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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'})

This happened while the csv dataset builder was generating data using

hf://datasets/sasipriyank/predectivemlops/engine_data.csv (at revision a11393d57a112e59bcc49a15f449e9df37a57fc8)

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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Engine rpm: int64
              Lub oil pressure: double
              Fuel pressure: double
              Coolant pressure: double
              lub oil temp: double
              Coolant temp: double
              Engine Condition: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1152
              to
              {'Lub oil pressure': Value('string'), 'Fuel pressure': Value('string'), 'Coolant pressure': Value('string'), 'lub oil temp': Value('string'), 'Coolant temp': Value('string'), 'Engine rpm': Value('string')}
              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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/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'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/sasipriyank/predectivemlops/engine_data.csv (at revision a11393d57a112e59bcc49a15f449e9df37a57fc8)
              
              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)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Lub oil pressure
string
Fuel pressure
string
Coolant pressure
string
lub oil temp
string
Coolant temp
string
Engine rpm
string
4.571150807
5.935587214
2.913508929
76.68700184
81.95892936
592
3.115581429
6.517538143
2.137477746
80.23114932
80.01926976
1072
5.391784807
11.5602246
5.492189365
77.10945287
86.5044749
618
4.682864253
5.11326734
3.031892914
78.30227801
87.89029349
808
2.750751044
3.80981655
1.013923491
77.81938658
84.80772676
693
2.312080704
6.255842284
1.672838186
75.87349237
81.6134238
398
2.095878913
7.660523074
2.673228972
75.93317247
75.26196988
567
2.910371908
6.847357457
1.791054699
75.55878853
69.39855701
632
3.155966007
7.486897844
1.487094028
76.86611087
75.75936577
1166
2.700518816
5.042276982
3.046871352
77.55113066
74.87751656
599
4.934090073
6.026293511
1.457255218
78.00369981
85.32268011
1284
2.721544553
6.591911473
1.568230811
76.71358737
85.37873618
530
2.055103607
4.396100517
4.850311084
75.79858696
75.92659444
464
3.220935581
6.022069907
1.240667598
76.51066818
86.80677091
646
2.487784062
6.489082498
1.937522121
77.91166093
68.15731882
495
0.462023636
4.317992793
4.20791116
78.11260065
83.50823721
654
4.878543795
4.575834135
1.345543801
87.06757739
75.56200356
920
4.500803469
4.495604877
1.160997651
77.65431242
71.96201381
576
4.421013717
7.489076494
2.021791046
73.8544458
67.80928918
770
5.076494544
4.545233898
4.48812313
75.5759409
80.17492264
825
4.941298477
4.889321453
1.524629007
76.54827988
82.79462861
650
4.970143117
5.679367921
1.199624607
75.52461905
78.36680931
633
3.252792314
5.99829684
1.482092362
76.36099608
74.56754872
591
3.949073699
15.22929807
2.137883252
75.42959888
75.24259251
868
3.910785642
3.365309743
2.734904635
78.01340968
82.05037362
672
1.907012767
9.515350186
1.578752661
75.19520903
74.24983946
447
4.098832269
5.167827931
2.920105763
77.16625897
76.27728114
690
2.908997177
7.195431409
1.435878782
76.78164974
72.26123278
1111
5.407791746
11.24281896
2.753037765
75.43395975
74.40004988
1141
3.388750796
8.606893148
2.182809232
77.54177822
68.60575394
841
2.924549842
4.710538472
2.906769036
86.31581697
83.09241146
725
2.577537492
5.168560934
2.085452844
83.06345494
86.05565543
913
4.306520713
4.539489206
1.652642843
78.58772319
82.5763491
1158
3.041438915
7.217041958
1.496423393
76.64844313
77.31373567
1147
2.944011557
5.148304739
0.884073817
76.29504331
66.90570264
1255
1.790913346
6.910742215
2.754959086
77.51149961
69.81532063
977
2.964359439
2.488098005
2.585564233
76.47554186
75.24074763
477
5.032921861
14.10747176
1.771239137
77.83934971
88.59752246
1241
