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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 8 new columns ({'Passport', 'ProdTaken', 'OwnCar', 'NumberOfTrips', 'NumberOfChildrenVisiting', 'Unnamed: 0', 'ProductPitched', 'MonthlyIncome'})
This happened while the csv dataset builder was generating data using
hf://datasets/vyasmax9/tourism-app/tourism.csv (at revision 76180bed5a4e7fb49e1e178cbff07d6b2420d032), [/tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtest.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtrain.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/tourism.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/ytest.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/ytrain.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/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 1800, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, 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'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'DurationOfPitch': Value('float64'), 'PitchSatisfactionScore': Value('int64'), 'CustomerID': Value('int64'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'Gender': Value('string'), 'CityTier': Value('int64'), 'MaritalStatus': Value('string'), 'PreferredPropertyStar': Value('float64'), '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 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, 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 1802, 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 8 new columns ({'Passport', 'ProdTaken', 'OwnCar', 'NumberOfTrips', 'NumberOfChildrenVisiting', 'Unnamed: 0', 'ProductPitched', 'MonthlyIncome'})
This happened while the csv dataset builder was generating data using
hf://datasets/vyasmax9/tourism-app/tourism.csv (at revision 76180bed5a4e7fb49e1e178cbff07d6b2420d032), [/tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtest.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtrain.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/tourism.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/ytest.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/45712913824732-config-parquet-and-info-vyasmax9-tourism-app-d3d06db9/hub/datasets--vyasmax9--tourism-app/snapshots/76180bed5a4e7fb49e1e178cbff07d6b2420d032/ytrain.csv (origin=hf://datasets/vyasmax9/tourism-app@76180bed5a4e7fb49e1e178cbff07d6b2420d032/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 | NumberOfPersonVisiting int64 | NumberOfFollowups float64 | DurationOfPitch float64 | PitchSatisfactionScore int64 | CustomerID int64 | TypeofContact string | Occupation string | Gender string | CityTier int64 | MaritalStatus string | PreferredPropertyStar float64 | Designation string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
44 | 3 | 1 | 8 | 4 | 201,214 | Self Enquiry | Salaried | Female | 1 | Married | 3 | Senior Manager |
35 | 3 | 4 | 20 | 1 | 203,829 | Self Enquiry | Small Business | Male | 3 | Married | 3 | Senior Manager |
47 | 4 | 4 | 7 | 2 | 202,622 | Self Enquiry | Small Business | Female | 3 | Married | 5 | Senior Manager |
32 | 3 | 3 | 6 | 3 | 201,543 | Self Enquiry | Salaried | Male | 1 | Married | 4 | Manager |
