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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 ({'ProdTaken'}) and 18 missing columns ({'Gender', 'Age', 'DurationOfPitch', 'NumberOfChildrenVisiting', 'TypeofContact', 'NumberOfPersonVisiting', 'PreferredPropertyStar', 'NumberOfTrips', 'NumberOfFollowups', 'Passport', 'CityTier', 'PitchSatisfactionScore', 'ProductPitched', 'Occupation', 'Designation', 'OwnCar', 'MaritalStatus', 'MonthlyIncome'}).
This happened while the csv dataset builder was generating data using
hf://datasets/lcsekar/tourism-project-data/y_train.csv (at revision d68eb56790d5e8ede65b589fe882a8894c1374d0)
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 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 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
ProdTaken: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 377
to
{'Age': Value('float64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'NumberOfTrips': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'CityTier': Value('int64'), 'PreferredPropertyStar': Value('float64'), 'PitchSatisfactionScore': Value('int64'), 'Designation': Value('string'), 'Gender': Value('string'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'ProductPitched': Value('string'), 'MaritalStatus': Value('string'), 'Passport': Value('int64'), 'OwnCar': Value('int64')}
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 1339, 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 972, 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 1833, 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 ({'ProdTaken'}) and 18 missing columns ({'Gender', 'Age', 'DurationOfPitch', 'NumberOfChildrenVisiting', 'TypeofContact', 'NumberOfPersonVisiting', 'PreferredPropertyStar', 'NumberOfTrips', 'NumberOfFollowups', 'Passport', 'CityTier', 'PitchSatisfactionScore', 'ProductPitched', 'Occupation', 'Designation', 'OwnCar', 'MaritalStatus', 'MonthlyIncome'}).
This happened while the csv dataset builder was generating data using
hf://datasets/lcsekar/tourism-project-data/y_train.csv (at revision d68eb56790d5e8ede65b589fe882a8894c1374d0)
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 | DurationOfPitch float64 | NumberOfPersonVisiting int64 | NumberOfFollowups float64 | NumberOfTrips float64 | NumberOfChildrenVisiting float64 | MonthlyIncome float64 | CityTier int64 | PreferredPropertyStar float64 | PitchSatisfactionScore int64 | Designation string | Gender string | TypeofContact string | Occupation string | ProductPitched string | MaritalStatus string | Passport int64 | OwnCar int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
55 | 17 | 4 | 4 | 8 | 1 | 23,118 | 1 | 5 | 1 | Manager | Female | Self Enquiry | Small Business | Deluxe | Unmarried | 1 | 0 |
39 | 9 | 3 | 4 | 7 | 2 | 22,622 | 1 | 3 | 4 | Executive | Male | Self Enquiry | Salaried | Basic | Unmarried | 1 | 0 |
42 | 8 | 3 | 1 | 1 | 2 | 21,272 | 2 | 5 | 2 | Manager | Male | Company Invited | Small Business | Deluxe | Divorced | 0 | 0 |
