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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 3 missing columns ({'RowNumber', 'Surname', 'CustomerId'})
This happened while the csv dataset builder was generating data using
hf://datasets/krisnadwipaj/customer-churn/df_clean.csv (at revision 7aac49b287040da43095c1fbb7a14067fca9844e)
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
CreditScore: int64
Geography: string
Gender: string
Age: int64
Tenure: int64
Balance: double
NumOfProducts: int64
HasCrCard: int64
IsActiveMember: int64
EstimatedSalary: double
Exited: int64
Complain: int64
Satisfaction Score: int64
Card Type: string
Point Earned: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2027
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), 'Complain': Value(dtype='int64', id=None), 'Satisfaction Score': Value(dtype='int64', id=None), 'Card Type': Value(dtype='string', id=None), 'Point Earned': 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 3 missing columns ({'RowNumber', 'Surname', 'CustomerId'})
This happened while the csv dataset builder was generating data using
hf://datasets/krisnadwipaj/customer-churn/df_clean.csv (at revision 7aac49b287040da43095c1fbb7a14067fca9844e)
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 | Complain
int64 | Satisfaction Score
int64 | Card Type
string | Point Earned
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1
| 15,634,602
|
Hargrave
| 619
|
France
|
Female
| 42
| 2
| 0
| 1
| 1
| 1
| 101,348.88
| 1
| 1
| 2
|
DIAMOND
| 464
|
2
| 15,647,311
|
Hill
| 608
|
Spain
|
Female
| 41
| 1
| 83,807.86
| 1
| 0
| 1
| 112,542.58
| 0
| 1
| 3
|
DIAMOND
| 456
|
3
| 15,619,304
|
Onio
| 502
|
France
|
Female
| 42
| 8
| 159,660.8
| 3
| 1
| 0
| 113,931.57
| 1
| 1
| 3
|
DIAMOND
| 377
|
4
| 15,701,354
|
Boni
| 699
|
France
|
Female
| 39
| 1
| 0
| 2
| 0
| 0
| 93,826.63
| 0
| 0
| 5
|
GOLD
| 350
|
5
| 15,737,888
|
Mitchell
| 850
|
Spain
|
Female
| 43
| 2
| 125,510.82
| 1
| 1
| 1
| 79,084.1
| 0
| 0
| 5
|
GOLD
| 425
|
6
| 15,574,012
|
Chu
| 645
|
Spain
|
Male
| 44
| 8
| 113,755.78
| 2
| 1
| 0
| 149,756.71
| 1
| 1
| 5
|
DIAMOND
| 484
|
7
| 15,592,531
|
Bartlett
| 822
|
France
|
Male
| 50
| 7
| 0
| 2
| 1
| 1
| 10,062.8
| 0
| 0
| 2
|
SILVER
| 206
|
8
| 15,656,148
|
Obinna
| 376
|
Germany
|
Female
| 29
| 4
| 115,046.74
| 4
| 1
| 0
| 119,346.88
| 1
| 1
| 2
|
DIAMOND
| 282
|
9
| 15,792,365
|
He
| 501
|
France
|
Male
| 44
| 4
| 142,051.07
| 2
| 0
| 1
| 74,940.5
| 0
| 0
| 3
|
GOLD
| 251
|
10
| 15,592,389
|
H?
