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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 ({'Unnamed: 0'}) and 1 missing columns ({'ID'}).

This happened while the csv dataset builder was generating data using

hf://datasets/prashant91/wellness-tourism-raw-data/tourism_data.csv (at revision d6e9ea3f823edf970331a1f6683aa855f501bd3d)

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
              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
              {'ID': Value('int64'), 'CustomerID': Value('int64'), 'ProdTaken': Value('int64'), 'Age': Value('float64'), 'TypeofContact': Value('string'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('float64'), 'Designation': Value('string'), 'MonthlyIncome': Value('float64')}
              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 ({'Unnamed: 0'}) and 1 missing columns ({'ID'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/prashant91/wellness-tourism-raw-data/tourism_data.csv (at revision d6e9ea3f823edf970331a1f6683aa855f501bd3d)
              
              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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ID int64	CustomerID int64	ProdTaken int64	Age float64	TypeofContact string	CityTier int64	DurationOfPitch float64	Occupation string	Gender string	NumberOfPersonVisiting int64	NumberOfFollowups float64	ProductPitched string	PreferredPropertyStar float64	MaritalStatus string	NumberOfTrips float64	Passport int64	PitchSatisfactionScore int64	OwnCar int64	NumberOfChildrenVisiting float64	Designation string	MonthlyIncome float64
0	200,000	1	41	Self Enquiry	3	6	Salaried	Female	3	3	Deluxe	3	Single	1	1	2	1	0	Manager	20,993
1	200,001	0	49	Company Invited	1	14	Salaried	Male	3	4	Deluxe	4	Divorced	2	0	3	1	2	Manager	20,130
2	200,002	1	37	Self Enquiry	1	8	Free Lancer	Male	3	4	Basic	3	Single	7	1	3	0	0	Executive	17,090
3	200,003	0	33	Company Invited	1	9	Salaried	Female	2	3	Basic	3	Divorced	2	1	5	1	1	Executive	17,909
5	200,005	0	32	Company Invited	1	8	Salaried	Male	3	3	Basic	3	Single	1	0	5	1	1	Executive	18,068
6	200,006	0	59	Self Enquiry	1	9	Small Business	Female	2	2	Basic	5	Divorced	5	1	2	1	1	Executive	17,670
7	200,007	0	30	Self Enquiry	1	30	Salaried	Male	3	3	Basic	3	Married	2	0	2	0	1	Executive	17,693
8	200,008	0	38	Company Invited	1	29	Salaried	Male	2	4	Standard	3	Unmarried	1	0	3	0	0	Senior Manager	24,526
9	200,009	0	36	Self Enquiry	1	33	Small Business	Male	3	3	Deluxe	3	Divorced	7	0	3	1	0	Manager	20,237
10	200,010	0	35	Self Enquiry	1	22	Small Business	Male	2	2	Basic	4	Divorced	1	0	3	1	1	Executive	17,426
12	200,012	0	31	Self Enquiry	1	32	Salaried	Male	2	3	Basic	3	Married	2	0	3	0	1	Executive	17,911
13	200,013	0	34	Self Enquiry	1	25	Small Business	Male	3	3	Basic	3	Married	1	0	3	0	2	Executive	17,661
14	200,014	1	28	Self Enquiry	1	30	Salaried	Male	2	4	Basic	3	Single	6	1	2	0	0	Executive	17,028
15	200,015	0	29	Self Enquiry	1	27	Salaried	Female	2	2	Standard	5	Married	2	0	5	1	1	Senior Manager	24,980