59 | 3 | 4 | 9 | 2 | 203,144 | Self Enquiry | Large Business | Male | 1 | Single | 3 | Executive |
44 | 2 | 3 | 11 | 5 | 200,907 | Self Enquiry | Small Business | Male | 3 | Divorced | 4 | VP |
32 | 2 | 4 | 35 | 3 | 201,426 | Self Enquiry | Salaried | Female | 1 | Single | 4 | Executive |
27 | 3 | 4 | 7 | 5 | 204,269 | Self Enquiry | Salaried | Male | 3 | Married | 3 | Manager |
38 | 2 | 4 | 8 | 5 | 200,261 | Company Invited | Salaried | Male | 3 | Divorced | 3 | Manager |
32 | 3 | 4 | 12 | 4 | 204,223 | Self Enquiry | Large Business | Male | 1 | Married | 3 | Executive |
40 | 3 | 3 | 30 | 3 | 200,243 | Self Enquiry | Large Business | Male | 1 | Married | 3 | Manager |
38 | 3 | 4 | 20 | 1 | 203,533 | Self Enquiry | Small Business | Male | 1 | Married | 3 | Manager |
35 | 3 | 3 | 6 | 5 | 200,228 | Company Invited | Small Business | Fe Male | 3 | Unmarried | 3 | Senior Manager |
35 | 3 | 3 | 8 | 1 | 201,110 | Self Enquiry | Salaried | Female | 1 | Married | 5 | Executive |
34 | 3 | 6 | 17 | 5 | 204,350 | Self Enquiry | Small Business | Male | 1 | Married | 3 | Executive |
33 | 3 | 5 | 36 | 3 | 203,870 | Self Enquiry | Salaried | Female | 1 | Unmarried | 4 | Executive |
51 | 3 | 3 | 15 | 3 | 200,087 | Self Enquiry | Salaried | Male | 1 | Divorced | 3 | Executive |
29 | 2 | 1 | 30 | 3 | 201,365 | Company Invited | Large Business | Male | 3 | Single | 5 | Executive |
34 | 3 | 2 | 25 | 2 | 200,378 | Company Invited | Small Business | Male | 3 | Single | 3 | Manager |
38 | 2 | 4 | 14 | 2 | 202,522 | Self Enquiry | Small Business | Male | 1 | Single | 3 | Senior Manager |
46 | 3 | 3 | 6 | 2 | 200,209 | Self Enquiry | Small Business | Male | 1 | Married | 5 | Senior Manager |
54 | 2 | 3 | 25 | 3 | 200,510 | Self Enquiry | Small Business | Male | 2 | Divorced | 4 | Senior Manager |
56 | 2 | 3 | 15 | 4 | 202,022 | Self Enquiry | Small Business | Male | 1 | Married | 3 | AVP |
30 | 2 | 3 | 10 | 4 | 200,385 | Company Invited | Large Business | Male | 1 | Single | 3 | Executive |
26 | 3 | 3 | 6 | 5 | 201,386 | Self Enquiry | Small Business | Male | 1 | Single | 5 | Executive |
33 | 2 | 3 | 13 | 4 | 202,060 | Self Enquiry | Small Business | Male | 1 | Married | 3 | Senior Manager |
24 | 3 | 4 | 23 | 3 | 201,946 | Self Enquiry | Salaried | Male | 1 | Married | 4 | Executive |
30 | 4 | 6 | 36 | 5 | 203,768 | Self Enquiry | Salaried | Male | 1 | Married | 3 | Manager |
33 | 3 | 3 | 8 | 1 | 201,253 | Company Invited | Small Business | Female | 3 | Single | 4 | Manager |
53 | 2 | 4 | 8 | 1 | 202,230 | Company Invited | Small Business | Female | 3 | Married | 4 | Senior Manager |
29 | 3 | 4 | 14 | 3 | 203,514 | Company Invited | Salaried | Male | 3 | Unmarried | 5 | Manager |
39 | 2 | 3 | 15 | 4 | 201,372 | Self Enquiry | Small Business | Male | 1 | Married | 5 | Manager |
46 | 4 | 4 | 9 | 5 | 204,366 | Self Enquiry | Salaried | Male | 3 | Married | 4 | Manager |
35 | 3 | 4 | 14 | 3 | 202,466 | Self Enquiry | Salaried | Female | 1 | Single | 4 | Senior Manager |
35 | 4 | 4 | 9 | 5 | 204,073 | Company Invited | Small Business | Female | 3 | Married | 3 | Executive |
33 | 4 | 5 | 7 | 3 | 204,596 | Company Invited | Salaried | Female | 1 | Married | 4 | Executive |