| 684
|
France
|
Male
| 27
| 2
| 134,603.88
| 1
| 1
| 1
| 71,725.73
| 0
| 0
| 3
|
GOLD
| 342
|
11
| 15,767,821
|
Bearce
| 528
|
France
|
Male
| 31
| 6
| 102,016.72
| 2
| 0
| 0
| 80,181.12
| 0
| 0
| 3
|
GOLD
| 264
|
12
| 15,737,173
|
Andrews
| 497
|
Spain
|
Male
| 24
| 3
| 0
| 2
| 1
| 0
| 76,390.01
| 0
| 0
| 3
|
GOLD
| 249
|
13
| 15,632,264
|
Kay
| 476
|
France
|
Female
| 34
| 10
| 0
| 2
| 1
| 0
| 26,260.98
| 0
| 0
| 3
|
SILVER
| 119
|
14
| 15,691,483
|
Chin
| 549
|
France
|
Female
| 25
| 5
| 0
| 2
| 0
| 0
| 190,857.79
| 0
| 0
| 3
|
PLATINUM
| 549
|
15
| 15,600,882
|
Scott
| 635
|
Spain
|
Female
| 35
| 7
| 0
| 2
| 1
| 1
| 65,951.65
| 0
| 0
| 2
|
GOLD
| 318
|
16
| 15,643,966
|
Goforth
| 616
|
Germany
|
Male
| 45
| 3
| 143,129.41
| 2
| 0
| 1
| 64,327.26
| 0
| 0
| 5
|
GOLD
| 308
|
17
| 15,737,452
|
Romeo
| 653
|
Germany
|
Male
| 58
| 1
| 132,602.88
| 1
| 1
| 0
| 5,097.67
| 1
| 0
| 2
|
SILVER
| 163
|
18
| 15,788,218
|
Henderson
| 549
|
Spain
|
Female
| 24
| 9
| 0
| 2
| 1
| 1
| 14,406.41
| 0
| 0
| 3
|
SILVER
| 544
|
19
| 15,661,507
|
Muldrow
| 587
|
Spain
|
Male
| 45
| 6
| 0
| 1
| 0
| 0
| 158,684.81
| 0
| 0
| 3
|
PLATINUM
| 732
|
20
| 15,568,982
|
Hao
| 726
|
France
|
Female
| 24
| 6
| 0
| 2
| 1
| 1
| 54,724.03
| 0
| 0
| 4
|
GOLD
| 477
|
21
| 15,577,657
|
McDonald
| 732
|
France
|
Male
| 41
| 8
| 0
| 2
| 1
| 1
| 170,886.17
| 0
| 0
| 3
|
PLATINUM
| 568
|
22
| 15,597,945
|
Dellucci
| 636
|
Spain
|
Female
| 32
| 8
| 0
| 2
| 1
| 0
| 138,555.46
| 0
| 0
| 2
|
DIAMOND
| 336
|
23
| 15,699,309
|
Gerasimov
| 510
|
Spain
|
Female
| 38
| 4
| 0
| 1
| 1
| 0
| 118,913.53
| 1
| 1
| 2
|
DIAMOND
| 887
|
24
| 15,725,737
|
Mosman
| 669
|
France
|
Male
| 46
| 3
| 0
| 2
| 0
| 1
| 8,487.75
| 0
| 0
| 2
|
SILVER
| 665
|
25
| 15,625,047
|
Yen
| 846
|
France
|
Female
| 38
| 5
| 0
| 1
| 1
| 1
| 187,616.16
| 0
| 0
| 3
|
PLATINUM
| 225
|
26
| 15,738,191
|
Maclean
| 577
|
France
|
Male
| 25
| 3
| 0
| 2
| 0
| 1
| 124,508.29
| 0
| 0
| 3
|
PLATINUM
| 659
|
27
| 15,736,816
|
Young
| 756
|
Germany
|
Male
| 36
| 2
| 136,815.64
| 1
| 1
| 1
| 170,041.95
| 0
| 0
| 5
|
DIAMOND
| 236
|
28
| 15,700,772
|
Nebechi
| 571
|
France
|
Male
| 44
| 9
| 0
| 2
| 0
| 0
| 38,433.35
| 0
| 0
| 4
|
PLATINUM
| 448
