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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 1 new columns ({'id'}) and 1 missing columns ({'tip'}).
This happened while the csv dataset builder was generating data using
hf://datasets/alonsete7/ML/test.csv (at revision a354aff2c0d0b3ea234836a0d0ec5de585ae2718)
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
id: int64
total_bill: double
sex: string
smoker: string
day: string
time: string
size: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1019
to
{'total_bill': Value(dtype='float64', id=None), 'tip': Value(dtype='float64', id=None), 'sex': Value(dtype='string', id=None), 'smoker': Value(dtype='string', id=None), 'day': Value(dtype='string', id=None), 'time': Value(dtype='string', id=None), 'size': 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 1 new columns ({'id'}) and 1 missing columns ({'tip'}).
This happened while the csv dataset builder was generating data using
hf://datasets/alonsete7/ML/test.csv (at revision a354aff2c0d0b3ea234836a0d0ec5de585ae2718)
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.
total_bill float64 | tip float64 | sex string | smoker string | day string | time string | size int64 |
|---|---|---|---|---|---|---|
11.35 | 2.5 | Female | Yes | Fri | Dinner | 2 |
12.6 | 1 | Male | Yes | Sat | Dinner | 2 |
29.85 | 5.14 | Female | No | Sun | Dinner | 5 |
17.81 | 2.34 | Male | No | Sat | Dinner | 4 |
27.2 | 4 | Male | No | Thur | Lunch | 4 |
20.53 | 4 | Male | Yes | Thur | Lunch | 4 |
20.9 | 3.5 | Female | Yes | Sun | Dinner | 3 |
16.49 | 2 | Male | No | Sun | Dinner | 4 |
10.33 | 1.67 | Female | No | Sun | Dinner | 3 |
15.69 | 1.5 | Male | Yes | Sun | Dinner | 2 |
17.51 | 3 | Female | Yes | Sun | Dinner | 2 |
16.45 | 2.47 | Female | No | Sat | Dinner | 2 |
20.92 | 4.08 | Female | No | Sat | Dinner | 2 |
24.55 | 2 | Male | No | Sun | Dinner | 4 |
18.29 | 3 | Male | No | Sun | Dinner | 2 |
17.46 | 2.54 | Male | No | Sun | Dinner | 2 |
16.47 | 3.23 | Female | Yes | Thur | Lunch | 3 |
19.82 | 3.18 | Male | No | Sat | Dinner | 2 |
16.31 | 2 | Male | No | Sat | Dinner | 3 |
21.58 | 3.92 | Male | No | Sun | Dinner | 2 |
15.69 | 3 | Male | Yes | Sat | Dinner | 3 |
20.69 | 5 | Male | No | Sun | Dinner | 5 |
19.08 | 1.5 | Male | No | Thur | Lunch | 2 |
29.93 | 5.07 | Male | No | Sun | Dinner | 4 |
9.68 | 1.32 | Male | No | Sun | Dinner | 2 |
19.44 | 3 | Male | Yes | Thur | Lunch | 2 |
26.88 | 3.12 | Male | No | Sun | Dinner | 4 |
21.7 | 4.3 | Male | No | Sat | Dinner | 2 |
17.29 | 2.71 | Male | No | Thur | Lunch | 2 |
28.17 | 6.5 | Female | Yes | Sat | Dinner | 3 |
22.67 | 2 | Male | Yes | Sat | Dinner | 2 |
17.07 | 3 | Female | No | Sat | Dinner | 3 |
19.65 | 3 | Female | No | Sat | Dinner | 2 |
14.73 | 2.2 | Female | No | Sat | Dinner | 2 |
18.04 | 3 | Male | No | Sun | Dinner | 2 |
32.68 | 5 | Male | Yes | Thur | Lunch | 2 |
11.02 | 1.98 | Male | Yes | Sat | Dinner | 2 |
