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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 20 new columns ({'Total night calls', 'Total day minutes', 'Total intl charge', 'Churn', 'State', 'Voice mail plan', 'Total day calls', 'Number vmail messages', 'Total eve minutes', 'Account length', 'Total intl minutes', 'Area code', 'International plan', 'Total night minutes', 'Total night charge', 'Total eve calls', 'Total intl calls', 'Customer service calls', 'Total eve charge', 'Total day charge'}) and 14 missing columns ({'Age', 'Surname', 'Exited', 'CreditScore', 'RowNumber', 'Gender', 'CustomerId', 'Tenure', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Geography', 'NumOfProducts', 'Balance'}).
This happened while the csv dataset builder was generating data using
hf://datasets/jskinner215/multi_kaggle_churn/churn-bigml-20.csv (at revision 1342703e30dad09cef7bd3b1c1aaf7591348c2b2)
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 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
State: string
Account length: int64
Area code: int64
International plan: string
Voice mail plan: string
Number vmail messages: int64
Total day minutes: double
Total day calls: int64
Total day charge: double
Total eve minutes: double
Total eve calls: int64
Total eve charge: double
Total night minutes: double
Total night calls: int64
Total night charge: double
Total intl minutes: double
Total intl calls: int64
Total intl charge: double
Customer service calls: int64
Churn: bool
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2860
to
{'RowNumber': Value(dtype='int64', id=None), 'CustomerId': Value(dtype='int64', id=None), 'Surname': Value(dtype='string', id=None), 'CreditScore': Value(dtype='int64', id=None), 'Geography': Value(dtype='string', id=None), 'Gender': Value(dtype='string', id=None), 'Age': Value(dtype='int64', id=None), 'Tenure': Value(dtype='int64', id=None), 'Balance': Value(dtype='float64', id=None), 'NumOfProducts': Value(dtype='int64', id=None), 'HasCrCard': Value(dtype='int64', id=None), 'IsActiveMember': Value(dtype='int64', id=None), 'EstimatedSalary': Value(dtype='float64', id=None), 'Exited': Value(dtype='int64', id=None)}
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 1321, 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 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 20 new columns ({'Total night calls', 'Total day minutes', 'Total intl charge', 'Churn', 'State', 'Voice mail plan', 'Total day calls', 'Number vmail messages', 'Total eve minutes', 'Account length', 'Total intl minutes', 'Area code', 'International plan', 'Total night minutes', 'Total night charge', 'Total eve calls', 'Total intl calls', 'Customer service calls', 'Total eve charge', 'Total day charge'}) and 14 missing columns ({'Age', 'Surname', 'Exited', 'CreditScore', 'RowNumber', 'Gender', 'CustomerId', 'Tenure', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Geography', 'NumOfProducts', 'Balance'}).
This happened while the csv dataset builder was generating data using
hf://datasets/jskinner215/multi_kaggle_churn/churn-bigml-20.csv (at revision 1342703e30dad09cef7bd3b1c1aaf7591348c2b2)
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.
RowNumber
int64 | CustomerId
int64 | Surname
string | CreditScore
int64 | Geography
string | Gender
string | Age
int64 | Tenure
int64 | Balance
float64 | NumOfProducts
int64 | HasCrCard
int64 | IsActiveMember
int64 | EstimatedSalary
float64 | Exited
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1
| 15,634,602
|
Hargrave
| 619
|
France
|
Female
| 42
| 2
| 0
| 1
| 1
| 1
| 101,348.88
| 1
|
2
| 15,647,311
|
Hill
| 608
|
Spain
|
Female
| 41
| 1
| 83,807.86
| 1
| 0
| 1
| 112,542.58
| 0
|
3
| 15,619,304
|
Onio
| 502
|
France
|
Female
| 42
| 8
| 159,660.8
| 3
| 1
| 0
| 113,931.57
| 1
|
4
| 15,701,354
|
Boni
| 699
|
France
|
Female
| 39
| 1
| 0
| 2
| 0
| 0
| 93,826.63
| 0
|
5
| 15,737,888
|
Mitchell
| 850
|
Spain
|
Female
| 43
| 2
| 125,510.82
| 1
| 1
| 1
| 79,084.1
| 0
|
6
| 15,574,012
|
Chu
| 645
|
Spain
|
Male
| 44
| 8
| 113,755.78
| 2
| 1
| 0
| 149,756.71
| 1
|
7
| 15,592,531
|
Bartlett
| 822
|
France
|
Male
| 50
| 7
| 0
| 2
| 1
| 1
| 10,062.8
| 0
|
8
| 15,656,148
|
Obinna
| 376
|
Germany
|
Female
| 29
| 4
| 115,046.74
| 4
| 1
| 0
| 119,346.88
| 1
|
9
| 15,792,365
|
He
| 501
|
France
|
Male
| 44
| 4
| 142,051.07
| 2
| 0
| 1
| 74,940.5
| 0
|
10
| 15,592,389
|
H?