|
29
| 15,728,693
|
McWilliams
| 574
|
Germany
|
Female
| 43
| 3
| 141,349.43
| 1
| 1
| 1
| 100,187.43
| 0
| 0
| 5
|
DIAMOND
| 499
|
30
| 15,656,300
|
Lucciano
| 411
|
France
|
Male
| 29
| 0
| 59,697.17
| 2
| 1
| 1
| 53,483.21
| 0
| 0
| 2
|
GOLD
| 343
|
31
| 15,589,475
|
Azikiwe
| 591
|
Spain
|
Female
| 39
| 3
| 0
| 3
| 1
| 0
| 140,469.38
| 1
| 1
| 3
|
DIAMOND
| 298
|
32
| 15,706,552
|
Odinakachukwu
| 533
|
France
|
Male
| 36
| 7
| 85,311.7
| 1
| 0
| 1
| 156,731.91
| 0
| 0
| 5
|
SILVER
| 628
|
33
| 15,750,181
|
Sanderson
| 553
|
Germany
|
Male
| 41
| 9
| 110,112.54
| 2
| 0
| 0
| 81,898.81
| 0
| 0
| 3
|
GOLD
| 611
|
34
| 15,659,428
|
Maggard
| 520
|
Spain
|
Female
| 42
| 6
| 0
| 2
| 1
| 1
| 34,410.55
| 0
| 0
| 2
|
SILVER
| 922
|
35
| 15,732,963
|
Clements
| 722
|
Spain
|
Female
| 29
| 9
| 0
| 2
| 1
| 1
| 142,033.07
| 0
| 0
| 5
|
PLATINUM
| 276
|
36
| 15,794,171
|
Lombardo
| 475
|
France
|
Female
| 45
| 0
| 134,264.04
| 1
| 1
| 0
| 27,822.99
| 1
| 1
| 1
|
DIAMOND
| 877
|
37
| 15,788,448
|
Watson
| 490
|
Spain
|
Male
| 31
| 3
| 145,260.23
| 1
| 0
| 1
| 114,066.77
| 0
| 0
| 4
|
SILVER
| 471
|
38
| 15,729,599
|
Lorenzo
| 804
|
Spain
|
Male
| 33
| 7
| 76,548.6
| 1
| 0
| 1
| 98,453.45
| 0
| 0
| 2
|
GOLD
| 994
|
39
| 15,717,426
|
Armstrong
| 850
|
France
|
Male
| 36
| 7
| 0
| 1
| 1
| 1
| 40,812.9
| 0
| 0
| 3
|
GOLD
| 944
|
40
| 15,585,768
|
Cameron
| 582
|
Germany
|
Male
| 41
| 6
| 70,349.48
| 2
| 0
| 1
| 178,074.04
| 0
| 0
| 5
|
GOLD
| 419
|
41
| 15,619,360
|
Hsiao
| 472
|
Spain
|
Male
| 40
| 4
| 0
| 1
| 1
| 0
| 70,154.22
| 0
| 0
| 4
|
DIAMOND
| 545
|
42
| 15,738,148
|
Clarke
| 465
|
France
|
Female
| 51
| 8
| 122,522.32
| 1
| 0
| 0
| 181,297.65
| 1
| 1
| 5
|
SILVER
| 828
|
43
| 15,687,946
|
Osborne
| 556
|
France
|
Female
| 61
| 2
| 117,419.35
| 1
| 1
| 1
| 94,153.83
| 0
| 0
| 3
|
PLATINUM
| 789
|
44
| 15,755,196
|
Lavine
| 834
|
France
|
Female
| 49
| 2
| 131,394.56
| 1
| 0
| 0
| 194,365.76
| 1
| 1
| 2
|
GOLD
| 567
|
45
| 15,684,171
|
Bianchi
| 660
|
Spain
|
Female
| 61
| 5
| 155,931.11
| 1
| 1
| 1
| 158,338.39
| 0
| 0
| 1
|
PLATINUM
| 884
|
46
| 15,754,849
|
Tyler
| 776
|
Germany
|
Female
| 32
| 4
| 109,421.13
| 2
| 1
| 1
| 126,517.46
| 0
| 0
| 1
|