37 | 12 | 3 | 5 | 2 | 1 | 98,678 | 1 | 5 | 2 | Executive | Female | Self Enquiry | Salaried | Basic | Divorced | 1 | 1 |
23 | 7 | 3 | 5 | 8 | 1 | 23,453 | 1 | 3 | 2 | Manager | Male | Self Enquiry | Salaried | Deluxe | Divorced | 0 | 1 |
33 | 31 | 4 | 4 | 3 | 1 | 23,987 | 1 | 3 | 4 | Manager | Male | Company Invited | Salaried | Deluxe | Divorced | 0 | 1 |
38 | 24 | 2 | 5 | 4 | 1 | 20,811 | 1 | 3 | 5 | Manager | Male | Self Enquiry | Small Business | Deluxe | Married | 1 | 0 |
60 | 9 | 4 | 5 | 5 | 3 | 32,404 | 1 | 3 | 5 | AVP | Female | Self Enquiry | Salaried | Super Deluxe | Single | 1 | 0 |
53 | 8 | 2 | 4 | 3 | 0 | 22,525 | 3 | 4 | 1 | Senior Manager | Female | Company Invited | Small Business | Standard | Married | 0 | 1 |
37 | 33 | 4 | 4 | 8 | 1 | 24,025 | 1 | 3 | 3 | Manager | Male | Self Enquiry | Salaried | Deluxe | Married | 0 | 1 |
60 | 34 | 3 | 4 | 5 | 0 | 25,266 | 3 | 5 | 1 | Senior Manager | Female | Company Invited | Small Business | Standard | Married | 0 | 1 |
43 | 36 | 3 | 6 | 6 | 2 | 22,950 | 1 | 3 | 3 | Manager | Male | Self Enquiry | Small Business | Deluxe | Unmarried | 0 | 1 |
35 | 22 | 2 | 1 | 1 | 1 | 17,426 | 1 | 4 | 4 | Executive | Male | Self Enquiry | Small Business | Basic | Married | 0 | 1 |
43 | 10 | 4 | 2 | 4 | 1 | 23,909 | 1 | 3 | 5 | Manager | Female | Self Enquiry | Salaried | Deluxe | Married | 1 | 1 |
52 | 34 | 2 | 1 | 3 | 0 | 28,247 | 1 | 3 | 4 | AVP | Female | Company Invited | Small Business | Super Deluxe | Divorced | 1 | 0 |
59 | 9 | 3 | 5 | 2 | 1 | 21,058 | 1 | 3 | 2 | Executive | Male | Company Invited | Salaried | Basic | Married | 1 | 0 |
36 | 33 | 3 | 3 | 7 | 0 | 20,237 | 1 | 3 | 3 | Manager | Male | Self Enquiry | Small Business | Deluxe | Divorced | 0 | 1 |
29 | 23 | 3 | 4 | 3 | 1 | 20,822 | 1 | 3 | 3 | Executive | Male | Company Invited | Small Business | Basic | Single | 0 | 0 |
37 | 16 | 3 | 5 | 4 | 2 | 27,525 | 1 | 4 | 4 | Manager | Male | Self Enquiry | Small Business | Deluxe | Married | 1 | 0 |
38 | 8 | 2 | 3 | 1 | 1 | 21,553 | 1 | 3 | 2 | Manager | Male | Self Enquiry | Salaried | Deluxe | Divorced | 0 | 0 |
31 | 6 | 2 | 5 | 2 | 1 | 16,359 | 3 | 3 | 3 | Executive | Female | Company Invited | Salaried | Basic | Single | 0 | 1 |
46 | 16 | 4 | 4 | 6 | 1 | 29,439 | 3 | 5 | 2 | Senior Manager | Male | Self Enquiry | Small Business | Standard | Married | 1 | 1 |
41 | 14 | 3 | 4 | 3 | 1 | 23,339 | 3 | 4 | 5 | Executive | Male | Self Enquiry | Small Business | Basic | Unmarried | 0 | 0 |
35 | 13 | 3 | 3 | 2 | 1 | 20,363 | 1 | 4 | 3 | Executive | Male | Self Enquiry | Salaried | Basic | Single | 1 | 1 |
29 | 16 | 3 | 3 | 2 | 0 | 17,642 | 3 | 3 | 4 | Executive | Male | Self Enquiry | Salaried | Basic | Single | 0 | 1 |
51 | 27 | 3 | 3 | 1 | 2 | 20,441 | 3 | 3 | 5 | Manager | Male | Self Enquiry | Small Business | Deluxe | Single | 1 | 0 |
39 | 6 | 2 | 2 | 1 | 0 | 24,613 | 1 | 3 | 3 | Senior Manager | Male | Self Enquiry | Small Business | Standard | Married | 0 | 1 |
37 | 22 | 3 | 4 | 5 | 2 | 21,334 | 3 | 3 | 5 | Manager | Male | Self Enquiry | Small Business | Deluxe | Married | 0 | 1 |