| 684
|
France
|
Male
| 27
| 2
| 134,603.88
| 1
| 1
| 1
| 71,725.73
| 0
|
11
| 15,767,821
|
Bearce
| 528
|
France
|
Male
| 31
| 6
| 102,016.72
| 2
| 0
| 0
| 80,181.12
| 0
|
12
| 15,737,173
|
Andrews
| 497
|
Spain
|
Male
| 24
| 3
| 0
| 2
| 1
| 0
| 76,390.01
| 0
|
13
| 15,632,264
|
Kay
| 476
|
France
|
Female
| 34
| 10
| 0
| 2
| 1
| 0
| 26,260.98
| 0
|
14
| 15,691,483
|
Chin
| 549
|
France
|
Female
| 25
| 5
| 0
| 2
| 0
| 0
| 190,857.79
| 0
|
15
| 15,600,882
|
Scott
| 635
|
Spain
|
Female
| 35
| 7
| 0
| 2
| 1
| 1
| 65,951.65
| 0
|
16
| 15,643,966
|
Goforth
| 616
|
Germany
|
Male
| 45
| 3
| 143,129.41
| 2
| 0
| 1
| 64,327.26
| 0
|
17
| 15,737,452
|
Romeo
| 653
|
Germany
|
Male
| 58
| 1
| 132,602.88
| 1
| 1
| 0
| 5,097.67
| 1
|
18
| 15,788,218
|
Henderson
| 549
|
Spain
|
Female
| 24
| 9
| 0
| 2
| 1
| 1
| 14,406.41
| 0
|
19
| 15,661,507
|
Muldrow
| 587
|
Spain
|
Male
| 45
| 6
| 0
| 1
| 0
| 0
| 158,684.81
| 0
|
20
| 15,568,982
|
Hao
| 726
|
France
|
Female
| 24
| 6
| 0
| 2
| 1
| 1
| 54,724.03
| 0
|
21
| 15,577,657
|
McDonald
| 732
|
France
|
Male
| 41
| 8
| 0
| 2
| 1
| 1
| 170,886.17
| 0
|
22
| 15,597,945
|
Dellucci
| 636
|
Spain
|
Female
| 32
| 8
| 0
| 2
| 1
| 0
| 138,555.46
| 0
|
23
| 15,699,309
|
Gerasimov
| 510
|
Spain
|
Female
| 38
| 4
| 0
| 1
| 1
| 0
| 118,913.53
| 1
|
24
| 15,725,737
|
Mosman
| 669
|
France
|
Male
| 46
| 3
| 0
| 2
| 0
| 1
| 8,487.75
| 0
|
25
| 15,625,047
|
Yen
| 846
|
France
|
Female
| 38
| 5
| 0
| 1
| 1
| 1
| 187,616.16
| 0
|
26
| 15,738,191
|
Maclean
| 577
|
France
|
Male
| 25
| 3
| 0
| 2
| 0
| 1
| 124,508.29
| 0
|
27
| 15,736,816
|
Young
| 756
|
Germany
|
Male
| 36
| 2
| 136,815.64
| 1
| 1
| 1
| 170,041.95
| 0
|
28
| 15,700,772
|
Nebechi
| 571
|
France
|
Male
| 44
| 9
| 0
| 2
| 0
| 0
| 38,433.35
| 0
|
29
| 15,728,693
|
McWilliams
| 574
|
Germany
|
Female
| 43
| 3
| 141,349.43
| 1
| 1
| 1
| 100,187.43
| 0
|
30
| 15,656,300
|
Lucciano
| 411
|
France
|
Male
| 29
| 0
| 59,697.17
| 2
| 1
| 1
| 53,483.21
| 0
|
31
| 15,589,475
|
Azikiwe
| 591
|
Spain
|
Female
| 39
| 3
| 0
| 3
| 1
| 0
| 140,469.38
| 1
|
32
| 15,706,552
|
Odinakachukwu
| 533
|
France
|
Male
| 36
| 7
| 85,311.7
| 1
| 0
| 1
| 156,731.91
| 0
|
33
| 15,750,181
|
Sanderson
| 553
|
Germany
|
Male
| 41
| 9
