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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'})

This happened while the csv dataset builder was generating data using

hf://datasets/Suunil-Dabral/tourism_project/tourism.csv (at revision 31c8e555409900ad1ce7d81ca501e0bd53cb0778)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              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, "' + 2647
              to
              {'Age': Value('float64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'Gender': Value('string'), 'ProductPitched': Value('string'), 'MaritalStatus': Value('string'), '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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 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'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Suunil-Dabral/tourism_project/tourism.csv (at revision 31c8e555409900ad1ce7d81ca501e0bd53cb0778)
              
              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 float64	CityTier int64	DurationOfPitch float64	NumberOfPersonVisiting int64	NumberOfFollowups float64	PreferredPropertyStar float64	NumberOfTrips float64	Passport int64	PitchSatisfactionScore int64	OwnCar int64	NumberOfChildrenVisiting float64	MonthlyIncome float64	TypeofContact string	Occupation string	Gender string	ProductPitched string	MaritalStatus string	Designation string
30	3	18	2	3	3	1	0	2	1	0	21,577	Self Enquiry	Large Business	Female	Deluxe	Single	Manager
28	1	13	3	5	3	3	0	2	1	2	21,217	Self Enquiry	Salaried	Male	Basic	Divorced	Executive
22	1	12	4	4	3	3	0	1	1	2	21,795	Self Enquiry	Small Business	Male	Basic	Single	Executive
30	3	27	3	4	3	3	0	3	0	2	20,835	Company Invited	Large Business	Female	Basic	Divorced	Executive
27	3	30	3	5	3	2	1	1	0	1	22,835	Self Enquiry	Small Business	Female	Deluxe	Married	Manager
37	1	17	2	3	3	2	1	3	0	1	27,185	Company Invited	Salaried	Male	Standard	Married	Senior Manager
28	1	12	2	4	3	2	1	4	1	1	17,703	Company Invited	Salaried	Male	Basic	Married	Executive
38	3	7	3	5	3	7	0	2	1	2	29,287	Self Enquiry	Salaried	Male	Standard	Divorced	Senior Manager
27	1	11	2	4	3	2	1	3	0	0	27,808	Self Enquiry	Large Business	Male	Standard	Married	Senior Manager
36	1	30	3	5	3	5	1	4	1	1	24,594	Self Enquiry	Salaried	Male	Deluxe	Married	Manager
55	1	26	4	4	5	2	1	3	0	1	24,163	Self Enquiry	Small Business	Female	Deluxe	Married	Manager
21	1	18	4	5	5	3	1	3	1	3	21,278	Self Enquiry	Small Business	Female	Basic	Single	Executive
29	3	7	3	5	3	3	1	1	0	1	25,512	Company Invited	Salaried	Male	Deluxe	Married	Manager
33	3	8	3	3	4	1	0	1	0	0	20,147	Company Invited	Small Business	Female	Deluxe	Single	Manager
29	1	14	3	4	3	2	1	3	1	1	20,056	Self Enquiry	Small Business	Male	Deluxe	Married	Manager
47	1	14	2	4	3	4	0	5	1	1	23,936	Company Invited	Small Business	Female	Deluxe	Married	Manager
25	1	14	3	4	3	3	1	4	0	1	21,564	Company Invited	Salaried	Female	Basic	Divorced	Executive
18	3	11	3	3	4	2	1	4	1	2	16,878	Company Invited	Small Business	Male	Basic	Single	Executive
31	3	26	2	3	3	2	0	2	1	0	21,932	Company Invited	Small Business	Female	Deluxe	Divorced	Manager
35	1	6	2	1	3	4	0	3	1	1	17,506	Self Enquiry	Salaried	Female	Basic	Single	Executive
31	3	11	3	3	3	2	0	1	0	2	20,476	Self Enquiry	Salaried	Female	Deluxe	Married	Manager
33	1	23	4	6	5	3	1	4	1	3	22,597	Company Invited	Salaried	Female	Basic	Single	Executive
44	1	8	2	3	3	6	1	1	0	1	25,209	Company Invited	Small Business	Male	Standard	Single	Senior Manager
