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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 22 new columns ({'Year', 'Longitude', 'Ward', 'Description', 'District', 'Y Coordinate', 'Location Description', 'Primary Type', 'Beat', 'Updated On', 'Community Area', 'ID', 'Block', 'Date', 'Case Number', 'Domestic', 'X Coordinate', 'FBI Code', 'IUCR', 'Latitude', 'Location', 'Arrest'}) and 9 missing columns ({'PERCENT OF HOUSING CROWDED', 'PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA', 'COMMUNITY AREA NAME', 'Community Area Number', 'PER CAPITA INCOME ', 'PERCENT AGED UNDER 18 OR OVER 64', 'HARDSHIP INDEX', 'PERCENT HOUSEHOLDS BELOW POVERTY', 'PERCENT AGED 16+ UNEMPLOYED'}).

This happened while the csv dataset builder was generating data using

zip://Dataset/Crimes_-_2001_to_Present_20260712.csv::hf://datasets/annghoang/chicago-smartcity-analytics@8286c561a52e5f921d017527a326f7036cabad37/dataset.zip, ['hf://datasets/annghoang/chicago-smartcity-analytics@8286c561a52e5f921d017527a326f7036cabad37/dataset.zip', 'hf://datasets/annghoang/chicago-smartcity-analytics@8286c561a52e5f921d017527a326f7036cabad37/models.zip']

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/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              ID: int64
              Case Number: string
              Date: string
              Block: string
              IUCR: string
              Primary Type: string
              Description: string
              Location Description: string
              Arrest: bool
              Domestic: bool
              Beat: int64
              District: int64
              Ward: int64
              Community Area: int64
              FBI Code: string
              X Coordinate: double
              Y Coordinate: double
              Year: int64
              Updated On: string
              Latitude: double
              Longitude: double
              Location: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2825
              to
              {'Community Area Number': Value('float64'), 'COMMUNITY AREA NAME': Value('string'), 'PERCENT OF HOUSING CROWDED': Value('float64'), 'PERCENT HOUSEHOLDS BELOW POVERTY': Value('float64'), 'PERCENT AGED 16+ UNEMPLOYED': Value('float64'), 'PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA': Value('float64'), 'PERCENT AGED UNDER 18 OR OVER 64': Value('float64'), 'PER CAPITA INCOME ': Value('int64'), 'HARDSHIP INDEX': 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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 22 new columns ({'Year', 'Longitude', 'Ward', 'Description', 'District', 'Y Coordinate', 'Location Description', 'Primary Type', 'Beat', 'Updated On', 'Community Area', 'ID', 'Block', 'Date', 'Case Number', 'Domestic', 'X Coordinate', 'FBI Code', 'IUCR', 'Latitude', 'Location', 'Arrest'}) and 9 missing columns ({'PERCENT OF HOUSING CROWDED', 'PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA', 'COMMUNITY AREA NAME', 'Community Area Number', 'PER CAPITA INCOME ', 'PERCENT AGED UNDER 18 OR OVER 64', 'HARDSHIP INDEX', 'PERCENT HOUSEHOLDS BELOW POVERTY', 'PERCENT AGED 16+ UNEMPLOYED'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://Dataset/Crimes_-_2001_to_Present_20260712.csv::hf://datasets/annghoang/chicago-smartcity-analytics@8286c561a52e5f921d017527a326f7036cabad37/dataset.zip, ['hf://datasets/annghoang/chicago-smartcity-analytics@8286c561a52e5f921d017527a326f7036cabad37/dataset.zip', 'hf://datasets/annghoang/chicago-smartcity-analytics@8286c561a52e5f921d017527a326f7036cabad37/models.zip']
              