| 110,112.54
| 2
| 0
| 0
| 81,898.81
| 0
|
34
| 15,659,428
|
Maggard
| 520
|
Spain
|
Female
| 42
| 6
| 0
| 2
| 1
| 1
| 34,410.55
| 0
|
35
| 15,732,963
|
Clements
| 722
|
Spain
|
Female
| 29
| 9
| 0
| 2
| 1
| 1
| 142,033.07
| 0
|
36
| 15,794,171
|
Lombardo
| 475
|
France
|
Female
| 45
| 0
| 134,264.04
| 1
| 1
| 0
| 27,822.99
| 1
|
37
| 15,788,448
|
Watson
| 490
|
Spain
|
Male
| 31
| 3
| 145,260.23
| 1
| 0
| 1
| 114,066.77
| 0
|
38
| 15,729,599
|
Lorenzo
| 804
|
Spain
|
Male
| 33
| 7
| 76,548.6
| 1
| 0
| 1
| 98,453.45
| 0
|
39
| 15,717,426
|
Armstrong
| 850
|
France
|
Male
| 36
| 7
| 0
| 1
| 1
| 1
| 40,812.9
| 0
|
40
| 15,585,768
|
Cameron
| 582
|
Germany
|
Male
| 41
| 6
| 70,349.48
| 2
| 0
| 1
| 178,074.04
| 0
|
41
| 15,619,360
|
Hsiao
| 472
|
Spain
|
Male
| 40
| 4
| 0
| 1
| 1
| 0
| 70,154.22
| 0
|
42
| 15,738,148
|
Clarke
| 465
|
France
|
Female
| 51
| 8
| 122,522.32
| 1
| 0
| 0
| 181,297.65
| 1
|
43
| 15,687,946
|
Osborne
| 556
|
France
|
Female
| 61
| 2
| 117,419.35
| 1
| 1
| 1
| 94,153.83
| 0
|
44
| 15,755,196
|
Lavine
| 834
|
France
|
Female
| 49
| 2
| 131,394.56
| 1
| 0
| 0
| 194,365.76
| 1
|
45
| 15,684,171
|
Bianchi
| 660
|
Spain
|
Female
| 61
| 5
| 155,931.11
| 1
| 1
| 1
| 158,338.39
| 0
|
46
| 15,754,849
|
Tyler
| 776
|
Germany
|
Female
| 32
| 4
| 109,421.13
| 2
| 1
| 1
| 126,517.46
| 0
|
47
| 15,602,280
|
Martin
| 829
|
Germany
|
Female
| 27
| 9
| 112,045.67
| 1
| 1
| 1
| 119,708.21
| 1
|
48
| 15,771,573
|
Okagbue
| 637
|
Germany
|
Female
| 39
| 9
| 137,843.8
| 1
| 1
| 1
| 117,622.8
| 1
|
49
| 15,766,205
|
Yin
| 550
|
Germany
|
Male
| 38
| 2
| 103,391.38
| 1
| 0
| 1
| 90,878.13
| 0
|
50
| 15,771,873
|
Buccho
| 776
|
Germany
|
Female
| 37
| 2
| 103,769.22
| 2
| 1
| 0
| 194,099.12
| 0
|
51
| 15,616,550
|
Chidiebele
| 698
|
Germany
|
Male
| 44
| 10
| 116,363.37
| 2
| 1
| 0
| 198,059.16
| 0
|
52
| 15,768,193
|
Trevisani
| 585
|
Germany
|
Male
| 36
| 5
| 146,050.97
| 2
| 0
| 0
| 86,424.57
| 0
|
53
| 15,683,553
|
O'Brien
| 788
|
France
|
Female
| 33
| 5
| 0
| 2
| 0
| 0
| 116,978.19
| 0
|
54
| 15,702,298
|
Parkhill
| 655
|
Germany
|
Male
| 41
| 8
| 125,561.97
| 1
| 0
| 0
| 164,040.94
| 1
|
55
| 15,569,590
|
Yoo
| 601
|
Germany
|
Male
| 42
| 1
| 98,495.72
| 1
| 1
| 0
