677
6.800314
2.553607
77.583227
84.24391
631
3.180473
4.588038
2.058372
74.685507
75.28811
960
2.503391
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2.455641
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70.609083
695
2.876399
3.799518
1.143531
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612
4.442201
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436
3.897132
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76.391635
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1,032
3.893372
5.321953
2.281724
74.677099
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633
3.271553
4.541113
2.378214
77.596503
68.345581
696
5.005687
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1.141288
74.492698
80.861036
728
3.506826
6.205877
1.432782
75.858182
77.318968
512
1.860994
6.326147
1.128839
76.624816
74.157281
989
2.335419
1.455418
3.091981
73.601978
69.178671
677
3.169062
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88.296283
639
2.627508
4.99165
1.810122
75.630389
82.880166
597
2.139896
4.53844
2.375392
72.851064
81.837688
692
3.62863
5.532653
1.644915
73.472109
74.684839
681
4.210147
7.116
2.587141
78.444075
78.278373
1,152
3.952011
4.955447
1.906075
76.271928
73.479152
901
5.10985
5.744217
3.179771
78.026904
73.782058
653
3.311074
6.214654
2.805208
74.697678
74.945373
638
2.615483
5.068612
1.236516
76.025738
73.068794
539
3.333235
8.204547
2.231954
77.460499
86.441718
638
5.078508
5.132751
2.227829
78.191478
74.504117
485
4.847598
8.725143
1.439916
76.75493
79.182832
782
2.725019
6.397499
3.064009
75.900124
71.097198
525
3.457539
11.007026
1.673039
75.503928
68.388645
506
3.835086
4.604191
3.525126
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76.614601
834
2.582245
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1.665253
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1,345
4.964371
7.929235
1.116681
77.675048
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812
2.838577
9.361612
1.36401
75.622287
74.239421
731
4.926004
8.3518
2.251393
77.653847
91.545453
837
4.484271
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1.540788
76.860383
86.740988
1,161
2.350326
8.389375
1.820227
77.581365
86.847544
929
4.107049
7.928831
1.506508
77.490943
84.329449
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