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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 7 new columns ({'TypeofContact', 'DurationOfPitch', 'NumberOfFollowups', 'Unnamed: 0', 'PitchSatisfactionScore', 'ProdTaken', 'CustomerID'})
This happened while the csv dataset builder was generating data using
hf://datasets/ani-maddy98/tourism-package-prediction/tourism.csv (at revision 759833b4e057a7dd740df72f702e6feb1ef7e9f9), [/tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtest.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtrain.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/tourism.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytest.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytrain.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytrain.csv)]
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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 674, 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 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Unnamed: 0: int64
CustomerID: int64
ProdTaken: int64
Age: double
TypeofContact: string
CityTier: int64
DurationOfPitch: double
Occupation: string
Gender: string
NumberOfPersonVisiting: int64
NumberOfFollowups: double
ProductPitched: string
PreferredPropertyStar: double
MaritalStatus: string
NumberOfTrips: double
Passport: int64
PitchSatisfactionScore: int64
OwnCar: int64
NumberOfChildrenVisiting: double
Designation: string
MonthlyIncome: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2881
to
{'Age': Value('float64'), 'MonthlyIncome': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfTrips': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), 'CityTier': Value('int64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('string'), 'Passport': Value('int64'), 'OwnCar': Value('int64'), 'Designation': Value('string')}
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 1347, 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 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, 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 1889, 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 7 new columns ({'TypeofContact', 'DurationOfPitch', 'NumberOfFollowups', 'Unnamed: 0', 'PitchSatisfactionScore', 'ProdTaken', 'CustomerID'})
This happened while the csv dataset builder was generating data using
hf://datasets/ani-maddy98/tourism-package-prediction/tourism.csv (at revision 759833b4e057a7dd740df72f702e6feb1ef7e9f9), [/tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtest.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtrain.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/tourism.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytest.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/67111491022736-config-parquet-and-info-ani-maddy98-tourism-packa-c95724b9/hub/datasets--ani-maddy98--tourism-package-prediction/snapshots/759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytrain.csv (origin=hf://datasets/ani-maddy98/tourism-package-prediction@759833b4e057a7dd740df72f702e6feb1ef7e9f9/ytrain.csv)]
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.