16	200,016	0	32	Self Enquiry	1	11	Salaried	Male	3	2	Basic	4	Married	1	1	2	1	0	Executive	18,298
17	200,017	0	22	Company Invited	1	22	Small Business	Male	3	2	Basic	3	Married	2	1	3	0	0	Executive	17,935
18	200,018	0	53	Self Enquiry	3	8	Salaried	Female	3	4	Super Deluxe	3	Divorced	3	0	3	1	0	AVP	30,427
22	200,022	0	34	Self Enquiry	1	13	Salaried	Fe Male	2	3	Standard	4	Unmarried	1	0	3	1	0	Senior Manager	26,994
23	200,023	0	21	Self Enquiry	1	21	Salaried	Male	3	3	Basic	3	Single	2	0	3	1	1	Executive	16,232
24	200,024	1	34	Self Enquiry	1	12	Small Business	Male	2	3	Basic	5	Single	3	0	2	1	1	Executive	17,960
25	200,025	0	53	Self Enquiry	1	11	Salaried	Female	2	3	King	3	Married	5	0	5	0	1	VP	34,094
27	200,027	0	42	Self Enquiry	3	14	Small Business	Male	2	3	Deluxe	4	Divorced	1	0	3	0	1	Manager	21,825
28	200,028	0	44	Self Enquiry	1	13	Small Business	Female	2	4	Standard	3	Divorced	4	0	4	1	0	Senior Manager	25,248
29	200,029	0	46	Self Enquiry	3	8	Small Business	Female	2	3	King	5	Single	4	0	2	0	1	VP	33,947
30	200,030	0	33	Self Enquiry	1	8	Small Business	Male	3	3	Basic	3	Single	5	0	3	1	2	Executive	17,496
31	200,031	0	44	Self Enquiry	1	16	Salaried	Male	2	3	Deluxe	3	Divorced	3	1	3	1	1	Manager	21,465
32	200,032	0	30	Self Enquiry	1	15	Small Business	Male	2	4	Basic	3	Single	2	0	4	1	1	Executive	17,206
33	200,033	1	39	Self Enquiry	3	11	Large Business	Male	2	3	Deluxe	3	Divorced	4	0	2	0	1	Manager	17,086
34	200,034	1	24	Self Enquiry	1	6	Small Business	Male	3	3	Basic	3	Divorced	3	1	3	1	2	Executive	17,293
35	200,035	0	43	Self Enquiry	1	8	Small Business	Female	3	2	Basic	3	Married	2	0	2	0	1	Executive	17,645
36	200,036	1	50	Self Enquiry	3	9	Small Business	Male	2	4	Basic	3	Divorced	2	1	4	0	0	Executive	17,683
37	200,037	0	35	Self Enquiry	3	8	Small Business	Female	3	3	Basic	3	Divorced	2	0	2	1	0	Executive	17,014
39	200,039	0	33	Company Invited	3	6	Salaried	Female	2	2	Deluxe	5	Divorced	3	0	3	1	0	Manager	20,376
40	200,040	0	35	Self Enquiry	1	10	Salaried	Male	3	3	Basic	3	Married	2	0	4	0	0	Executive	16,951
41	200,041	0	27	Self Enquiry	1	8	Salaried	Female	2	3	Basic	5	Married	2	0	3	0	1	Executive	17,341
42	200,042	1	26	Self Enquiry	1	31	Salaried	Male	2	5	Basic	3	Single	2	0	2	1	1	Executive	17,293
43	200,043	0	27	Company Invited	3	14	Salaried	Male	2	3	Standard	3	Unmarried	2	0	2	1	0	Senior Manager	23,726
45	200,045	1	41	Self Enquiry	1	18	Large Business	Female	2	3	King	3	Divorced	2	0	4	1	0	VP	34,545
47	200,047	0	37	Self Enquiry	1	25	Salaried	Male	3	3	Basic	4	Divorced	5	0	4	1	1	Executive	18,022
48	200,048	0	46	Company Invited	3	11	Small Business	Male	3	3	Deluxe	3	Single	5	1	5	1	1	Manager	20,772
49	200,049	0	35	Self Enquiry	1	14	Salaried	Male	2	2	Basic	3	Divorced	2	0	5	1	1	Executive	17,269
50	200,050	1	48	Self Enquiry	1	6	Salaried	Male	3	4	Standard	3	Single	1	1	5	0	2	Senior Manager	20,381
52	200,052	0	44	Self Enquiry	3	6	Small Business	Female	3	3	Deluxe	5	Married	6	1	2	0	0	Manager	20,454
