Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Float value 25.700000 was truncated converting to int64
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2086, in cast_array_to_feature
return array_cast(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1949, in array_cast
return array.cast(pa_type)
^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 1135, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.12/site-packages/pyarrow/compute.py", line 412, in cast
return call_function("cast", [arr], options, memory_pool)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Float value 25.700000 was truncated converting to int64
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1455, 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 1054, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
age
int64 | bmi
int64 | children
int64 | sex
string | smoker
string | region
string | prediction
float64 |
|---|---|---|---|---|---|---|
40
| 37
| 2
|
female
|
no
|
southwest
| 7,359.319926
|
40
| 37
| 2
|
female
|
yes
|
southwest
| 43,032.575997
|
40
| 37
| 2
|
male
|
yes
|
southwest
| 42,426.646516
|
34
| 3
| 1
|
male
|
no
|
northwest
| 7,824.32104
|
34
| 3
| 1
|
male
|
no
|
northwest
| 7,824.32104
|
34
| 3
| 1
|
male
|
no
|
northwest
| 7,824.32104
|
34
| 3
| 1
|
male
|
no
|
northwest
| 7,824.32104
|
34
| 3
| 1
|
male
|
yes
|
northwest
| 16,773.531475
|
34
| 3
| 1
|
male
|
yes
|
northwest
| 16,773.531475
|
34
| 3
| 1
|
male
|
yes
|
northwest
| 16,773.531475
|
32
| 12
| 2
|
female
|
yes
|
northeast
| 16,565.936506
|
46
| 10
| 3
|
male
|
no
|
northwest
| 9,519.047247
|
46
| 10
| 0
|
male
|
no
|
northwest
| 8,589.269509
|
46
| 78
| 0
|
male
|
no
|
northwest
| 10,866.626696
|
50
| 20
| 2
|
female
|
yes
|
southeast
| 23,850.976824
|
33
| 117
| 1
|
male
|
no
|
northeast
| 8,168.662122
|
33
| 51
| 1
|
male
|
no
|
northeast
| 8,168.662122
|
33
| 161
| 1
|
male
|
no
|
northeast
| 8,168.662122
|
33
| 161
| 3
|
male
|
no
|
northeast
| 9,770.511679
|
33
| 161
| 3
|
male
|
no
|
southeast
| 7,026.814738
|
33
| 161
| 3
|
male
|
no
|
southwest
| 7,104.121182
|
33
| 161
| 3
|
male
|
no
|
northeast
| 9,770.511679
|
33
| 161
| 3
|
male
|
no
|
northwest
| 7,471.283578
|
33
| 161
| 3
|
male
|
yes
|
northwest
| 47,479.38227
|
33
| 161
| 3
|
male
|
no
|
northwest
| 7,471.283578
|
33
| 161
| 3
|
male
|
yes
|
northwest
| 47,479.38227
|
33
| 161
| 3
|
male
|
no
|
northwest
| 7,471.283578
|
32
| 34
| 2
|
male
|
no
|
northeast
| 7,332.486299
|
23
| 27
| 2
|
female
|
no
|
southeast
| 7,188.916885
|
23
| 27
| 2
|
female
|
yes
|
southeast
| 19,306.546678
|
41
| 86
| 2
|
male
|
no
|
southeast
| 8,845.522532
|
41
| 86
| 2
|
male
|
no
|
southeast
| 8,845.522532
|
41
| 86
| 2
|
male
|
yes
|
southeast
| 43,995.607406
|
61
| 15
| 2
|
male
|
no
|
northeast
| 16,212.280952
|
61
| 15
| 2
|
male
|
yes
|
northeast
| 25,869.645046
|
75
| 59
| 2
|
male
|
no
|
northeast
| 19,092.241658
|
75
| 59
| 2
|
male
|
yes
|
northeast
| 48,292.276555
|
96
| 59
| 2
|
male
|
yes
|
northeast
| 48,292.276555
|
96
| 59
| 2
|
male
|
no
|
northeast
| 19,092.241658
|
45
| 33
| 1
|
male
|
no
|
southeast
| 8,395.185167
|
45
| 33
| 1
|
male
|
yes
|
southeast
| 42,322.717899
|
19
| 79
| 1
|
male
|
yes
|
southeast
| 39,256.806569
|
35
| 83
| 1
|
male
|
yes
|
southeast