33 | 23 | 2 | 3 | 2 | 1 | 32,444 | 3 | 3 | 3 | AVP | Male | Company Invited | Salaried | Super Deluxe | Single | 0 | 1 |
51 | 19 | 4 | 4 | 6 | 3 | 27,886 | 3 | 3 | 5 | Senior Manager | Female | Company Invited | Small Business | Standard | Unmarried | 0 | 1 |
42 | 12 | 3 | 2 | 5 | 1 | 25,548 | 1 | 4 | 5 | Manager | Male | Self Enquiry | Salaried | Deluxe | Unmarried | 0 | 1 |
33 | 15 | 4 | 5 | 3 | 1 | 23,906 | 3 | 4 | 2 | Manager | Female | Self Enquiry | Large Business | Deluxe | Divorced | 1 | 1 |
30 | 17 | 4 | 4 | 2 | 1 | 21,969 | 1 | 4 | 5 | Executive | Female | Company Invited | Salaried | Basic | Married | 0 | 1 |
41 | 7 | 3 | 6 | 4 | 1 | 26,135 | 3 | 3 | 3 | Manager | Male | Self Enquiry | Small Business | Deluxe | Divorced | 1 | 1 |
38 | 12 | 3 | 2 | 2 | 1 | 22,178 | 1 | 3 | 5 | Executive | Male | Company Invited | Large Business | Basic | Unmarried | 0 | 1 |
28 | 9 | 3 | 6 | 5 | 2 | 23,749 | 3 | 3 | 4 | Manager | Male | Company Invited | Salaried | Deluxe | Unmarried | 0 | 1 |
27 | 24 | 4 | 6 | 3 | 3 | 20,983 | 1 | 3 | 3 | Executive | Male | Self Enquiry | Small Business | Basic | Married | 0 | 0 |
27 | 11 | 2 | 3 | 2 | 1 | 17,478 | 1 | 4 | 3 | Executive | Female | Self Enquiry | Salaried | Basic | Single | 1 | 0 |
24 | 11 | 3 | 2 | 4 | 2 | 21,497 | 1 | 5 | 4 | Executive | Male | Self Enquiry | Small Business | Basic | Married | 0 | 0 |
34 | 22 | 3 | 4 | 2 | 2 | 17,553 | 1 | 3 | 5 | Executive | Female | Company Invited | Salaried | Basic | Single | 0 | 1 |
37 | 17 | 3 | 5 | 2 | 1 | 25,772 | 3 | 5 | 5 | Senior Manager | Male | Self Enquiry | Small Business | Standard | Married | 0 | 0 |
34 | 7 | 3 | 4 | 1 | 0 | 20,343 | 1 | 5 | 1 | Manager | Male | Company Invited | Small Business | Deluxe | Single | 0 | 0 |
30 | 32 | 2 | 4 | 6 | 1 | 21,696 | 3 | 5 | 2 | Manager | Female | Company Invited | Small Business | Deluxe | Unmarried | 0 | 0 |
27 | 23 | 2 | 3 | 1 | 0 | 18,058 | 1 | 4 | 4 | Executive | Male | Self Enquiry | Large Business | Basic | Married | 1 | 0 |
36 | 9 | 3 | 5 | 4 | 1 | 28,952 | 1 | 4 | 4 | Senior Manager | Male | Self Enquiry | Salaried | Standard | Married | 0 | 1 |
40 | 30 | 3 | 3 | 2 | 1 | 18,319 | 1 | 3 | 3 | Manager | Male | Self Enquiry | Large Business | Deluxe | Married | 0 | 1 |
38 | 7 | 3 | 4 | 6 | 2 | 26,169 | 1 | 3 | 5 | Senior Manager | Female | Self Enquiry | Large Business | Standard | Unmarried | 0 | 1 |
33 | 9 | 3 | 5 | 2 | 1 | 28,585 | 3 | 4 | 1 | Manager | Male | Self Enquiry | Small Business | Deluxe | Single | 1 | 1 |
30 | 16 | 2 | 5 | 2 | 1 | 22,661 | 1 | 3 | 1 | Executive | Male | Self Enquiry | Salaried | Basic | Unmarried | 0 | 1 |
52 | 6 | 3 | 3 | 3 | 2 | 32,099 | 1 | 3 | 1 | AVP | Male | Self Enquiry | Salaried | Super Deluxe | Married | 0 | 1 |
33 | 7 | 3 | 6 | 8 | 2 | 25,413 | 3 | 4 | 3 | Manager | Male | Self Enquiry | Salaried | Deluxe | Unmarried | 0 | 0 |
20 | 17 | 4 | 5 | 3 | 3 | 20,537 | 1 | 4 | 5 | Executive | Female | Company Invited | Small Business | Basic | Single | 1 | 0 |