Age float64 | MonthlyIncome float64 | NumberOfPersonVisiting int64 | NumberOfTrips float64 | NumberOfChildrenVisiting float64 | CityTier int64 | Occupation string | Gender string | ProductPitched string | PreferredPropertyStar float64 | MaritalStatus string | Passport int64 | OwnCar int64 | Designation string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
44 | 22,879 | 3 | 2 | 0 | 1 | Salaried | Female | Standard | 3 | Married | 1 | 1 | Senior Manager |
35 | 27,306 | 3 | 3 | 2 | 3 | Small Business | Male | Standard | 3 | Married | 0 | 1 | Senior Manager |
47 | 29,131 | 4 | 3 | 2 | 3 | Small Business | Female | Standard | 5 | Married | 0 | 1 | Senior Manager |
32 | 21,220 | 3 | 2 | 0 | 1 | Salaried | Male | Deluxe | 4 | Married | 0 | 1 | Manager |
59 | 21,157 | 3 | 6 | 2 | 1 | Large Business | Male | Basic | 3 | Single | 0 | 1 | Executive |
44 | 33,213 | 2 | 1 | 1 | 3 | Small Business | Male | King | 4 | Divorced | 0 | 1 | VP |
32 | 17,837 | 2 | 2 | 0 | 1 | Salaried | Female | Basic | 4 | Single | 0 | 1 | Executive |
27 | 23,974 | 3 | 3 | 2 | 3 | Salaried | Male | Deluxe | 3 | Married | 0 | 0 | Manager |
38 | 20,249 | 2 | 4 | 1 | 3 | Salaried | Male | Deluxe | 3 | Divorced | 0 | 1 | Manager |
32 | 23,499 | 3 | 2 | 1 | 1 | Large Business | Male | Basic | 3 | Married | 1 | 1 | Executive |
40 | 18,319 | 3 | 2 | 1 | 1 | Large Business | Male | Deluxe | 3 | Married | 0 | 1 | Manager |
38 | 22,963 | 3 | 3 | 1 | 1 | Small Business | Male | Deluxe | 3 | Married | 0 | 0 | Manager |
35 | 23,789 | 3 | 2 | 0 | 3 | Small Business | Female | Standard | 3 | Single | 0 | 1 | Senior Manager |
35 | 17,074 | 3 | 2 | 1 | 1 | Salaried | Female | Basic | 5 | Married | 1 | 1 | Executive |
34 | 22,086 | 3 | 2 | 1 | 1 | Small Business | Male | Basic | 3 | Married | 0 | 0 | Executive |
33 | 21,515 | 3 | 3 | 1 | 1 | Salaried | Female | Basic | 4 | Single | 0 | 1 | Executive |
51 | 17,075 | 3 | 4 | 0 | 1 | Salaried | Male | Basic | 3 | Divorced | 0 | 1 | Executive |
29 | 16,091 | 2 | 2 | 1 | 3 | Large Business | Male | Basic | 5 | Single | 0 | 1 | Executive |
34 | 20,304 | 3 | 1 | 2 | 3 | Small Business | Male | Deluxe | 3 | Single | 1 | 1 | Manager |
38 | 32,342 | 2 | 6 | 1 | 1 | Small Business | Male | Standard | 3 | Single | 0 | 0 | Senior Manager |
46 | 24,396 | 3 | 1 | 0 | 1 | Small Business | Male | Standard | 5 | Married | 0 | 0 | Senior Manager |
54 | 25,725 | 2 | 3 | 0 | 2 | Small Business | Male | Standard | 4 | Divorced | 0 | 1 | Senior Manager |
56 | 26,103 | 2 | 1 | 0 | 1 | Small Business | Male | Super Deluxe | 3 | Married | 0 | 0 | AVP |
30 | 17,285 | 2 | 19 | 1 | 1 | Large Business | Male | Basic | 3 | Single | 1 | 1 | Executive |
26 | 17,867 | 3 | 1 | 2 | 1 | Small Business | Male | Basic | 5 | Single | 0 | 1 | Executive |
33 | 26,691 | 2 | 1 | 0 | 1 | Small Business | Male | Standard | 3 | Married | 0 | 1 | Senior Manager |
24 | 17,127 | 3 | 2 | 1 | 1 | Salaried | Male | Basic | 4 | Married | 0 | 1 | Executive |
30 | 25,062 | 4 | 2 | 3 | 1 | Salaried | Male | Deluxe | 3 | Married | 0 | 1 | Manager |
33 | 20,147 | 3 | 1 | 0 | 3 | Small Business | Female | Deluxe | 4 | Single | 0 | 0 | Manager |
53 | 22,525 | 2 | 3 | 0 | 3 | Small Business | Female | Standard | 4 | Married | 0 | 1 | Senior Manager |
29 | 23,576 | 3 | 2 | 2 | 3 | Salaried | Male | Deluxe | 5 | Single | 0 | 1 | Manager |