53	200,053	0	35	Company Invited	1	17	Small Business	Male	3	4	Standard	5	Divorced	3	1	5	1	1	Senior Manager	24,884
55	200,055	0	33	Company Invited	1	6	Salaried	Fe Male	3	3	Standard	3	Unmarried	2	1	2	1	0	Senior Manager	28,458
56	200,056	0	35	Company Invited	3	24	Salaried	Male	3	3	Standard	5	Divorced	2	0	4	1	0	Senior Manager	24,069
58	200,058	0	31	Self Enquiry	1	13	Salaried	Male	2	3	Deluxe	3	Married	4	0	3	1	0	Manager	20,915
59	200,059	0	37	Self Enquiry	1	6	Salaried	Male	2	4	Deluxe	3	Married	2	0	5	1	1	Manager	20,993
60	200,060	0	32	Self Enquiry	1	6	Small Business	Male	2	3	Deluxe	3	Divorced	2	1	4	1	0	Manager	21,162
61	200,061	0	38	Company Invited	1	35	Salaried	Female	2	3	Deluxe	3	Single	2	0	3	1	0	Manager	17,406
62	200,062	0	50	Self Enquiry	1	13	Small Business	Female	2	4	King	3	Married	6	1	4	1	1	VP	33,740
63	200,063	0	59	Self Enquiry	3	31	Salaried	Female	2	3	Standard	5	Unmarried	1	0	3	1	0	Senior Manager	22,637
64	200,064	0	36	Self Enquiry	1	14	Large Business	Female	2	3	Standard	3	Married	2	0	4	1	1	Senior Manager	25,096
65	200,065	0	55	Self Enquiry	1	14	Small Business	Female	2	3	Super Deluxe	3	Married	3	1	3	0	1	AVP	29,756
66	200,066	0	36	Company Invited	1	17	Salaried	Male	3	4	Deluxe	4	Unmarried	2	0	4	1	1	Manager	21,499
67	200,067	0	45	Self Enquiry	1	13	Salaried	Male	2	3	Standard	5	Married	3	0	3	1	1	Senior Manager	24,724
68	200,068	0	35	Company Invited	1	6	Small Business	Male	3	3	Basic	5	Divorced	5	0	2	0	1	Executive	17,194
70	200,070	0	59	Company Invited	3	6	Salaried	Female	2	4	Deluxe	3	Single	1	0	5	1	1	Manager	20,473
71	200,071	0	29	Self Enquiry	1	8	Salaried	Male	3	3	Basic	4	Divorced	1	0	4	0	0	Executive	17,703
72	200,072	0	31	Self Enquiry	1	6	Small Business	Male	2	3	Basic	4	Single	2	0	3	0	0	Executive	17,501
73	200,073	0	32	Self Enquiry	1	6	Salaried	Male	3	3	Deluxe	4	Divorced	2	0	3	0	0	Manager	21,220
74	200,074	0	36	Self Enquiry	1	12	Salaried	Female	2	2	Deluxe	3	Divorced	4	0	2	0	0	Manager	18,038
77	200,077	0	45	Self Enquiry	1	12	Salaried	Male	2	3	Standard	5	Divorced	5	1	3	0	0	Senior Manager	28,245
78	200,078	0	37	Self Enquiry	1	13	Small Business	Male	1	3	Standard	3	Single	5	0	2	0	0	Senior Manager	28,664
80	200,080	0	30	Self Enquiry	1	6	Salaried	Male	2	4	Deluxe	3	Divorced	2	1	2	1	1	Manager	20,126
81	200,081	0	35	Self Enquiry	1	6	Small Business	Male	1	4	Basic	3	Single	2	0	4	1	0	Executive	17,859
82	200,082	0	55	Self Enquiry	3	6	Salaried	Male	3	3	Standard	3	Divorced	4	0	2	0	1	Senior Manager	25,239
83	200,083	0	38	Company Invited	1	12	Small Business	Female	3	5	Deluxe	3	Married	1	1	2	0	2	Manager	20,329
85	200,085	0	56	Self Enquiry	1	13	Salaried	Male	2	5	Standard	3	Unmarried	5	0	2	0	0	Senior Manager	22,260
86	200,086	0	23	Self Enquiry	3	8	Large Business	Male	3	3	Basic	5	Married	4	0	3	1	1	Executive	17,322
87	200,087	0	51	Self Enquiry	1	15	Salaried	Male	3	3	Basic	3	Divorced	4	0	3	1	0	Executive	17,075
89	200,089	1	46	Self Enquiry	3	15	Small Business	Male	3	4	Standard	3	Unmarried	2	0	5	0	1	Senior Manager	24,619
