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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 2 new columns ({'demo_age_5_17', 'demo_age_17_'}) and 2 missing columns ({'bio_age_5_17', 'bio_age_17_'}).

This happened while the csv dataset builder was generating data using

hf://datasets/gani2004/data/datahackthon/api_data_aadhar_demographic/api_data_aadhar_demographic/api_data_aadhar_demographic_0_500000.csv (at revision c4eb8776c433889b0f6d23d97bdee24f9ca4ec44)

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
              date: string
              state: string
              district: string
              pincode: int64
              demo_age_5_17: int64
              demo_age_17_: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 952
              to
              {'date': Value('string'), 'state': Value('string'), 'district': Value('string'), 'pincode': Value('int64'), 'bio_age_5_17': Value('int64'), 'bio_age_17_': 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 2 new columns ({'demo_age_5_17', 'demo_age_17_'}) and 2 missing columns ({'bio_age_5_17', 'bio_age_17_'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/gani2004/data/datahackthon/api_data_aadhar_demographic/api_data_aadhar_demographic/api_data_aadhar_demographic_0_500000.csv (at revision c4eb8776c433889b0f6d23d97bdee24f9ca4ec44)
              
              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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date
string
state
string
district
string
pincode
int64
bio_age_5_17
int64
bio_age_17_
int64
01-03-2025
Haryana
Mahendragarh
123,029
280
577
01-03-2025
Bihar
Madhepura
852,121
144
369
01-03-2025
Jammu and Kashmir
Punch
185,101
643
1,091
01-03-2025
Bihar
Bhojpur
802,158
256
980
01-03-2025
Tamil Nadu
Madurai
625,514
271
815
01-03-2025
Maharashtra
Ratnagiri
416,702
155
529
01-03-2025
Gujarat
Anand
388,130
75
143
01-03-2025
Gujarat
Gandhinagar
382,421
192
298
01-03-2025
Odisha
Dhenkanal
759,025
122
214
01-03-2025
Gujarat
Valsad
396,055
67
85
01-03-2025
Tamil Nadu
Salem
636,119
103
63
01-03-2025
West Bengal
Hooghly
712,414
91
97
01-03-2025
West Bengal
Paschim Medinipur
721,147
86
116
01-03-2025
Kerala
Wayanad
670,721
75
293
01-03-2025
Rajasthan
Sawai Madhopur
322,027
108
300
01-03-2025
Bihar
Vaishali
844,504
426
958
01-03-2025
Tamil Nadu
Karur
639,105
131
103
01-03-2025
Punjab
Rupnagar
140,118
38
97
01-03-2025
Gujarat
Sabarkantha
383,440
56
91
01-03-2025
Himachal Pradesh
Una
177,039
24
30
01-03-2025
Rajasthan
Bhilwara
311,805
52
64
01-03-2025
Uttar Pradesh
Bara Banki
225,408
17
3
01-03-2025
Assam
Cachar
788,106
21
24
01-03-2025
Uttarakhand
Dehradun
248,146
13
17
01-03-2025
Himachal Pradesh
Chamba
176,302
83
101
01-03-2025
Madhya Pradesh
Shajapur
465,339
234
153
01-03-2025
Tamil Nadu
The Nilgiris
643,231
53
59
01-03-2025
Maharashtra
Wardha
442,101
269
538
01-03-2025
Odisha
Nabarangapur
764,075
438
605
01-03-2025
Punjab
Shaheed Bhagat Singh Nagar
144,514
134
249
01-03-2025
Karnataka
Davangere
577,002
219
386
01-03-2025
Gujarat
Dahod
389,382
341
569
01-03-2025
Tamil Nadu
Tiruppur
641,654
307
415
01-03-2025
Haryana
Mahendragarh
123,001
454
730
