The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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)

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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
End of preview.