29 | 2 | 4 | 16 | 4 | 202,373 | Company Invited | Salaried | Female | 1 | Unmarried | 3 | Executive |
41 | 2 | 3 | 16 | 1 | 201,916 | Company Invited | Salaried | Male | 3 | Single | 3 | Manager |
43 | 3 | 6 | 36 | 3 | 203,268 | Self Enquiry | Small Business | Male | 1 | Unmarried | 3 | Manager |
35 | 3 | 6 | 13 | 4 | 204,329 | Company Invited | Small Business | Female | 3 | Married | 3 | Executive |
41 | 3 | 3 | 12 | 1 | 201,685 | Self Enquiry | Salaried | Female | 3 | Single | 3 | Senior Manager |
33 | 2 | 4 | 6 | 4 | 200,694 | Self Enquiry | Salaried | Female | 1 | Unmarried | 3 | Manager |
40 | 2 | 3 | 15 | 4 | 200,837 | Company Invited | Small Business | Fe Male | 1 | Unmarried | 3 | Senior Manager |
26 | 3 | 3 | 9 | 3 | 201,852 | Company Invited | Large Business | Male | 1 | Single | 5 | Executive |
41 | 2 | 3 | 25 | 1 | 201,712 | Self Enquiry | Salaried | Male | 1 | Married | 5 | Manager |
37 | 2 | 3 | 17 | 3 | 200,222 | Company Invited | Salaried | Male | 1 | Married | 3 | Senior Manager |
31 | 2 | 4 | 13 | 4 | 202,145 | Self Enquiry | Salaried | Male | 3 | Married | 3 | Executive |
45 | 3 | 6 | 8 | 3 | 204,867 | Self Enquiry | Salaried | Male | 3 | Single | 4 | Manager |
33 | 3 | 3 | 9 | 5 | 200,514 | Company Invited | Salaried | Male | 1 | Single | 5 | Executive |
33 | 4 | 4 | 9 | 4 | 202,795 | Self Enquiry | Small Business | Female | 1 | Divorced | 4 | Executive |
33 | 3 | 3 | 14 | 3 | 201,074 | Self Enquiry | Salaried | Male | 1 | Unmarried | 3 | Manager |
30 | 2 | 3 | 18 | 2 | 200,402 | Self Enquiry | Large Business | Female | 3 | Unmarried | 3 | Manager |
42 | 2 | 2 | 25 | 3 | 200,547 | Company Invited | Small Business | Male | 1 | Married | 3 | Executive |
46 | 2 | 3 | 8 | 5 | 201,899 | Self Enquiry | Salaried | Male | 1 | Married | 3 | AVP |
51 | 4 | 4 | 16 | 5 | 204,656 | Self Enquiry | Salaried | Male | 1 | Married | 3 | Executive |
30 | 2 | 5 | 8 | 1 | 201,880 | Self Enquiry | Salaried | Female | 1 | Single | 3 | Manager |
37 | 3 | 3 | 25 | 5 | 202,742 | Company Invited | Salaried | Male | 1 | Divorced | 3 | Executive |
28 | 2 | 3 | 6 | 4 | 201,323 | Company Invited | Salaried | Male | 2 | Married | 3 | Executive |
42 | 2 | 3 | 12 | 3 | 201,357 | Self Enquiry | Small Business | Male | 1 | Married | 5 | Senior Manager |
44 | 2 | 3 | 10 | 2 | 200,617 | Self Enquiry | Small Business | Male | 1 | Single | 4 | Manager |
39 | 3 | 5 | 9 | 1 | 203,637 | Company Invited | Small Business | Female | 1 | Single | 4 | Executive |
42 | 2 | 2 | 23 | 2 | 200,253 | Self Enquiry | Salaried | Female | 1 | Unmarried | 5 | Manager |
39 | 2 | 3 | 28 | 5 | 202,223 | Company Invited | Small Business | Fe Male | 1 | Unmarried | 5 | Senior Manager |
28 | 2 | 5 | 6 | 3 | 200,944 | Company Invited | Salaried | Female | 1 | Divorced | 3 | Manager |
43 | 3 | 3 | 20 | 5 | 202,079 | Self Enquiry | Salaried | Male | 1 | Married | 5 | AVP |
45 | 4 | 4 | 22 | 3 | 203,372 | Self Enquiry | Small Business | Female | 1 | Divorced | 3 | Senior Manager |
53 | 4 | 4 | 13 | 4 | 204,382 | Self Enquiry | Large Business | Male | 1 | Married | 5 | Manager |
42 | 4 | 4 | 16 | 1 | 204,062 | Self Enquiry | Salaried | Male | 1 | Married | 5 | Executive |