39 | 20,151 | 2 | 2 | 0 | 1 | Small Business | Male | Deluxe | 5 | Married | 0 | 1 | Manager |
46 | 23,483 | 4 | 2 | 3 | 3 | Salaried | Male | Deluxe | 4 | Married | 0 | 1 | Manager |
35 | 30,672 | 3 | 2 | 1 | 1 | Salaried | Female | Standard | 4 | Single | 0 | 1 | Senior Manager |
35 | 20,909 | 4 | 8 | 1 | 3 | Small Business | Female | Basic | 3 | Married | 0 | 0 | Executive |
33 | 21,010 | 4 | 8 | 3 | 1 | Salaried | Female | Basic | 4 | Married | 0 | 0 | Executive |
29 | 21,623 | 2 | 2 | 0 | 1 | Salaried | Female | Basic | 3 | Single | 0 | 1 | Executive |
41 | 21,230 | 2 | 1 | 1 | 3 | Salaried | Male | Deluxe | 3 | Single | 0 | 0 | Manager |
43 | 22,950 | 3 | 6 | 1 | 1 | Small Business | Male | Deluxe | 3 | Single | 0 | 1 | Manager |
35 | 21,029 | 3 | 2 | 2 | 3 | Small Business | Female | Basic | 3 | Married | 0 | 0 | Executive |
41 | 28,591 | 3 | 4 | 0 | 3 | Salaried | Female | Standard | 3 | Single | 1 | 0 | Senior Manager |
33 | 21,949 | 2 | 1 | 0 | 1 | Salaried | Female | Deluxe | 3 | Single | 0 | 0 | Manager |
40 | 28,499 | 2 | 1 | 0 | 1 | Small Business | Female | Standard | 3 | Single | 0 | 0 | Senior Manager |
26 | 18,102 | 3 | 1 | 1 | 1 | Large Business | Male | Basic | 5 | Single | 0 | 0 | Executive |
41 | 18,072 | 2 | 3 | 0 | 1 | Salaried | Male | Deluxe | 5 | Married | 0 | 0 | Manager |
37 | 27,185 | 2 | 2 | 1 | 1 | Salaried | Male | Standard | 3 | Married | 1 | 0 | Senior Manager |
31 | 17,329 | 2 | 4 | 1 | 3 | Salaried | Male | Basic | 3 | Married | 0 | 1 | Executive |
45 | 21,040 | 3 | 8 | 2 | 3 | Salaried | Male | Deluxe | 4 | Single | 0 | 0 | Manager |
33 | 18,348 | 3 | 2 | 2 | 1 | Salaried | Male | Basic | 5 | Single | 1 | 1 | Executive |
33 | 21,048 | 4 | 3 | 1 | 1 | Small Business | Female | Basic | 4 | Divorced | 0 | 0 | Executive |
33 | 21,388 | 3 | 3 | 2 | 1 | Salaried | Male | Deluxe | 3 | Single | 1 | 0 | Manager |
30 | 21,577 | 2 | 1 | 0 | 3 | Large Business | Female | Deluxe | 3 | Single | 0 | 1 | Manager |
42 | 17,759 | 2 | 7 | 1 | 1 | Small Business | Male | Basic | 3 | Married | 1 | 1 | Executive |
46 | 32,861 | 2 | 7 | 0 | 1 | Salaried | Male | Super Deluxe | 3 | Married | 0 | 1 | AVP |
51 | 21,058 | 4 | 6 | 3 | 1 | Salaried | Male | Basic | 3 | Married | 0 | 1 | Executive |
30 | 21,091 | 2 | 3 | 0 | 1 | Salaried | Female | Deluxe | 3 | Single | 0 | 1 | Manager |
37 | 22,366 | 3 | 6 | 1 | 1 | Salaried | Male | Basic | 3 | Divorced | 0 | 0 | Executive |
28 | 17,706 | 2 | 2 | 1 | 2 | Salaried | Male | Basic | 3 | Married | 0 | 0 | Executive |
42 | 28,348 | 2 | 1 | 0 | 1 | Small Business | Male | Standard | 5 | Married | 0 | 1 | Senior Manager |
44 | 20,933 | 2 | 1 | 0 | 1 | Small Business | Male | Deluxe | 4 | Single | 0 | 1 | Manager |
39 | 21,118 | 3 | 3 | 1 | 1 | Small Business | Female | Basic | 4 | Single | 0 | 1 | Executive |
42 | 21,545 | 2 | 4 | 0 | 1 | Salaried | Female | Deluxe | 5 | Single | 1 | 0 | Manager |
39 | 25,880 | 2 | 2 | 1 | 1 | Small Business | Female | Standard | 5 | Single | 1 | 1 | Senior Manager |
28 | 21,674 | 2 | 1 | 0 | 1 | Salaried | Female | Deluxe | 3 | Divorced | 0 | 1 | Manager |
43 | 32,159 | 3 | 7 | 1 | 1 | Salaried | Male | Super Deluxe | 5 | Married | 0 | 1 | AVP |
45 | 26,656 | 4 | 3 | 2 | 1 | Small Business | Female | Standard | 3 | Divorced | 0 | 0 | Senior Manager |