38 | 29 | 2 | 4 | 1 | 0 | 24,526 | 1 | 3 | 3 | Senior Manager | Male | Company Invited | Salaried | Standard | Unmarried | 0 | 0 |
31 | 17 | 2 | 3 | 4 | 0 | 17,356 | 1 | 3 | 3 | Executive | Male | Self Enquiry | Salaried | Basic | Married | 1 | 0 |
52 | 11 | 3 | 4 | 2 | 2 | 21,139 | 1 | 3 | 2 | Executive | Male | Self Enquiry | Salaried | Basic | Divorced | 1 | 1 |
39 | 10 | 3 | 4 | 5 | 1 | 22,995 | 1 | 3 | 5 | Manager | Female | Self Enquiry | Large Business | Deluxe | Unmarried | 1 | 1 |
40 | 11 | 3 | 5 | 6 | 2 | 24,580 | 3 | 3 | 5 | Manager | Female | Self Enquiry | Salaried | Deluxe | Married | 0 | 1 |
26 | 26 | 4 | 4 | 5 | 3 | 22,347 | 1 | 3 | 5 | Executive | Male | Self Enquiry | Small Business | Basic | Divorced | 0 | 1 |
47 | 15 | 2 | 5 | 1 | 1 | 27,936 | 3 | 3 | 5 | AVP | Male | Company Invited | Salaried | Super Deluxe | Married | 0 | 1 |
28 | 16 | 3 | 3 | 2 | 2 | 16,052 | 3 | 4 | 5 | Executive | Male | Self Enquiry | Small Business | Basic | Married | 0 | 0 |
19 | 15 | 4 | 4 | 3 | 1 | 20,582 | 1 | 3 | 5 | Executive | Male | Company Invited | Small Business | Basic | Single | 0 | 0 |
52 | 9 | 2 | 4 | 2 | 0 | 31,856 | 3 | 5 | 5 | AVP | Male | Self Enquiry | Small Business | Super Deluxe | Married | 0 | 1 |
20 | 7 | 4 | 6 | 2 | 2 | 21,003 | 3 | 5 | 3 | Executive | Female | Company Invited | Large Business | Basic | Single | 0 | 1 |
43 | 15 | 3 | 4 | 2 | 2 | 25,503 | 3 | 4 | 3 | Manager | Male | Self Enquiry | Small Business | Deluxe | Divorced | 0 | 0 |
30 | 8 | 4 | 4 | 3 | 3 | 22,438 | 1 | 3 | 1 | Executive | Female | Self Enquiry | Salaried | Basic | Married | 0 | 1 |
51 | 7 | 4 | 4 | 2 | 2 | 25,406 | 3 | 3 | 3 | Manager | Male | Company Invited | Salaried | Deluxe | Married | 0 | 1 |
41 | 16 | 4 | 5 | 2 | 2 | 23,554 | 1 | 3 | 5 | Manager | Male | Company Invited | Salaried | Deluxe | Married | 0 | 0 |
33 | 15 | 3 | 4 | 3 | 2 | 27,676 | 3 | 3 | 4 | Senior Manager | Female | Company Invited | Small Business | Standard | Unmarried | 0 | 1 |
22 | 16 | 3 | 4 | 3 | 1 | 21,288 | 3 | 3 | 4 | Executive | Male | Company Invited | Small Business | Basic | Unmarried | 0 | 0 |
40 | 16 | 2 | 1 | 4 | 1 | 17,213 | 1 | 3 | 3 | Executive | Female | Self Enquiry | Salaried | Basic | Married | 1 | 0 |
53 | 6 | 2 | 3 | 1 | 1 | 23,381 | 3 | 5 | 1 | Manager | Female | Self Enquiry | Small Business | Deluxe | Unmarried | 0 | 1 |
29 | 9 | 3 | 5 | 2 | 1 | 21,239 | 1 | 5 | 4 | Executive | Male | Company Invited | Small Business | Basic | Single | 0 | 0 |
44 | 16 | 4 | 4 | 5 | 3 | 24,357 | 1 | 3 | 3 | Manager | Male | Company Invited | Small Business | Deluxe | Married | 1 | 1 |
23 | 13 | 4 | 4 | 2 | 1 | 21,451 | 1 | 3 | 2 | Executive | Male | Self Enquiry | Small Business | Basic | Divorced | 0 | 1 |
43 | 36 | 3 | 6 | 6 | 1 | 22,950 | 1 | 3 | 3 | Manager | Male | Self Enquiry | Small Business | Deluxe | Unmarried | 0 | 1 |
33 | 23 | 2 | 3 | 2 | 0 | 32,444 | 3 | 3 | 3 | AVP | Male | Company Invited | Salaried | Super Deluxe | Single | 0 | 1 |