              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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Community Area Number float64	COMMUNITY AREA NAME string	PERCENT OF HOUSING CROWDED float64	PERCENT HOUSEHOLDS BELOW POVERTY float64	PERCENT AGED 16+ UNEMPLOYED float64	PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA float64	PERCENT AGED UNDER 18 OR OVER 64 float64	PER CAPITA INCOME int64	HARDSHIP INDEX float64
1	Rogers Park	7.7	23.6	8.7	18.2	27.5	23,939	39
2	West Ridge	7.8	17.2	8.8	20.8	38.5	23,040	46
3	Uptown	3.8	24	8.9	11.8	22.2	35,787	20
4	Lincoln Square	3.4	10.9	8.2	13.4	25.5	37,524	17
5	North Center	0.3	7.5	5.2	4.5	26.2	57,123	6
6	Lake View	1.1	11.4	4.7	2.6	17	60,058	5
7	Lincoln Park	0.8	12.3	5.1	3.6	21.5	71,551	2
8	Near North Side	1.9	12.9	7	2.5	22.6	88,669	1
9	Edison Park	1.1	3.3	6.5	7.4	35.3	40,959	8
10	Norwood Park	2	5.4	9	11.5	39.5	32,875	21
11	Jefferson Park	2.7	8.6	12.4	13.4	35.5	27,751	25
12	Forest Glen	1.1	7.5	6.8	4.9	40.5	44,164	11
13	North Park	3.9	13.2	9.9	14.4	39	26,576	33
14	Albany Park	11.3	19.2	10	32.9	32	21,323	53
15	Portage Park	4.1	11.6	12.6	19.3	34	24,336	35
16	Irving Park	6.3	13.1	10	22.4	31.6	27,249	34
17	Dunning	5.2	10.6	10	16.2	33.6	26,282	28
18	Montclaire	8.1	15.3	13.8	23.5	38.6	22,014	50
19	Belmont Cragin	10.8	18.7	14.6	37.3	37.3	15,461	70
20	Hermosa	6.9	20.5	13.1	41.6	36.4	15,089	71
21	Avondale	6	15.3	9.2	24.7	31	20,039	42
22	Logan Square	3.2	16.8	8.2	14.8	26.2	31,908	23
23	Humboldt park	14.8	33.9	17.3	35.4	38	13,781	85
24	West Town	2.3	14.7	6.6	12.9	21.7	43,198	10
25	Austin	6.3	28.6	22.6	24.4	37.9	15,957	73
26	West Garfield Park	9.4	41.7	25.8	24.5	43.6	10,934	92
27	East Garfield Park	8.2	42.4	19.6	21.3	43.2	12,961	83
28	Near West Side	3.8	20.6	10.7	9.6	22.2	44,689	15
29	North Lawndale	7.4	43.1	21.2	27.6	42.7	12,034	87
30	South Lawndale	15.2	30.7	15.8	54.8	33.8	10,402	96
31	Lower West Side	9.6	25.8	15.8	40.7	32.6	16,444	76
32	Loop	1.5	14.7	5.7	3.1	13.5	65,526	3
33	Near South Side	1.3	13.8	4.9	7.4	21.8	59,077	7
34	Armour Square	5.7	40.1	16.7	34.5	38.3	16,148	82
35	Douglas	1.8	29.6	18.2	14.3	30.7	23,791	47
36	Oakland	1.3	39.7	28.7	18.4	40.4	19,252	78
37	Fuller Park	3.2	51.2	33.9	26.6	44.9	10,432	97
38	Grand Boulevard	3.3	29.3	24.3	15.9	39.5	23,472	57
39	Kenwood	2.4	21.7	15.7	11.3	35.4	35,911	26
40	Washington Park	5.6	42.1	28.6	25.4	42.8	13,785	88
41	Hyde Park	1.5	18.4	8.4	4.3	26.2	39,056	14
42	Woodlawn	2.9	30.7	23.4	16.5	36.1	18,672	58
43	South Shore	2.8	31.1	20	14	35.7	19,398	55
44	Chatham	3.3	27.8	24	14.5	40.3	18,881	60
45	Avalon Park	1.4	17.2	21.1	10.6	39.3	24,454	41
46	South Chicago	4.7	29.8	19.7	26.6	41.1	16,579	75
47	Burnside	6.8	33	18.6	19.3	42.7	12,515	79
48	Calumet Heights	2.1	11.5	20	11	44	28,887	38
49	Roseland	2.5	19.8	20.3	16.9	41.2	17,949	52
50	Pullman	1.5	21.6	22.8	13.1	38.6	20,588	51
51	South Deering	4	29.2	16.3	21	39.5	14,685	65
52	East Side	6.8	19.2	12.1	31.9	42.8	17,104	64
53	West Pullman	3.3	25.9	19.4	20.5	42.1	16,563	62
54	Riverdale	5.8	56.5	34.6	27.5	51.5	8,201	98
55	Hegewisch	3.3	17.1	9.6	19.2	42.9	22,677	44
56	Garfield Ridge	2.6	8.8	11.3	19.3	38.1	26,353	32
57	Archer Heights	8.5	14.1	16.5	35.9	39.2	16,134	67
58	Brighton Park	14.4	23.6	13.9	45.1	39.3	13,089	84
59	McKinley Park	7.2	18.7	13.4	32.9	35.6	16,954	61
60	Bridgeport	4.5	18.9	13.7	22.2	31.3	22,694	43
61	New City	11.9	29	23	41.5	38.9	12,765	91
62	West Elsdon	11.1	15.6	16.7	37	37.7	15,754	69
63	Gage Park	15.8	23.4	18.2	51.5	38.8	12,171	93
64	Clearing	2.7	8.9	9.5	18.8	37.6	25,113	29
65	West Lawn	5.8	14.9	9.6	33.6	39.6	16,907	56
66	Chicago Lawn	7.6	27.9	17.1	31.2	40.6	13,231	80
67	West Englewood	4.8	34.4	35.9	26.3	40.7	11,317	89
68	Englewood	3.8	46.6	28	28.5	42.5	11,888	94
69	Greater Grand Crossing	3.6	29.6	23	16.5	41	17,285	66
70	Ashburn	4	10.4	11.7	17.7	36.9	23,482	37
71	Auburn Gresham	4	27.6	28.3	18.5	41.9	15,528	74
72	Beverly	0.9	5.1	8	3.7	40.5	39,523	12
73	Washington Height	1.1	16.9	20.8	13.7	42.6	19,713	48
74	Mount Greenwood	1	3.4	8.7	4.3	36.8	34,381	16
75	Morgan Park	0.8	13.2	15	10.8	40.3	27,149	30
76	O'Hare	3.6	15.4	7.1	10.9	30.3	25,828	24
77	Edgewater	4.1	18.2	9.2	9.7	23.8	33,385	19
null	CHICAGO	4.7	19.7	12.9	19.5	33.5	28,202	null
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