SILVER
| 296
|
47
| 15,602,280
|
Martin
| 829
|
Germany
|
Female
| 27
| 9
| 112,045.67
| 1
| 1
| 1
| 119,708.21
| 1
| 1
| 3
|
DIAMOND
| 779
|
48
| 15,771,573
|
Okagbue
| 637
|
Germany
|
Female
| 39
| 9
| 137,843.8
| 1
| 1
| 1
| 117,622.8
| 1
| 1
| 1
|
SILVER
| 730
|
49
| 15,766,205
|
Yin
| 550
|
Germany
|
Male
| 38
| 2
| 103,391.38
| 1
| 0
| 1
| 90,878.13
| 0
| 0
| 3
|
DIAMOND
| 960
|
50
| 15,771,873
|
Buccho
| 776
|
Germany
|
Female
| 37
| 2
| 103,769.22
| 2
| 1
| 0
| 194,099.12
| 0
| 0
| 1
|
SILVER
| 268
|
51
| 15,616,550
|
Chidiebele
| 698
|
Germany
|
Male
| 44
| 10
| 116,363.37
| 2
| 1
| 0
| 198,059.16
| 0
| 0
| 4
|
SILVER
| 829
|
52
| 15,768,193
|
Trevisani
| 585
|
Germany
|
Male
| 36
| 5
| 146,050.97
| 2
| 0
| 0
| 86,424.57
| 0
| 0
| 3
|
SILVER
| 665
|
53
| 15,683,553
|
O'Brien
| 788
|
France
|
Female
| 33
| 5
| 0
| 2
| 0
| 0
| 116,978.19
| 0
| 0
| 3
|
GOLD
| 982
|
54
| 15,702,298
|
Parkhill
| 655
|
Germany
|
Male
| 41
| 8
| 125,561.97
| 1
| 0
| 0
| 164,040.94
| 1
| 1
| 3
|
SILVER
| 343
|
55
| 15,569,590
|
Yoo
| 601
|
Germany
|
Male
| 42
| 1
| 98,495.72
| 1
| 1
| 0
| 40,014.76
| 1
| 1
| 2
|
DIAMOND
| 225
|
56
| 15,760,861
|
Phillipps
| 619
|
France
|
Male
| 43
| 1
| 125,211.92
| 1
| 1
| 1
| 113,410.49
| 0
| 0
| 5
|
SILVER
| 657
|
57
| 15,630,053
|
Tsao
| 656
|
France
|
Male
| 45
| 5
| 127,864.4
| 1
| 1
| 0
| 87,107.57
| 0
| 0
| 2
|
PLATINUM
| 221
|
58
| 15,647,091
|
Endrizzi
| 725
|
Germany
|
Male
| 19
| 0
| 75,888.2
| 1
| 0
| 0
| 45,613.75
| 0
| 0
| 2
|
PLATINUM
| 406
|
59
| 15,623,944
|
T'ien
| 511
|
Spain
|
Female
| 66
| 4
| 0
| 1
| 1
| 0
| 1,643.11
| 1
| 1
| 5
|
SILVER
| 549
|
60
| 15,804,771
|
Velazquez
| 614
|
France
|
Male
| 51
| 4
| 40,685.92
| 1
| 1
| 1
| 46,775.28
| 0
| 0
| 4
|
PLATINUM
| 954
|
61
| 15,651,280
|
Hunter
| 742
|
Germany
|
Male
| 35
| 5
| 136,857
| 1
| 0
| 0
| 84,509.57
| 0
| 0
| 4
|
GOLD
| 823
|
62
| 15,773,469
|
Clark
| 687
|
Germany
|
Female
| 27
| 9
| 152,328.88
| 2
| 0
| 0
| 126,494.82
| 0
| 0
| 5
|
DIAMOND
| 769
|
63
| 15,702,014
|
Jeffrey
| 555
|
Spain
|
Male
| 33
| 1
| 56,084.69
| 2
| 0
| 0
| 178,798.13
| 0
| 0
| 5
|
PLATINUM
| 481
|
64
| 15,751,208
|
Pirozzi
| 684
|
Spain
|
Male
| 56
| 8
| 78,707.16
| 1
| 1
| 1