90	200,090	0	40	Company Invited	1	6	Salaried	Male	3	4	Super Deluxe	4	Divorced	2	0	2	1	2	AVP	28,503
91	200,091	0	51	Self Enquiry	3	27	Small Business	Male	3	3	Deluxe	3	Single	1	1	5	1	1	Manager	20,441
92	200,092	0	30	Self Enquiry	3	10	Small Business	Female	3	4	Deluxe	4	Married	2	1	3	1	1	Manager	20,209
93	200,093	0	46	Company Invited	1	6	Small Business	Male	2	4	Standard	5	Divorced	3	1	2	1	1	Senior Manager	25,673
95	200,095	0	54	Self Enquiry	1	8	Large Business	Female	2	3	Standard	3	Divorced	1	0	4	1	0	Senior Manager	28,549
98	200,098	0	58	Self Enquiry	3	16	Small Business	Male	2	3	Super Deluxe	3	Single	1	0	3	1	1	AVP	28,872
99	200,099	0	44	Company Invited	1	29	Small Business	Male	3	3	Deluxe	4	Divorced	5	0	3	1	0	Manager	17,042
100	200,100	1	37	Self Enquiry	2	12	Salaried	Male	3	3	Basic	5	Married	5	1	2	1	0	Executive	17,073
101	200,101	0	32	Self Enquiry	1	6	Salaried	Male	3	3	Basic	5	Single	2	0	2	0	2	Executive	17,956
102	200,102	1	20	Company Invited	1	12	Salaried	Female	3	4	Basic	3	Single	2	1	2	1	1	Executive	17,926
104	200,104	0	37	Company Invited	1	8	Salaried	Male	3	4	Deluxe	3	Married	6	0	2	0	1	Manager	20,163
105	200,105	0	59	Company Invited	2	8	Salaried	Female	2	4	King	3	Divorced	1	0	2	1	1	VP	33,844
106	200,106	0	50	Company Invited	1	6	Salaried	Female	3	3	King	4	Divorced	4	1	2	0	2	VP	33,172
107	200,107	1	25	Self Enquiry	3	11	Small Business	Male	2	4	Deluxe	3	Single	2	1	3	1	0	Manager	20,744
108	200,108	0	25	Self Enquiry	1	13	Small Business	Male	3	4	Basic	3	Divorced	2	0	2	1	2	Executive	17,889
109	200,109	0	22	Self Enquiry	1	21	Small Business	Female	2	3	Basic	3	Single	2	0	2	1	1	Executive	17,871
110	200,110	0	51	Company Invited	1	6	Small Business	Female	1	4	Standard	5	Unmarried	4	0	2	1	0	Senior Manager	22,484
111	200,111	1	34	Company Invited	1	13	Salaried	Male	3	5	Deluxe	3	Single	2	1	5	1	1	Manager	21,074
112	200,112	0	54	Company Invited	2	32	Salaried	Female	1	2	Super Deluxe	3	Single	3	1	3	1	0	AVP	32,328
113	200,113	0	24	Self Enquiry	1	24	Salaried	Male	2	3	Basic	3	Divorced	1	0	4	1	0	Executive	17,774
115	200,115	0	37	Self Enquiry	3	9	Salaried	Male	2	3	Standard	3	Unmarried	3	0	3	0	0	Senior Manager	22,428
116	200,116	0	34	Self Enquiry	1	11	Small Business	Fe Male	2	4	Standard	5	Unmarried	3	0	2	0	0	Senior Manager	26,631
117	200,117	0	36	Company Invited	3	17	Large Business	Female	3	3	Standard	3	Divorced	1	0	5	0	0	Senior Manager	24,738
118	200,118	0	36	Self Enquiry	1	9	Salaried	Female	2	3	Basic	3	Married	6	0	2	1	0	Executive	17,835
119	200,119	0	43	Company Invited	3	32	Salaried	Male	3	3	Super Deluxe	3	Divorced	2	1	2	0	0	AVP	31,959
120	200,120	0	30	Company Invited	1	29	Salaried	Male	3	5	Basic	3	Married	2	0	3	1	2	Executive	17,613
121	200,121	0	33	Company Invited	3	28	Small Business	Male	3	3	Deluxe	4	Divorced	1	0	2	0	1	Manager	21,146
123	200,123	0	51	Self Enquiry	1	12	Salaried	Male	2	3	King	3	Single	1	0	5	1	0	VP	34,537

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