48	1	23	4	2	3	3	1	2	0	1	23,745	Company Invited	Small Business	Male	Deluxe	Married	Manager
43	1	15	3	3	3	4	1	3	1	0	20,679	Company Invited	Small Business	Male	Deluxe	Married	Manager
36	1	7	3	2	3	5	0	3	0	1	21,184	Self Enquiry	Salaried	Female	Basic	Single	Executive
44	1	34	3	2	4	7	0	5	1	2	23,554	Self Enquiry	Large Business	Male	Basic	Married	Executive
39	1	9	3	5	4	3	0	1	1	1	21,118	Company Invited	Small Business	Female	Basic	Single	Executive
34	1	22	3	4	3	2	0	5	1	1	17,553	Company Invited	Salaried	Female	Basic	Single	Executive
33	3	14	4	5	3	3	0	3	1	3	24,162	Self Enquiry	Salaried	Male	Deluxe	Divorced	Manager
41	3	17	4	5	4	4	0	4	0	1	28,383	Self Enquiry	Small Business	Male	Standard	Married	Senior Manager
34	3	14	3	3	5	6	0	2	1	0	21,500	Self Enquiry	Salaried	Female	Deluxe	Divorced	Manager
27	1	23	3	4	4	4	1	2	1	2	21,051	Self Enquiry	Small Business	Male	Basic	Married	Executive
51	1	15	3	3	3	4	0	3	1	0	17,075	Self Enquiry	Salaried	Male	Basic	Divorced	Executive
46	1	16	3	4	4	2	0	4	1	1	21,026	Self Enquiry	Salaried	Male	Deluxe	Married	Manager
29	1	15	3	5	4	3	0	4	0	2	20,832	Self Enquiry	Salaried	Female	Basic	Single	Executive
38	3	9	4	4	3	6	1	3	0	3	28,280	Self Enquiry	Small Business	Male	Deluxe	Married	Manager
46	3	33	4	4	5	4	0	1	0	3	22,964	Company Invited	Salaried	Female	Deluxe	Married	Manager
59	1	9	3	5	3	2	1	2	0	1	21,058	Company Invited	Salaried	Male	Basic	Married	Executive
44	1	13	4	5	3	3	1	4	1	2	22,759	Self Enquiry	Small Business	Female	Deluxe	Single	Manager
37	2	20	3	5	5	6	1	5	1	2	23,317	Self Enquiry	Salaried	Male	Basic	Married	Executive
45	1	31	3	4	3	5	1	5	0	2	21,839	Company Invited	Salaried	Male	Basic	Married	Executive
40	1	30	3	3	3	2	0	3	1	1	18,319	Self Enquiry	Large Business	Male	Deluxe	Married	Manager
28	3	11	2	3	5	1	0	1	1	0	23,463	Self Enquiry	Small Business	Male	Deluxe	Single	Manager
36	1	29	3	6	3	2	0	4	1	2	22,908	Self Enquiry	Salaried	Male	Basic	Single	Executive
55	1	14	2	3	3	3	1	3	1	1	29,756	Self Enquiry	Small Business	Female	Super Deluxe	Married	AVP
43	1	27	3	3	3	1	0	4	0	2	17,258	Company Invited	Small Business	Male	Basic	Divorced	Executive
43	3	15	3	4	4	2	0	3	1	1	25,503	Self Enquiry	Small Business	Male	Deluxe	Married	Manager
38	3	8	2	4	3	4	0	5	1	1	20,249	Company Invited	Salaried	Male	Deluxe	Divorced	Manager
37	1	15	3	4	3	3	0	4	1	2	28,774	Self Enquiry	Large Business	Female	Standard	Married	Senior Manager
35	1	7	3	5	3	3	1	2	0	1	22,300	Self Enquiry	Salaried	Male	Basic	Divorced	Executive
39	2	9	4	4	4	7	0	3	1	3	21,270	Company Invited	Salaried	Male	Basic	Married	Executive
21	3	6	3	4	4	2	1	5	1	2	17,174	Company Invited	Large Business	Female	Basic	Single	Executive
30	3	15	2	3	3	2	0	5	0	0	16,081	Self Enquiry	Small Business	Male	Basic	Single	Executive
23	1	26	4	4	3	3	0	1	1	1	21,001	Self Enquiry	Small Business	Male	Basic	Married	Executive
25	1	25	3	4	3	2	0	4	1	1	21,452	Self Enquiry	Salaried	Male	Basic	Divorced	Executive
39	1	16	3	3	5	3	0	5	1	2	20,377	Self Enquiry	Small Business	Male	Deluxe	Married	Manager
40	1	10	2	3	3	2	0	5	0	1	34,033	Self Enquiry	Small Business	Female	King	Divorced	VP
41	3	6	2	1	5	2	0	3	1	1	23,392	Self Enquiry	Small Business	Male	Standard	Married	Senior Manager
24	1	11	3	4	4	3	0	3	1	2	21,973	Self Enquiry	Small Business	Female	Basic	Single	Executive
53	1	15	3	5	4	4	0	1	1	1	23,619	Self Enquiry	Small Business	Male	Deluxe	Married	Manager
37	3	10	3	5	3	3	1	1	1	1	29,003	Self Enquiry	Salaried	Male	Standard	Married	Senior Manager
