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chandrachurhghosh
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tourism_project_data

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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 29 missing columns ({'Occupation_Salaried', 'CityTier', 'ProductPitched_King', 'MaritalStatus_Single', 'OwnCar', 'NumberOfPersonVisiting', 'ProductPitched_Standard', 'Designation_Senior Manager', 'NumberOfFollowups', 'Gender_Female', 'PitchSatisfactionScore', 'ProductPitched_Super Deluxe', 'MaritalStatus_Unmarried', 'ProductPitched_Deluxe', 'NumberOfChildrenVisiting', 'NumberOfTrips', 'MonthlyIncome', 'Gender_Male', 'Occupation_Small Business', 'Passport', 'Designation_Executive', 'Age', 'MaritalStatus_Married', 'Designation_Manager', 'DurationOfPitch', 'Designation_VP', 'Occupation_Large Business', 'TypeofContact_Self Enquiry', 'PreferredPropertyStar'}).

This happened while the csv dataset builder was generating data using

hf://datasets/chandrachurhghosh/tourism_project_data/data/y_train.csv (at revision b8365c7791cf17a781513d1d6c59f2bc1986f9a7), [/tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/X_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/X_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/y_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/y_train.csv), /tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/y_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/y_train.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 675, 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('int64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('int64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('int64'), 'PreferredPropertyStar': Value('int64'), 'NumberOfTrips': Value('int64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('int64'), 'MonthlyIncome': Value('int64'), 'TypeofContact_Self Enquiry': Value('bool'), 'Occupation_Large Business': Value('bool'), 'Occupation_Salaried': Value('bool'), 'Occupation_Small Business': Value('bool'), 'Gender_Female': Value('bool'), 'Gender_Male': Value('bool'), 'ProductPitched_Deluxe': Value('bool'), 'ProductPitched_King': Value('bool'), 'ProductPitched_Standard': Value('bool'), 'ProductPitched_Super Deluxe': Value('bool'), 'MaritalStatus_Married': Value('bool'), 'MaritalStatus_Single': Value('bool'), 'MaritalStatus_Unmarried': Value('bool'), 'Designation_Executive': Value('bool'), 'Designation_Manager': Value('bool'), 'Designation_Senior Manager': Value('bool'), 'Designation_VP': Value('bool')}
              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 1 new columns ({'ProdTaken'}) and 29 missing columns ({'Occupation_Salaried', 'CityTier', 'ProductPitched_King', 'MaritalStatus_Single', 'OwnCar', 'NumberOfPersonVisiting', 'ProductPitched_Standard', 'Designation_Senior Manager', 'NumberOfFollowups', 'Gender_Female', 'PitchSatisfactionScore', 'ProductPitched_Super Deluxe', 'MaritalStatus_Unmarried', 'ProductPitched_Deluxe', 'NumberOfChildrenVisiting', 'NumberOfTrips', 'MonthlyIncome', 'Gender_Male', 'Occupation_Small Business', 'Passport', 'Designation_Executive', 'Age', 'MaritalStatus_Married', 'Designation_Manager', 'DurationOfPitch', 'Designation_VP', 'Occupation_Large Business', 'TypeofContact_Self Enquiry', 'PreferredPropertyStar'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/chandrachurhghosh/tourism_project_data/data/y_train.csv (at revision b8365c7791cf17a781513d1d6c59f2bc1986f9a7), [/tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/X_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/X_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/y_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/data/y_train.csv), /tmp/hf-datasets-cache/medium/datasets/89880221828000-config-parquet-and-info-chandrachurhghosh-tourism-9b8717cb/hub/datasets--chandrachurhghosh--tourism_project_data/snapshots/b8365c7791cf17a781513d1d6c59f2bc1986f9a7/y_train.csv (origin=hf://datasets/chandrachurhghosh/tourism_project_data@b8365c7791cf17a781513d1d6c59f2bc1986f9a7/y_train.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)