3.894732157
7.142922079
2.416514174
81.39502721
80.66447311
841
3.501495461
5.058909741
1.612818613
76.08253378
76.06596592
869
1.77694736
4.55955391
1.293648198
76.66128551
76.76576713
1164
6.168664129
3.179575469
1.365877236
78.03118373
79.36605018
533
4.129580145
5.582654023
1.743125678
74.22883381
82.84692619
2022
2.049522202
6.551194747
1.237310974
81.67058321
83.41871863
882
2.540692603
5.775913792
3.131384927
82.00756972
69.57059285
1266
1.854605238
7.957275075
4.570949497
77.35571801
80.73435075
730
4.183711117
7.13686938
2.776893627
77.18754137
79.17833622
671
3.007204652
9.451811043
1.553212479
76.81777437
70.94383733
390
1.86503738
4.309897002
1.462881509
75.3632435
73.27006312
1218
3.645413297
6.938714975
1.719368583
76.66058612
72.77808768
429
4.126548026
7.037687949
1.581362637
85.50506439
72.75752415
805
2.312382992
1.464872594
1.512579853
76.83109519
83.23966098
1172
2.149429611
9.005664741
2.011843936
83.98443599
83.41351224
998
5.364933592
5.382517668
1.952156588
77.65829494
80.65242769
791
2.932798873
4.64397581
2.470648989
85.19900857
91.18008058
751
3.939991879
3.221985605
1.196916
80.61394434
73.99585924
1040
4.909902492
4.556781073
3.936352831
74.5981944
78.11637884
576
3.117680552
4.713397622
2.280614837
78.0153261
75.98546917
576
4.705015733
5.242661437
1.51377409
83.02781118
71.24984553
975
2.923823814
8.528407415
1.638165763
75.1957196
85.101773
821
2.252527233
7.415194738
0.515570248
76.25259086
91.60161014
652
2.680463762
4.69809188
2.604655092
74.62704515
85.07227462
597
3.848494602
6.152212152
1.700257087
77.81026777
72.18098571
519
4.011249096
2.6038419
3.867857243
76.63942681
68.16844867
977
4.182577794
4.031174437
1.575276432
76.45705878
75.38016176
533
3.306585451
6.099863731
1.890512728
76.68293614
70.27972898
784
2.515099498
6.287310825
1.199893815
76.27053573
69.61658458
649
5.756821081
9.233288595
2.185571902
77.04087642
72.76650446
1041
2.499077673
2.605464921
2.979263608
75.72842967
76.33419344
1247
2.36037043
6.952857726
3.541114157
76.06259779
79.18958372
979
1.93441491
4.636650274
1.935479939
80.26301037
79.04495165
855
4.810722584
4.487639878
1.91594843
85.52997522
78.62239524
1031
5.246488113
4.491023955
1.631800538
76.57553977
68.07049629
678
4.596713219
8.118890312
2.416075421
78.65720947
77.46713744
1190
4.673194032
5.43569624
1.468899056
75.27419029
83.80354985
581
4.748495662
5.806730317
2.791687329
88.3040141
82.11916933
821
3.328394282
9.037388784
3.324936855
77.67604754
73.74955535
964
3.609384029
4.686886885
2.670477907
77.66065232
79.53215595
764
3.881194087
7.998058391
3.295330217
77.95381773
85.74277551
880
5.052811105
6.155270811
4.03462998
75.72129257
85.84755416
1412
3.736931893
6.099670669
1.017987444
75.20365047
82.57459989
1206
3.76317708
4.699220797
1.56022991
84.6681539
72.93052788
457
1.93275284
7.696412673
1.118915298
75.8270111
88.38721105
1176
3.333600609
8.367737992
2.841853155
75.57932845
67.56606961
612
2.613012547
7.390181899
3.069503339
76.34255798
87.8474422
658
2.618627562
15.61449265
1.281945119
77.74729819
68.90203951
475
2.59155178
7.383816959
2.33410448
76.62804159
76.08376101
529
3.620408198
1.811607536
3.197963606
76.19940115
76.46016067
910
1.977110036
7.32878256
1.988495964
82.77830081
71.81278026
1086
3.178755046
12.09133308
2.052876541
74.26058792
68.65545338
577
2.428413432
11.58370845
3.204227042
76.71252417
83.41625234
598
2.20405502
4.432296498
2.000236648
77.38186668
82.54855027
850
2.531922016
8.869784827
0.875401918
75.9214321
78.18301799
1003
3.41805478
6.338584239
1.133487181
77.52058315
78.28927617
752
2.318481441
11.63922019
2.342058547
76.53774241
94.04875781
1000
2.651584097
7.795454353
1.460266682
75.96699028
80.55247065
876
2.295944984
4.221203379
5.858005136
76.00485271
88.28611486
868
2.447431979
8.028257416
2.123220975
77.25014976
73.38370256
1280
2.357595399
13.50897593
1.371742133
81.55440945
79.38250434
728
2.716683867
7.464623542
2.99049275
75.83105129
77.87148519
961
End of preview.