| 99,398.36
| 0
| 0
| 4
|
SILVER
| 903
|
65
| 15,592,461
|
Jackson
| 603
|
Germany
|
Male
| 26
| 4
| 109,166.37
| 1
| 1
| 1
| 92,840.67
| 0
| 0
| 3
|
PLATINUM
| 882
|
66
| 15,789,484
|
Hammond
| 751
|
Germany
|
Female
| 36
| 6
| 169,831.46
| 2
| 1
| 1
| 27,758.36
| 0
| 1
| 5
|
GOLD
| 379
|
67
| 15,696,061
|
Brownless
| 581
|
Germany
|
Female
| 34
| 1
| 101,633.04
| 1
| 1
| 0
| 110,431.51
| 0
| 1
| 3
|
PLATINUM
| 469
|
68
| 15,641,582
|
Chibugo
| 735
|
Germany
|
Male
| 43
| 10
| 123,180.01
| 2
| 1
| 1
| 196,673.28
| 0
| 1
| 3
|
PLATINUM
| 683
|
69
| 15,638,424
|
Glauert
| 661
|
Germany
|
Female
| 35
| 5
| 150,725.53
| 2
| 0
| 1
| 113,656.85
| 0
| 1
| 1
|
GOLD
| 770
|
70
| 15,755,648
|
Pisano
| 675
|
France
|
Female
| 21
| 8
| 98,373.26
| 1
| 1
| 0
| 18,203
| 0
| 0
| 5
|
SILVER
| 985
|
71
| 15,703,793
|
Konovalova
| 738
|
Germany
|
Male
| 58
| 2
| 133,745.44
| 4
| 1
| 0
| 28,373.86
| 1
| 1
| 4
|
GOLD
| 725
|
72
| 15,620,344
|
McKee
| 813
|
France
|
Male
| 29
| 6
| 0
| 1
| 1
| 0
| 33,953.87
| 0
| 0
| 3
|
PLATINUM
| 259
|
73
| 15,812,518
|
Palermo
| 657
|
Spain
|
Female
| 37
| 0
| 163,607.18
| 1
| 0
| 1
| 44,203.55
| 0
| 0
| 2
|
GOLD
| 979
|
74
| 15,779,052
|
Ballard
| 604
|
Germany
|
Female
| 25
| 5
| 157,780.84
| 2
| 1
| 1
| 58,426.81
| 0
| 0
| 2
|
SILVER
| 988
|
75
| 15,770,811
|
Wallace
| 519
|
France
|
Male
| 36
| 9
| 0
| 2
| 0
| 1
| 145,562.4
| 0
| 0
| 2
|
PLATINUM
| 833
|
76
| 15,780,961
|
Cavenagh
| 735
|
France
|
Female
| 21
| 1
| 178,718.19
| 2
| 1
| 0
| 22,388
| 0
| 0
| 1
|
PLATINUM
| 527
|
77
| 15,614,049
|
Hu
| 664
|
France
|
Male
| 55
| 8
| 0
| 2
| 1
| 1
| 139,161.64
| 0
| 0
| 5
|
SILVER
| 220
|
78
| 15,662,085
|
Read
| 678
|
France
|
Female
| 32
| 9
| 0
| 1
| 1
| 1
| 148,210.64
| 0
| 0
| 4
|
GOLD
| 983
|
79
| 15,575,185
|
Bushell
| 757
|
Spain
|
Male
| 33
| 5
| 77,253.22
| 1
| 0
| 1
| 194,239.63
| 0
| 0
| 3
|
SILVER
| 582
|
80
| 15,803,136
|
Postle
| 416
|
Germany
|
Female
| 41
| 10
| 122,189.66
| 2
| 1
| 0
| 98,301.61
| 0
| 0
| 3
|
SILVER
| 281
|
81
| 15,706,021
|
Buley
| 665
|
France
|
Female
| 34
| 1
| 96,645.54
| 2
| 0
| 0
| 171,413.66
| 0
| 0
| 3
|
DIAMOND
| 798
|
82
| 15,663,706
|
Leonard
| 777
|
France
|
Female
| 32
| 2
| 0
| 1