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Age int64	CityTier int64	DurationOfPitch int64	NumberOfPersonVisiting int64	NumberOfFollowups int64	PreferredPropertyStar int64	NumberOfTrips int64	Passport int64	PitchSatisfactionScore int64	OwnCar int64	NumberOfChildrenVisiting int64	MonthlyIncome int64	TypeofContact_Self Enquiry bool	Occupation_Large Business bool	Occupation_Salaried bool	Occupation_Small Business bool	Gender_Female bool	Gender_Male bool	ProductPitched_Deluxe bool	ProductPitched_King bool	ProductPitched_Standard bool	ProductPitched_Super Deluxe bool	MaritalStatus_Married bool	MaritalStatus_Single bool	MaritalStatus_Unmarried bool	Designation_Executive bool	Designation_Manager bool	Designation_Senior Manager bool	Designation_VP bool
26	2	23	2	3	3	1	1	5	0	1	17,741	false	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
42	1	6	2	4	3	1	1	3	0	1	21,062	true	false	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false
56	1	9	4	4	4	5	0	1	0	2	29,654	false	false	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false
27	3	36	4	6	5	2	0	3	1	2	23,647	false	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
37	1	9	4	4	3	6	0	5	1	1	21,221	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false
31	2	28	2	5	3	2	0	1	0	1	24,852	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
49	3	8	3	4	4	3	0	3	1	2	20,390	false	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
43	1	16	2	3	3	1	0	5	0	0	17,455	false	false	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false
40	3	9	3	2	3	2	0	1	0	1	26,558	true	false	true	false	false	true	false	false	true	false	false	false	true	false	false	true	false
58	1	31	3	3	3	5	1	4	1	2	28,117	true	false	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false
56	1	27	3	4	3	5	1	2	1	2	24,093	true	true	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false
56	3	12	2	2	5	1	0	3	1	0	28,212	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	false
52	3	34	3	4	3	3	1	5	1	2	32,704	true	false	true	false	false	true	true	false	false	false	false	true	false	false	true	false	false
36	1	7	3	5	5	3	1	1	1	0	25,252	true	true	false	false	true	false	false	false	true	false	true	false	false	false	false	true	false
24	1	15	2	4	4	2	0	5	1	0	17,694	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
30	3	14	3	3	3	6	0	3	1	0	22,264	true	false	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false
25	1	15	2	3	5	4	0	1	1	0	17,096	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
37	1	11	3	5	3	3	0	5	1	2	24,488	true	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
20	1	16	2	3	3	2	1	5	1	1	16,009	true	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false
44	3	11	3	5	3	5	0	3	1	1	28,909	true	false	false	true	true	false	false	false	true	false	false	true	false	false	false	true	false
29	3	12	4	4	3	3	0	3	0	2	23,586	true	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
36	1	14	3	4	3	5	0	3	0	2	28,899	true	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true	false
49	1	11	4	5	3	2	0	5	1	2	29,677	true	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true	false
36	1	12	2	2	3	4	0	2	0	0	18,038	true	false	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false
41	3	9	3	4	4	2	0	1	0	1	24,393	true	false	false	true	true	false	true	false	false	false	true	false	false	false	true	false	false
34	2	10	3	4	4	5	1	5	0	2	20,955	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
30	3	9	3	4	3	3	0	2	0	1	23,232	false	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
45	1	13	3	3	5	2	0	2	1	2	20,210	false	false	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false
56	1	27	3	4	3	5	1	1	0	1	24,093	true	true	false	false	false	true	true	false	false	false	true	false	false	false	true	false	false
18	1	11	3	3	3	2	0	1	0	1	16,051	false	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
40	1	7	3	2	3	2	0	3	1	1	28,291	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
33	1	6	3	3	3	2	1	3	1	0	17,686	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
31	3	12	2	5	3	3	0	1	1	1	24,796	true	false	false	true	true	false	true	false	false	false	true	false	false	false	true	false	false
29	1	13	3	5	3	3	1	4	1	1	21,381	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
44	1	23	3	5	3	3	0	4	1	1	17,290	false	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
38	1	12	3	5	3	1	1	2	0	2	20,329	false	false	false	true	true	false	true	false	false	false	true	false	false	false	true	false	false
36	1	22	2	1	5	2	0	1	1	0	17,743	true	false	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false