| 40,014.76
| 1
|
56
| 15,760,861
|
Phillipps
| 619
|
France
|
Male
| 43
| 1
| 125,211.92
| 1
| 1
| 1
| 113,410.49
| 0
|
57
| 15,630,053
|
Tsao
| 656
|
France
|
Male
| 45
| 5
| 127,864.4
| 1
| 1
| 0
| 87,107.57
| 0
|
58
| 15,647,091
|
Endrizzi
| 725
|
Germany
|
Male
| 19
| 0
| 75,888.2
| 1
| 0
| 0
| 45,613.75
| 0
|
59
| 15,623,944
|
T'ien
| 511
|
Spain
|
Female
| 66
| 4
| 0
| 1
| 1
| 0
| 1,643.11
| 1
|
60
| 15,804,771
|
Velazquez
| 614
|
France
|
Male
| 51
| 4
| 40,685.92
| 1
| 1
| 1
| 46,775.28
| 0
|
61
| 15,651,280
|
Hunter
| 742
|
Germany
|
Male
| 35
| 5
| 136,857
| 1
| 0
| 0
| 84,509.57
| 0
|
62
| 15,773,469
|
Clark
| 687
|
Germany
|
Female
| 27
| 9
| 152,328.88
| 2
| 0
| 0
| 126,494.82
| 0
|
63
| 15,702,014
|
Jeffrey
| 555
|
Spain
|
Male
| 33
| 1
| 56,084.69
| 2
| 0
| 0
| 178,798.13
| 0
|
64
| 15,751,208
|
Pirozzi
| 684
|
Spain
|
Male
| 56
| 8
| 78,707.16
| 1
| 1
| 1
| 99,398.36
| 0
|
65
| 15,592,461
|
Jackson
| 603
|
Germany
|
Male
| 26
| 4
| 109,166.37
| 1
| 1
| 1
| 92,840.67
| 0
|
66
| 15,789,484
|
Hammond
| 751
|
Germany
|
Female
| 36
| 6
| 169,831.46
| 2
| 1
| 1
| 27,758.36
| 0
|
67
| 15,696,061
|
Brownless
| 581
|
Germany
|
Female
| 34
| 1
| 101,633.04
| 1
| 1
| 0
| 110,431.51
| 0
|
68
| 15,641,582
|
Chibugo
| 735
|
Germany
|
Male
| 43
| 10
| 123,180.01
| 2
| 1
| 1
| 196,673.28
| 0
|
69
| 15,638,424
|
Glauert
| 661
|
Germany
|
Female
| 35
| 5
| 150,725.53
| 2
| 0
| 1
| 113,656.85
| 0
|
70
| 15,755,648
|
Pisano
| 675
|
France
|
Female
| 21
| 8
| 98,373.26
| 1
| 1
| 0
| 18,203
| 0
|
71
| 15,703,793
|
Konovalova
| 738
|
Germany
|
Male
| 58
| 2
| 133,745.44
| 4
| 1
| 0
| 28,373.86
| 1
|
72
| 15,620,344
|
McKee
| 813
|
France
|
Male
| 29
| 6
| 0
| 1
| 1
| 0
| 33,953.87
| 0
|
73
| 15,812,518
|
Palermo
| 657
|
Spain
|
Female
| 37
| 0
| 163,607.18
| 1
| 0
| 1
| 44,203.55
| 0
|
74
| 15,779,052
|
Ballard
| 604
|
Germany
|
Female
| 25
| 5
| 157,780.84
| 2
| 1
| 1
| 58,426.81
| 0
|
75
| 15,770,811
|
Wallace
| 519
|
France
|
Male
| 36
| 9
| 0
| 2
| 0
| 1
| 145,562.4
| 0
|
76
| 15,780,961
|
Cavenagh
| 735
|
France
|
Female
| 21
| 1
| 178,718.19
| 2
| 1
| 0
| 22,388
| 0
|
77
| 15,614,049
|
Hu
| 664
|
France
|
Male
| 55
| 8
| 0
| 2
| 1
| 1
| 139,161.64
| 0
|
78
| 15,662,085
|
Read
| 678