| 44,146.601789
|
35
| 83
| 1
|
male
|
no
|
southeast
| 7,415.229663
|
30
| 70
| 5
|
male
|
no
|
southwest
| 7,180.808139
|
30
| 70
| 5
|
male
|
yes
|
southwest
| 43,751.113128
|
36
| 0
| 1
|
male
|
no
|
southeast
| 7,426.367354
|
23
| 70
| 0
|
female
|
yes
|
southeast
| 39,343.784165
|
23
| 70
| 0
|
female
|
no
|
southeast
| 5,940.411886
|
34
| 22
| 2
|
female
|
no
|
southwest
| 8,264.749571
|
34
| 22
| 2
|
female
|
yes
|
southwest
| 17,656.431575
|
42
| 57
| 2
|
female
|
yes
|
southeast
| 44,718.186471
|
42
| 57
| 2
|
male
|
yes
|
southeast
| 44,153.095878
|
42
| 57
| 2
|
male
|
no
|
southeast
| 8,886.687401
|
64
| 57
| 2
|
male
|
no
|
southeast
| 19,126.004604
|
88
| 57
| 2
|
male
|
no
|
southeast
| 19,126.004604
|
88
| 57
| 2
|
male
|
no
|
southeast
| 19,126.004604
|
86
| 38
| 3
|
male
|
no
|
southeast
| 19,613.590103
|
86
| 38
| 3
|
male
|
no
|
southeast
| 19,613.590103
|
86
| 38
| 3
|
male
|
yes
|
southeast
| 49,234.122659
|
33
| 38
| 3
|
male
|
no
|
southeast
| 6,623.582118
|
90
| 38
| 3
|
male
|
no
|
southeast
| 19,613.590103
|
79
| 38
| 3
|
male
|
no
|
southeast
| 19,613.590103
|
57
| 39
| 2
|
male
|
no
|
southeast
| 13,673.111576
|
57
| 39
| 2
|
male
|
yes
|
southeast
| 47,735.770042
|
42
| 35
| 2
|
female
|
yes
|
southwest
| 40,312.385665
|
36
| 54
| 2
|
female
|
yes
|
northeast
| 45,374.823615
|
30
| 80
| 2
|
female
|
no
|
southeast
| 6,754.224766
|
30
| 80
| 2
|
female
|
yes
|
southeast
| 43,789.698711
|
63
| 24
| 3
|
male
|
no
|
southeast
| 16,865.44068
|
63
| 24
| 3
|
male
|
no
|
southeast
| 16,865.44068
|
63
| 24
| 3
|
male
|
yes
|
southeast
| 25,800.210511
|
17
| 150
| 0
|
male
|
no
|
northwest
| 2,855.636313
|
45
| 25.7
| 3
|
female
|
no
|
southwest
| 9,975.453542
|
19
| 30.59
| 0
|
male
|
no
|
northwest
| 3,093.041322
|
39
| 32.8
| 0
|
female
|
no
|
southwest
| 6,183.392482
|
18
| 30.14
| 0
|
male
|
no
|
southeast
| 4,472.059219
|
45
| 38.285
| 0
|
female
|
no
|
northeast
| 11,888.941538
|
40
| 24.97
| 2
|
male
|
no
|
southeast
| 8,445.92038
|
47
| 19.57
| 1
|
male
|
no
|
northwest
| 9,804.189541
|
21
| 36.85
| 0
|
male
|
no
|
southeast
| 2,893.756152
|
31
| 25.935
| 1
|
male
|
no
|
northwest
| 5,055.123119
|
43
| 38.06
| 2
|
male
|
yes
|
southeast
| 44,383.556197
|
59
| 37.1
| 1
|
male
|
no
|
southwest
| 20,813.086292
|
40
| 41.42
| 1
|
female
|
no
|
northwest
| 7,111.140683
|
58
| 27.17
| 0
|
female
|
no
|
northwest
| 13,073.332296
|
46
| 48.07
| 2
|
female
|
no
|
northeast
| 11,040.859798
|
29
| 29.64
| 1
|
male
|
no
|
northeast
| 5,085.090895
|
42
| 37.18
| 2
|
male
|
no
|
southeast
| 8,691.872044
|
27
| 32.585
| 3
|
male
|
no
|
northeast
| 7,964.43015
|
61
| 21.09
| 0
|
female
|
no
|
northwest
| 15,719.921192
|
60
| 18.335
| 0
|
female
|
no
|
northeast
| 15,143.63757
|
26
| 31.065
| 0
|
male
|
no
|
northwest
| 3,597.931183
|
22
| 52.58
| 1
|
male
|
yes
|
southeast
| 39,256.806569
|
23
| 41.91
| 0
|
male
|
no
|
southeast
| 4,790.834146
|
22
| 39.805
| 0
|
female
|
no
|
northeast
| 3,746.225954
|
18
| 29.165
| 0
|
female
|
no
|
northeast
| 3,559.51485
|
62
| 32.11
| 0
|
male
|
no
|
northeast
| 13,506.264752
|
21
| 26.4
| 1
|
female
|
no
|
southwest
| 2,610.618663
|
62
| 21.4
| 0
|
male
|
no
|
southwest
| 15,314.334706
|
End of preview.
No dataset card yet
- Downloads last month
- 21