53 | 24,255 | 4 | 5 | 2 | 1 | Large Business | Male | Deluxe | 5 | Married | 1 | 1 | Manager |
42 | 20,916 | 4 | 4 | 1 | 1 | Salaried | Male | Basic | 5 | Married | 0 | 0 | Executive |
36 | 20,237 | 3 | 7 | 0 | 1 | Small Business | Male | Deluxe | 3 | Divorced | 0 | 1 | Manager |
22 | 20,748 | 4 | 3 | 3 | 1 | Large Business | Female | Basic | 4 | Single | 1 | 0 | Executive |
37 | 24,592 | 4 | 2 | 3 | 1 | Salaried | Male | Deluxe | 4 | Single | 0 | 0 | Manager |
30 | 24,443 | 3 | 7 | 2 | 3 | Large Business | Female | Deluxe | 4 | Single | 0 | 0 | Manager |
36 | 28,562 | 4 | 4 | 3 | 1 | Small Business | Male | Standard | 5 | Married | 1 | 1 | Senior Manager |
40 | 34,033 | 2 | 2 | 1 | 1 | Small Business | Female | King | 3 | Divorced | 0 | 0 | VP |
51 | 25,650 | 2 | 3 | 1 | 1 | Salaried | Male | Standard | 3 | Single | 0 | 0 | Senior Manager |
39 | 21,536 | 3 | 6 | 2 | 3 | Salaried | Male | Basic | 5 | Single | 0 | 0 | Executive |
43 | 29,336 | 2 | 2 | 1 | 1 | Salaried | Male | Super Deluxe | 4 | Married | 0 | 0 | AVP |
35 | 16,951 | 3 | 2 | 0 | 1 | Salaried | Male | Basic | 3 | Married | 0 | 0 | Executive |
40 | 29,616 | 4 | 2 | 2 | 1 | Large Business | Female | Standard | 3 | Single | 0 | 1 | Senior Manager |
27 | 23,362 | 3 | 3 | 1 | 3 | Small Business | Male | Deluxe | 3 | Single | 0 | 0 | Manager |
26 | 17,042 | 2 | 7 | 0 | 1 | Salaried | Male | Basic | 5 | Divorced | 1 | 1 | Executive |
43 | 31,959 | 3 | 2 | 0 | 3 | Salaried | Male | Super Deluxe | 3 | Divorced | 1 | 0 | AVP |
32 | 25,511 | 4 | 3 | 3 | 1 | Small Business | Male | Deluxe | 5 | Divorced | 1 | 0 | Manager |
35 | 30,309 | 3 | 4 | 1 | 1 | Small Business | Female | Standard | 5 | Single | 0 | 0 | Senior Manager |
34 | 21,300 | 3 | 8 | 2 | 1 | Small Business | Female | Basic | 4 | Married | 0 | 0 | Executive |
31 | 16,261 | 2 | 2 | 1 | 1 | Salaried | Female | Basic | 4 | Single | 0 | 0 | Executive |
35 | 24,392 | 4 | 3 | 1 | 3 | Salaried | Female | Deluxe | 3 | Married | 0 | 0 | Manager |
42 | 24,829 | 3 | 2 | 2 | 3 | Salaried | Male | Super Deluxe | 3 | Married | 0 | 1 | AVP |
34 | 20,121 | 2 | 4 | 1 | 1 | Salaried | Female | Deluxe | 5 | Married | 0 | 1 | Manager |
34 | 21,385 | 3 | 2 | 1 | 1 | Salaried | Female | Basic | 5 | Divorced | 0 | 1 | Executive |
34 | 26,994 | 2 | 1 | 0 | 1 | Salaried | Female | Standard | 4 | Single | 0 | 1 | Senior Manager |
39 | 24,939 | 3 | 5 | 2 | 1 | Large Business | Male | Deluxe | 3 | Divorced | 0 | 0 | Manager |
29 | 22,119 | 3 | 3 | 1 | 1 | Large Business | Male | Basic | 3 | Single | 1 | 0 | Executive |
35 | 20,762 | 2 | 3 | 1 | 1 | Small Business | Male | Deluxe | 3 | Married | 0 | 0 | Manager |
26 | 20,828 | 2 | 2 | 1 | 3 | Small Business | Male | Deluxe | 3 | Single | 1 | 1 | Manager |
37 | 21,513 | 3 | 7 | 1 | 1 | Salaried | Female | Basic | 3 | Married | 0 | 1 | Executive |
35 | 24,024 | 4 | 6 | 2 | 1 | Salaried | Male | Deluxe | 5 | Married | 0 | 0 | Manager |
40 | 30,847 | 3 | 2 | 1 | 1 | Salaried | Male | Super Deluxe | 3 | Married | 0 | 1 | AVP |
33 | 17,851 | 2 | 2 | 0 | 3 | Small Business | Female | Basic | 3 | Single | 1 | 1 | Executive |
38 | 17,899 | 3 | 1 | 0 | 3 | Small Business | Male | Basic | 4 | Divorced | 0 | 0 | Executive |
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