30	3	13	2	3	4	1	0	1	0	0	17,983	true	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false
37	3	12	3	3	3	5	0	3	0	0	21,502	true	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false
49	3	36	4	4	3	5	0	4	0	2	31,182	true	false	false	true	true	false	false	false	true	false	true	false	false	false	false	true	false
26	1	9	3	4	3	8	1	5	0	1	22,655	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
36	3	14	4	4	3	3	0	3	1	1	21,082	true	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false
39	1	36	3	4	3	3	0	3	1	1	21,084	false	false	true	false	true	false	true	false	false	false	false	true	false	false	true	false	false
35	1	9	2	4	3	2	0	1	1	1	16,281	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
51	1	6	1	4	5	4	0	2	1	0	22,484	false	false	false	true	true	false	false	false	true	false	false	false	true	false	false	true	false
37	3	8	3	3	3	5	1	3	0	2	24,602	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
28	1	10	3	4	3	3	0	1	0	1	20,384	false	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
35	3	14	3	4	3	5	1	5	1	2	25,377	false	false	false	true	true	false	false	false	true	false	true	false	false	false	false	true	false
31	1	14	3	5	4	3	0	5	0	1	20,819	true	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
31	2	24	2	1	5	1	0	1	1	0	17,956	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
30	1	7	4	2	3	2	1	3	0	2	24,972	false	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
35	1	22	3	3	3	5	1	1	0	0	22,632	true	false	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false
46	3	13	3	5	3	8	0	4	1	1	27,543	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	true	false
21	3	28	3	2	3	3	0	3	1	2	21,356	true	false	false	true	false	true	false	false	false	false	false	false	true	true	false	false	false
30	3	33	2	3	3	1	0	3	1	0	20,304	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
37	3	10	3	5	3	6	0	1	0	1	28,377	false	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
48	3	10	3	4	3	2	1	5	1	1	32,448	false	false	false	true	true	false	false	false	false	true	true	false	false	false	false	false	false
36	1	16	3	4	3	3	0	4	0	2	23,776	true	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
49	1	8	2	3	3	4	0	3	1	0	34,161	false	false	true	false	false	true	false	true	false	false	true	false	false	false	false	false	true
33	1	6	2	4	3	1	0	4	1	1	21,949	true	false	true	false	true	false	true	false	false	false	false	false	true	false	true	false	false
20	1	10	4	4	4	3	0	3	1	3	20,161	true	false	false	true	true	false	false	false	false	false	false	true	false	true	false	false	false
30	3	7	3	5	3	8	1	1	1	2	21,478	true	false	false	true	true	false	false	false	false	false	true	false	false	true	false	false	false
32	1	30	4	5	3	2	0	3	1	2	24,260	true	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false
58	1	8	2	3	4	1	1	3	1	0	34,246	true	false	true	false	false	true	false	true	false	false	false	true	false	false	false	false	true
29	1	9	3	5	3	3	1	4	1	1	22,545	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false
33	1	13	3	4	3	5	0	1	0	2	21,716	true	false	true	false	false	true	false	false	false	false	false	false	true	true	false	false	false
35	3	6	3	3	4	2	0	4	0	0	22,295	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
42	1	11	3	3	3	5	0	3	0	1	17,093	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
42	1	29	2	3	3	3	0	3	0	0	30,992	true	false	true	false	true	false	false	false	false	true	false	true	false	false	false	false	false
48	1	8	3	1	4	6	0	2	0	2	17,559	true	true	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false
27	3	14	2	3	4	2	0	2	0	0	21,214	true	false	false	true	true	false	true	false	false	false	false	false	false	false	true	false	false
22	3	29	3	4	3	3	0	2	1	2	22,125	true	true	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false
28	1	30	3	4	5	2	0	2	0	0	23,722	false	true	false	false	false	true	false	false	true	false	false	false	true	false	false	true	false
38	1	21	4	4	5	3	0	4	1	1	21,712	true	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
41	1	18	2	3	3	2	0	4	1	1	34,545	true	true	false	false	true	false	false	true	false	false	true	false	false	false	false	false	true
50	1	30	3	3	3	4	1	4	1	2	28,973	true	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
35	3	7	4	2	3	2	0	5	0	2	28,403	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
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