Dataset Preview
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 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.
No dataset card yet
- Downloads last month
- 4