|
France
|
Female
| 32
| 9
| 0
| 1
| 1
| 1
| 148,210.64
| 0
|
79
| 15,575,185
|
Bushell
| 757
|
Spain
|
Male
| 33
| 5
| 77,253.22
| 1
| 0
| 1
| 194,239.63
| 0
|
80
| 15,803,136
|
Postle
| 416
|
Germany
|
Female
| 41
| 10
| 122,189.66
| 2
| 1
| 0
| 98,301.61
| 0
|
81
| 15,706,021
|
Buley
| 665
|
France
|
Female
| 34
| 1
| 96,645.54
| 2
| 0
| 0
| 171,413.66
| 0
|
82
| 15,663,706
|
Leonard
| 777
|
France
|
Female
| 32
| 2
| 0
| 1
| 1
| 0
| 136,458.19
| 1
|
83
| 15,641,732
|
Mills
| 543
|
France
|
Female
| 36
| 3
| 0
| 2
| 0
| 0
| 26,019.59
| 0
|
84
| 15,701,164
|
Onyeorulu
| 506
|
France
|
Female
| 34
| 4
| 90,307.62
| 1
| 1
| 1
| 159,235.29
| 0
|
85
| 15,738,751
|
Beit
| 493
|
France
|
Female
| 46
| 4
| 0
| 2
| 1
| 0
| 1,907.66
| 0
|
86
| 15,805,254
|
Ndukaku
| 652
|
Spain
|
Female
| 75
| 10
| 0
| 2
| 1
| 1
| 114,675.75
| 0
|
87
| 15,762,418
|
Gant
| 750
|
Spain
|
Male
| 22
| 3
| 121,681.82
| 1
| 1
| 0
| 128,643.35
| 1
|
88
| 15,625,759
|
Rowley
| 729
|
France
|
Male
| 30
| 9
| 0
| 2
| 1
| 0
| 151,869.35
| 0
|
89
| 15,622,897
|
Sharpe
| 646
|
France
|
Female
| 46
| 4
| 0
| 3
| 1
| 0
| 93,251.42
| 1
|
90
| 15,767,954
|
Osborne
| 635
|
Germany
|
Female
| 28
| 3
| 81,623.67
| 2
| 1
| 1
| 156,791.36
| 0
|
91
| 15,757,535
|
Heap
| 647
|
Spain
|
Female
| 44
| 5
| 0
| 3
| 1
| 1
| 174,205.22
| 1
|
92
| 15,731,511
|
Ritchie
| 808
|
France
|
Male
| 45
| 7
| 118,626.55
| 2
| 1
| 0
| 147,132.46
| 0
|
93
| 15,809,248
|
Cole
| 524
|
France
|
Female
| 36
| 10
| 0
| 2
| 1
| 0
| 109,614.57
| 0
|
94
| 15,640,635
|
Capon
| 769
|
France
|
Male
| 29
| 8
| 0
| 2
| 1
| 1
| 172,290.61
| 0
|
95
| 15,676,966
|
Capon
| 730
|
Spain
|
Male
| 42
| 4
| 0
| 2
| 0
| 1
| 85,982.47
| 0
|
96
| 15,699,461
|
Fiorentini
| 515
|
Spain
|
Male
| 35
| 10
| 176,273.95
| 1
| 0
| 1
| 121,277.78
| 0
|
97
| 15,738,721
|
Graham
| 773
|
Spain
|
Male
| 41
| 9
| 102,827.44
| 1
| 0
| 1
| 64,595.25
| 0
|
98
| 15,693,683
|
Yuille
| 814
|
Germany
|
Male
| 29
| 8
| 97,086.4
| 2
| 1
| 1
| 197,276.13
| 0
|
99
| 15,604,348
|
Allard
| 710
|
Spain
|
Male
| 22
| 8
| 0
| 2
| 0
| 0
| 99,645.04
| 0
|
100
| 15,633,059
|
Fanucci
| 413
|
France
|
Male
| 34
| 9
| 0
| 2
| 0
| 0
| 6,534.18
| 0
|
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