31	1	11	3	4	3	20	1	4	1	2	20,963	Company Invited	Large Business	Male	Basic	Single	Executive
50	1	8	3	3	3	3	1	1	1	2	34,237	Self Enquiry	Small Business	Male	King	Married	VP
26	1	31	2	5	3	2	0	1	0	0	17,293	Self Enquiry	Salaried	Male	Basic	Single	Executive
51	1	27	3	4	3	4	1	4	1	2	29,923	Company Invited	Small Business	Male	Super Deluxe	Married	AVP
34	3	8	2	3	3	2	0	5	0	0	21,274	Self Enquiry	Salaried	Male	Deluxe	Single	Manager
41	3	7	3	6	3	4	1	3	1	1	26,135	Self Enquiry	Small Business	Male	Deluxe	Divorced	Manager
32	1	18	4	4	5	3	1	1	1	2	25,511	Self Enquiry	Small Business	Male	Deluxe	Married	Manager
43	1	13	2	3	5	1	0	5	1	0	24,985	Self Enquiry	Salaried	Female	Standard	Divorced	Senior Manager
54	3	7	3	4	5	2	0	1	1	2	27,059	Self Enquiry	Small Business	Female	Deluxe	Single	Manager
32	1	11	3	2	4	1	1	2	1	0	18,298	Self Enquiry	Salaried	Male	Basic	Married	Executive
25	3	7	4	4	4	3	1	4	0	1	21,880	Self Enquiry	Large Business	Female	Basic	Single	Executive
37	1	13	3	4	3	2	0	5	1	2	21,888	Self Enquiry	Salaried	Male	Basic	Divorced	Executive
61	3	23	3	4	5	2	0	4	1	1	24,083	Self Enquiry	Small Business	Male	Deluxe	Single	Manager
36	1	33	3	3	3	7	0	3	1	0	20,237	Self Enquiry	Small Business	Male	Deluxe	Divorced	Manager
26	1	13	2	4	5	1	1	4	1	1	17,875	Self Enquiry	Small Business	Female	Standard	Married	Senior Manager
55	1	8	3	3	4	4	0	1	0	1	25,976	Company Invited	Salaried	Male	Standard	Married	Senior Manager
42	3	32	3	3	4	6	0	3	1	1	28,525	Company Invited	Small Business	Female	Super Deluxe	Married	AVP
27	3	8	2	1	3	1	0	1	0	1	21,500	Self Enquiry	Small Business	Female	Deluxe	Single	Manager
46	1	6	3	3	5	1	0	2	0	0	24,396	Self Enquiry	Small Business	Male	Standard	Married	Senior Manager
39	1	17	4	4	3	5	0	3	0	2	28,502	Self Enquiry	Small Business	Female	Deluxe	Married	Manager
39	1	17	3	6	3	5	0	1	1	2	31,884	Self Enquiry	Small Business	Female	Standard	Married	Senior Manager
38	2	13	4	4	5	6	1	2	1	1	20,751	Self Enquiry	Salaried	Male	Basic	Married	Executive
35	1	7	3	4	3	2	0	4	1	1	24,162	Self Enquiry	Salaried	Male	Deluxe	Married	Manager
39	1	7	3	2	3	2	0	1	0	1	26,303	Self Enquiry	Salaried	Female	Deluxe	Single	Manager
27	3	30	3	5	3	2	1	2	1	1	22,835	Self Enquiry	Small Business	Female	Deluxe	Divorced	Manager
39	1	28	2	3	5	2	1	5	1	0	25,880	Company Invited	Small Business	Female	Standard	Single	Senior Manager
23	1	12	3	1	5	2	1	3	0	0	16,601	Self Enquiry	Salaried	Male	Basic	Married	Executive
31	3	29	4	4	5	2	0	1	1	2	27,090	Company Invited	Salaried	Female	Standard	Married	Senior Manager
31	1	29	3	4	3	6	1	2	0	2	20,810	Self Enquiry	Salaried	Female	Basic	Divorced	Executive
35	3	16	2	4	5	1	0	5	1	1	25,306	Self Enquiry	Small Business	Male	Standard	Married	Senior Manager
31	1	6	3	1	4	2	0	3	1	2	22,446	Company Invited	Salaried	Female	Standard	Single	Senior Manager
38	1	16	2	5	3	4	0	1	1	1	28,206	Self Enquiry	Small Business	Female	Standard	Married	Senior Manager
30	1	10	2	3	3	19	1	4	1	1	17,285	Company Invited	Large Business	Male	Basic	Single	Executive
34	3	32	3	5	4	4	1	5	1	1	27,058	Self Enquiry	Small Business	Male	Standard	Single	Senior Manager
54	3	13	3	4	3	4	1	5	0	2	20,984	Self Enquiry	Small Business	Male	Deluxe	Married	Manager
23	3	13	2	3	3	2	1	1	1	0	17,275	Self Enquiry	Salaried	Male	Basic	Married	Executive
24	1	17	3	2	3	4	0	1	1	1	20,751	Self Enquiry	Large Business	Male	Basic	Married	Executive
46	3	27	3	4	3	2	0	1	1	1	23,528	Self Enquiry	Salaried	Female	Deluxe	Single	Manager

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