22.76 | 3 | Male | No | Thur | Lunch | 2 |
10.07 | 1.25 | Male | No | Sat | Dinner | 2 |
10.07 | 1.83 | Female | No | Thur | Lunch | 1 |
15.36 | 1.64 | Male | Yes | Sat | Dinner | 2 |
14.31 | 4 | Female | Yes | Sat | Dinner | 2 |
23.17 | 6.5 | Male | Yes | Sun | Dinner | 4 |
7.74 | 1.44 | Male | Yes | Sat | Dinner | 2 |
3.07 | 1 | Female | Yes | Sat | Dinner | 1 |
18.24 | 3.76 | Male | No | Sat | Dinner | 2 |
31.71 | 4.5 | Male | No | Sun | Dinner | 4 |
39.42 | 7.58 | Male | No | Sat | Dinner | 4 |
8.51 | 1.25 | Female | No | Thur | Lunch | 2 |
10.51 | 1.25 | Male | No | Sat | Dinner | 2 |
19.49 | 3.51 | Male | No | Sun | Dinner | 2 |
30.14 | 3.09 | Female | Yes | Sat | Dinner | 4 |
14.78 | 3.23 | Male | No | Sun | Dinner | 2 |
32.83 | 1.17 | Male | Yes | Sat | Dinner | 2 |
34.81 | 5.2 | Female | No | Sun | Dinner | 4 |
12.02 | 1.97 | Male | No | Sat | Dinner | 2 |
30.06 | 2 | Male | Yes | Sat | Dinner | 3 |
12.48 | 2.52 | Female | No | Thur | Lunch | 2 |
18.15 | 3.5 | Female | Yes | Sun | Dinner | 3 |
20.08 | 3.15 | Male | No | Sat | Dinner | 3 |
14.15 | 2 | Female | No | Thur | Lunch | 2 |
15.81 | 3.16 | Male | Yes | Sat | Dinner | 2 |
17.26 | 2.74 | Male | No | Sun | Dinner | 3 |
38.73 | 3 | Male | Yes | Sat | Dinner | 4 |
29.03 | 5.92 | Male | No | Sat | Dinner | 3 |
15.04 | 1.96 | Male | No | Sun | Dinner | 2 |
18.26 | 3.25 | Female | No | Thur | Lunch | 2 |
27.05 | 5 | Female | No | Thur | Lunch | 6 |
21.01 | 3 | Male | Yes | Fri | Dinner | 2 |
12.16 | 2.2 | Male | Yes | Fri | Lunch | 2 |
18.71 | 4 | Male | Yes | Thur | Lunch | 3 |
25.28 | 5 | Female | Yes | Sat | Dinner | 2 |
20.69 | 2.45 | Female | No | Sat | Dinner | 4 |
8.58 | 1.92 | Male | Yes | Fri | Lunch | 1 |
15.42 | 1.57 | Male | No | Sun | Dinner | 2 |
21.5 | 3.5 | Male | No | Sun | Dinner | 4 |
22.82 | 2.18 | Male | No | Thur | Lunch | 3 |
34.65 | 3.68 | Male | Yes | Sun | Dinner | 4 |
15.48 | 2.02 | Male | Yes | Thur | Lunch | 2 |
11.59 | 1.5 | Male | Yes | Sat | Dinner | 2 |
24.27 | 2.03 | Male | Yes | Sat | Dinner | 2 |
13.16 | 2.75 | Female | No | Thur | Lunch | 2 |
18.78 | 3 | Female | No | Thur | Dinner | 2 |
7.56 | 1.44 | Male | No | Thur | Lunch | 2 |
25.29 | 4.71 | Male | No | Sun | Dinner | 4 |
26.41 | 1.5 | Female | No | Sat | Dinner | 2 |
38.07 | 4 | Male | No | Sun | Dinner | 3 |
10.29 | 2.6 | Female | No | Sun | Dinner | 2 |
28.15 | 3 | Male | Yes | Sat | Dinner | 5 |
24.08 | 2.92 | Female | No | Thur | Lunch | 4 |
45.35 | 3.5 | Male | Yes | Sun | Dinner | 3 |
17.31 | 3.5 | Female | No | Sun | Dinner | 2 |
13.03 | 2 | Male | No | Thur | Lunch | 2 |
40.55 | 3 | Male | Yes | Sun | Dinner | 2 |
27.28 | 4 | Male | Yes | Fri | Dinner | 2 |
9.55 | 1.45 | Male | No | Sat | Dinner | 2 |
16.97 | 3.5 | Female | No | Sun | Dinner | 3 |
31.27 | 5 | Male | No | Sat | Dinner | 3 |
18.29 | 3.76 | Male | Yes | Sat | Dinner | 4 |
10.27 | 1.71 | Male | No | Sun | Dinner | 2 |
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