01-03-2025
Andhra Pradesh
Vizianagaram
535,592
77
72
01-03-2025
Punjab
Moga
142,041
69
138
01-03-2025
Gujarat
Rajkot
363,630
17
79
01-03-2025
Telangana
Hyderabad
500,013
581
654
01-03-2025
Odisha
Sundergarh
769,004
152
186
01-03-2025
Andhra Pradesh
Kurnool
518,385
291
138
01-03-2025
Kerala
Wayanad
673,121
130
225
01-03-2025
Andhra Pradesh
Warangal
506,163
39
92
01-03-2025
Tamil Nadu
Tirunelveli
627,108
238
435
01-03-2025
Goa
South Goa
403,601
183
188
01-03-2025
Andhra Pradesh
Anantapur
515,301
83
105
01-03-2025
Kerala
Thiruvananthapuram
695,303
71
78
01-03-2025
Andhra Pradesh
Srikakulam
532,459
362
161
01-03-2025
Telangana
Nizamabad
503,164
399
120
01-03-2025
Odisha
Cuttack
753,003
40
112
01-03-2025
Rajasthan
Baran
325,216
438
580
01-03-2025
Andhra Pradesh
Guntur
522,329
126
55
01-03-2025
Karnataka
Mysuru
571,602
117
131
01-03-2025
West Bengal
Purba Medinipur
721,441
49
53
01-03-2025
Kerala
Ernakulam
682,508
48
64
01-03-2025
Madhya Pradesh
Balaghat
481,335
328
401
01-03-2025
Tamil Nadu
Sivaganga
630,106
59
58
01-03-2025
Bihar
Bhojpur
802,164
165
311
01-03-2025
Tamil Nadu
Tirunelveli
627,761
46
36
01-03-2025
Madhya Pradesh
Harda *
461,441
30
143
01-03-2025
Nagaland
Mokokchung
798,613
18
70
01-03-2025
Karnataka
Uttara Kannada
581,344
15
7
01-03-2025
Tamil Nadu
Perambalur
621,113
198
80
01-03-2025
Karnataka
Udupi
574,114
16
16
01-03-2025
Andhra Pradesh
Ananthapur
515,261
58
11
01-03-2025
Tamil Nadu
Coimbatore
642,104
55
55
01-03-2025
Jharkhand
West Singhbhum
833,213
93
70
01-03-2025
Karnataka
Tumakuru
572,225
16
3
01-03-2025
West Bengal
Birbhum
731,244
46
52
01-03-2025
Andhra Pradesh
Visakhapatnam
530,045
50
134
01-03-2025
Uttar Pradesh
Hardoi
241,406
803
216
01-03-2025
Uttar Pradesh
Siddharthnagar
272,205
857
272
01-03-2025
Jharkhand
Dhanbad
828,304
44
118
01-03-2025
Bihar
Darbhanga
846,003
438
859
01-03-2025
Madhya Pradesh
Jabalpur
482,002
458
678
01-03-2025
West Bengal
North 24 Parganas
743,145
127
234
01-03-2025
Uttar Pradesh
Hardoi
241,304
1,025
328
01-03-2025
Karnataka
Tumkur
572,175
25
56
01-03-2025
Jammu and Kashmir
Leh
194,401
52
43
01-03-2025
Andhra Pradesh
West Godavari
534,316
134
73
01-03-2025
Haryana
Kaithal
136,044
235
368
01-03-2025
Gujarat
Kachchh
370,140
392
286
01-03-2025
Andhra Pradesh
Krishna
521,401
109
125
01-03-2025
Karnataka
Davangere
577,001
318
517
01-03-2025
Kerala
Kannur
670,633
22
52
01-03-2025
Karnataka
Shivamogga
577,401
62
50
01-03-2025
West Bengal
Bankura
722,151
178
160
01-03-2025
Rajasthan
Rajsamand
313,331
454
345
01-03-2025
Kerala
Thiruvananthapuram
695,582
33
90
01-03-2025
Assam
Cachar
788,123
15
21
01-03-2025
Karnataka
Davangere
577,003
39
87
01-03-2025
Tamil Nadu
Namakkal
636,203
49
51
01-03-2025
Tamil Nadu
Thiruvarur
614,704
174
105
01-03-2025
Telangana
Karimnagar
505,331
68
77
01-03-2025
Assam
South Salmara Mankachar
783,131
63
49
01-03-2025
Himachal Pradesh
Shimla
171,201
42
54
01-03-2025
Telangana
Nalgonda
508,210
69
64
01-03-2025
Bihar
Saharsa
852,210
58
118
01-03-2025
Tamil Nadu
Kanniyakumari
629,165
40
115
01-03-2025
Telangana
Medak
502,280
32
46
01-03-2025
Telangana
Warangal
506,015
31
29
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