37 | 7 | 3 | 4 | 6 | 2 | 25,331 | 3 | 3 | 1 | Manager | Female | Company Invited | Small Business | Deluxe | Unmarried | 0 | 1 |
37 | 16 | 2 | 1 | 2 | 1 | 28,744 | 1 | 3 | 1 | Senior Manager | Female | Self Enquiry | Salaried | Standard | Married | 1 | 0 |
40 | 10 | 3 | 4 | 6 | 2 | 23,916 | 3 | 3 | 4 | Manager | Female | Self Enquiry | Small Business | Deluxe | Married | 1 | 1 |
36 | 7 | 3 | 2 | 5 | 2 | 21,184 | 1 | 3 | 3 | Executive | Female | Self Enquiry | Salaried | Basic | Single | 0 | 1 |
50 | 23 | 4 | 4 | 6 | 2 | 21,265 | 1 | 5 | 1 | Executive | Female | Self Enquiry | Small Business | Basic | Married | 1 | 1 |
21 | 6 | 3 | 4 | 2 | 2 | 17,174 | 3 | 4 | 5 | Executive | Female | Company Invited | Large Business | Basic | Single | 1 | 1 |
28 | 9 | 4 | 6 | 4 | 2 | 21,195 | 3 | 4 | 5 | VP | Female | Self Enquiry | Small Business | King | Single | 1 | 1 |
52 | 15 | 3 | 5 | 7 | 2 | 31,168 | 1 | 4 | 3 | Senior Manager | Male | Self Enquiry | Salaried | Standard | Divorced | 0 | 1 |
40 | 14 | 3 | 4 | 2 | 2 | 24,094 | 1 | 3 | 4 | Executive | Male | Self Enquiry | Small Business | Basic | Unmarried | 1 | 1 |
29 | 12 | 2 | 3 | 2 | 1 | 18,131 | 1 | 3 | 3 | Executive | Female | Self Enquiry | Small Business | Basic | Married | 0 | 0 |
35 | 17 | 3 | 4 | 3 | 1 | 24,884 | 1 | 5 | 5 | Senior Manager | Male | Company Invited | Small Business | Standard | Divorced | 1 | 1 |
38 | 13 | 4 | 4 | 6 | 1 | 25,180 | 3 | 3 | 3 | Manager | Male | Self Enquiry | Small Business | Deluxe | Married | 0 | 1 |
51 | 6 | 1 | 4 | 4 | 0 | 22,484 | 1 | 5 | 2 | Senior Manager | Female | Company Invited | Small Business | Standard | Unmarried | 0 | 1 |
22 | 16 | 3 | 4 | 3 | 1 | 21,288 | 3 | 3 | 4 | Executive | Male | Company Invited | Small Business | Basic | Unmarried | 0 | 1 |
36 | 19 | 2 | 3 | 5 | 1 | 17,143 | 2 | 4 | 3 | Executive | Male | Self Enquiry | Salaried | Basic | Married | 0 | 1 |
31 | 17 | 3 | 3 | 2 | 1 | 21,833 | 1 | 5 | 1 | Manager | Male | Self Enquiry | Small Business | Deluxe | Married | 1 | 1 |
28 | 16 | 3 | 4 | 3 | 2 | 22,783 | 3 | 3 | 1 | Manager | Male | Self Enquiry | Small Business | Deluxe | Unmarried | 0 | 0 |
50 | 7 | 3 | 5 | 2 | 1 | 32,642 | 1 | 3 | 3 | AVP | Female | Self Enquiry | Large Business | Super Deluxe | Single | 1 | 0 |
28 | 13 | 3 | 5 | 3 | 2 | 21,217 | 1 | 3 | 1 | Executive | Male | Self Enquiry | Salaried | Basic | Married | 0 | 1 |
40 | 14 | 3 | 3 | 3 | 0 | 21,516 | 1 | 5 | 1 | Manager | Female | Self Enquiry | Salaried | Deluxe | Married | 1 | 0 |
29 | 21 | 2 | 3 | 2 | 0 | 17,340 | 1 | 3 | 3 | Executive | Male | Self Enquiry | Salaried | Basic | Single | 0 | 0 |
40 | 17 | 4 | 4 | 2 | 1 | 32,142 | 1 | 3 | 3 | Senior Manager | Male | Self Enquiry | Small Business | Standard | Single | 0 | 1 |
29 | 7 | 3 | 4 | 2 | 1 | 20,832 | 1 | 3 | 4 | Executive | Male | Company Invited | Small Business | Basic | Single | 1 | 0 |
31 | 8 | 4 | 4 | 2 | 3 | 22,257 | 1 | 4 | 4 | Executive | Male | Self Enquiry | Small Business | Basic | Married | 1 | 1 |
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