36 | 3 | 3 | 33 | 3 | 200,009 | Self Enquiry | Small Business | Male | 1 | Divorced | 3 | Manager |
22 | 4 | 5 | 7 | 5 | 203,259 | Self Enquiry | Large Business | Female | 1 | Single | 4 | Executive |
37 | 4 | 4 | 12 | 2 | 202,664 | Self Enquiry | Salaried | Male | 1 | Unmarried | 4 | Manager |
30 | 3 | 4 | 20 | 3 | 203,501 | Company Invited | Large Business | Fe Male | 3 | Unmarried | 4 | Manager |
36 | 4 | 5 | 18 | 5 | 203,967 | Company Invited | Small Business | Male | 1 | Married | 5 | Senior Manager |
40 | 2 | 3 | 10 | 5 | 200,186 | Self Enquiry | Small Business | Female | 1 | Divorced | 3 | VP |
51 | 2 | 5 | 14 | 2 | 200,136 | Company Invited | Salaried | Male | 1 | Unmarried | 3 | Senior Manager |
39 | 3 | 5 | 7 | 3 | 203,835 | Self Enquiry | Salaried | Male | 3 | Unmarried | 5 | Executive |
43 | 2 | 4 | 18 | 3 | 200,390 | Self Enquiry | Salaried | Male | 1 | Married | 4 | AVP |
35 | 3 | 3 | 10 | 4 | 200,040 | Self Enquiry | Salaried | Male | 1 | Married | 3 | Executive |
40 | 4 | 4 | 9 | 2 | 202,695 | Company Invited | Large Business | Female | 1 | Single | 3 | Senior Manager |
27 | 3 | 4 | 17 | 1 | 203,753 | Self Enquiry | Small Business | Male | 3 | Unmarried | 3 | Manager |
26 | 2 | 3 | 8 | 5 | 200,762 | Company Invited | Salaried | Male | 1 | Divorced | 5 | Executive |
43 | 3 | 3 | 32 | 2 | 200,119 | Company Invited | Salaried | Male | 3 | Divorced | 3 | AVP |
32 | 4 | 4 | 18 | 2 | 203,339 | Self Enquiry | Small Business | Male | 1 | Divorced | 5 | Manager |
35 | 3 | 5 | 12 | 2 | 202,560 | Self Enquiry | Small Business | Female | 1 | Single | 5 | Senior Manager |
34 | 3 | 5 | 11 | 4 | 204,135 | Self Enquiry | Small Business | Female | 1 | Married | 4 | Executive |
31 | 2 | 4 | 14 | 4 | 201,016 | Self Enquiry | Salaried | Female | 1 | Single | 4 | Executive |
35 | 4 | 4 | 16 | 1 | 204,748 | Self Enquiry | Salaried | Female | 3 | Married | 3 | Manager |
42 | 3 | 6 | 16 | 5 | 204,865 | Company Invited | Salaried | Male | 3 | Married | 3 | AVP |
34 | 2 | 3 | 14 | 5 | 202,030 | Self Enquiry | Salaried | Female | 1 | Married | 5 | Manager |
34 | 3 | 4 | 9 | 3 | 202,680 | Self Enquiry | Salaried | Female | 1 | Divorced | 5 | Executive |
34 | 2 | 3 | 13 | 3 | 200,022 | Self Enquiry | Salaried | Fe Male | 1 | Unmarried | 4 | Senior Manager |
39 | 3 | 4 | 36 | 2 | 202,643 | Self Enquiry | Large Business | Male | 1 | Divorced | 3 | Manager |
29 | 3 | 4 | 12 | 1 | 203,965 | Self Enquiry | Large Business | Male | 1 | Unmarried | 3 | Executive |
35 | 2 | 3 | 8 | 3 | 201,288 | Company Invited | Small Business | Male | 1 | Married | 3 | Manager |
26 | 2 | 4 | 10 | 2 | 200,293 | Self Enquiry | Small Business | Male | 3 | Single | 3 | Manager |
37 | 3 | 4 | 10 | 2 | 202,562 | Self Enquiry | Salaried | Female | 1 | Married | 3 | Executive |
35 | 4 | 4 | 16 | 3 | 203,734 | Company Invited | Salaried | Male | 1 | Married | 5 | Manager |
40 | 3 | 4 | 9 | 3 | 204,727 | Company Invited | Salaried | Male | 1 | Married | 3 | AVP |
33 | 2 | 3 | 11 | 2 | 200,363 | Self Enquiry | Small Business | Female | 3 | Single | 3 | Executive |
38 | 3 | 4 | 15 | 4 | 200,642 | Self Enquiry | Small Business | Male | 3 | Divorced | 4 | Executive |
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