| 1
| 0
| 136,458.19
| 1
| 1
| 5
|
SILVER
| 917
|
83
| 15,641,732
|
Mills
| 543
|
France
|
Female
| 36
| 3
| 0
| 2
| 0
| 0
| 26,019.59
| 0
| 0
| 2
|
DIAMOND
| 255
|
84
| 15,701,164
|
Onyeorulu
| 506
|
France
|
Female
| 34
| 4
| 90,307.62
| 1
| 1
| 1
| 159,235.29
| 0
| 0
| 4
|
SILVER
| 511
|
85
| 15,738,751
|
Beit
| 493
|
France
|
Female
| 46
| 4
| 0
| 2
| 1
| 0
| 1,907.66
| 0
| 0
| 1
|
SILVER
| 498
|
86
| 15,805,254
|
Ndukaku
| 652
|
Spain
|
Female
| 75
| 10
| 0
| 2
| 1
| 1
| 114,675.75
| 0
| 0
| 5
|
DIAMOND
| 651
|
87
| 15,762,418
|
Gant
| 750
|
Spain
|
Male
| 22
| 3
| 121,681.82
| 1
| 1
| 0
| 128,643.35
| 1
| 1
| 2
|
SILVER
| 852
|
88
| 15,625,759
|
Rowley
| 729
|
France
|
Male
| 30
| 9
| 0
| 2
| 1
| 0
| 151,869.35
| 0
| 0
| 1
|
DIAMOND
| 807
|
89
| 15,622,897
|
Sharpe
| 646
|
France
|
Female
| 46
| 4
| 0
| 3
| 1
| 0
| 93,251.42
| 1
| 1
| 3
|
PLATINUM
| 470
|
90
| 15,767,954
|
Osborne
| 635
|
Germany
|
Female
| 28
| 3
| 81,623.67
| 2
| 1
| 1
| 156,791.36
| 0
| 0
| 1
|
DIAMOND
| 659
|
91
| 15,757,535
|
Heap
| 647
|
Spain
|
Female
| 44
| 5
| 0
| 3
| 1
| 1
| 174,205.22
| 1
| 1
| 3
|
DIAMOND
| 752
|
92
| 15,731,511
|
Ritchie
| 808
|
France
|
Male
| 45
| 7
| 118,626.55
| 2
| 1
| 0
| 147,132.46
| 0
| 0
| 1
|
SILVER
| 559
|
93
| 15,809,248
|
Cole
| 524
|
France
|
Female
| 36
| 10
| 0
| 2
| 1
| 0
| 109,614.57
| 0
| 0
| 2
|
GOLD
| 375
|
94
| 15,640,635
|
Capon
| 769
|
France
|
Male
| 29
| 8
| 0
| 2
| 1
| 1
| 172,290.61
| 0
| 0
| 2
|
DIAMOND
| 411
|
95
| 15,676,966
|
Capon
| 730
|
Spain
|
Male
| 42
| 4
| 0
| 2
| 0
| 1
| 85,982.47
| 0
| 0
| 4
|
DIAMOND
| 722
|
96
| 15,699,461
|
Fiorentini
| 515
|
Spain
|
Male
| 35
| 10
| 176,273.95
| 1
| 0
| 1
| 121,277.78
| 0
| 0
| 1
|
GOLD
| 231
|
97
| 15,738,721
|
Graham
| 773
|
Spain
|
Male
| 41
| 9
| 102,827.44
| 1
| 0
| 1
| 64,595.25
| 0
| 0
| 5
|
DIAMOND
| 488
|
98
| 15,693,683
|
Yuille
| 814
|
Germany
|
Male
| 29
| 8
| 97,086.4
| 2
| 1
| 1
| 197,276.13
| 0
| 0
| 2
|
GOLD
| 965
|
99
| 15,604,348
|
Allard
| 710
|
Spain
|
Male
| 22
| 8
| 0
| 2
| 0
| 0
| 99,645.04
| 0
| 0
| 4
|
SILVER
| 289
|
100
| 15,633,059
|
Fanucci
| 413
|
France
|
Male
| 34
| 9
| 0
| 2
| 0
| 0
| 6,534.18
| 0
| 0
| 1
|
PLATINUM
| 449
|
End of preview.