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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 5 new columns ({'geolocation_zip_code_prefix', 'geolocation_lat', 'geolocation_state', 'geolocation_city', 'geolocation_lng'}) and 5 missing columns ({'customer_zip_code_prefix', 'customer_state', 'customer_id', 'customer_city', 'customer_unique_id'}).

This happened while the csv dataset builder was generating data using

hf://datasets/Keths/Said/olist_geolocation_dataset.csv (at revision 04765c9ce909701471f20244cb1b7e1c58f08137)

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 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              geolocation_zip_code_prefix: int64
              geolocation_lat: double
              geolocation_lng: double
              geolocation_city: string
              geolocation_state: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 942
              to
              {'customer_id': Value(dtype='string', id=None), 'customer_unique_id': Value(dtype='string', id=None), 'customer_zip_code_prefix': Value(dtype='int64', id=None), 'customer_city': Value(dtype='string', id=None), 'customer_state': Value(dtype='string', id=None)}
              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 1577, 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 1191, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 5 new columns ({'geolocation_zip_code_prefix', 'geolocation_lat', 'geolocation_state', 'geolocation_city', 'geolocation_lng'}) and 5 missing columns ({'customer_zip_code_prefix', 'customer_state', 'customer_id', 'customer_city', 'customer_unique_id'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Keths/Said/olist_geolocation_dataset.csv (at revision 04765c9ce909701471f20244cb1b7e1c58f08137)
              
              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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customer_id
string
customer_unique_id
string
customer_zip_code_prefix
int64
customer_city
string
customer_state
string
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14,409
franca
SP
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9,790
sao bernardo do campo
SP
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1,151
sao paulo
SP
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mogi das cruzes
SP
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campinas
SP
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89,254
jaragua do sul
SC
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sao paulo
SP
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35,182
timoteo
MG
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81,560
curitiba
PR
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30,575
belo horizonte
MG
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39,400
montes claros
MG
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20,231
rio de janeiro
RJ
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lencois paulista
SP
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5,704
sao paulo
SP
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caxias do sul
RS
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piracicaba
SP
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22,750
rio de janeiro
RJ
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7,124
guarulhos
SP
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5,416
sao paulo
SP
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68,485
pacaja
PA
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88,034
florianopolis
SC
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74,914
aparecida de goiania
GO
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5,713
sao paulo
SP
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82,820
curitiba
PR
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8,225
sao paulo
SP
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9,121
santo andre
SP
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74,310
goiania
GO
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2e6a42a9b5cbb0da62988694f18ee295
4,571
sao paulo
SP
e0eea8f69a457b3f1fa246e44c9ebefd
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29,311
cachoeiro de itapemirim
ES
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5,528
sao paulo
SP
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12,235
sao jose dos campos
SP
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18,130
sao roque
SP
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42,800
camacari
BA
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27,525
resende
RJ
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81,750
curitiba
PR
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13,175
sumare
SP
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7,170
guarulhos
SP
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93,415
novo hamburgo
RS
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65,075
sao luis
MA
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88,104
sao jose
SC
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7,176
guarulhos
SP
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35,960
santa barbara
MG
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5,727
sao paulo
SP
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guarulhos
SP
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14,026
ribeirao preto
SP
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30,320
belo horizonte
MG
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38,300
ituiutaba
MG
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18,740
taquarituba
SP
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83,085
sao jose dos pinhais
PR
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89,254
jaragua do sul
SC
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5,351
sao paulo
SP
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39,406
montes claros
MG
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14,860
barrinha
SP
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21,310
rio de janeiro
RJ
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23,970
parati
RJ
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3af0b2f7654f613ff1527b997a2ac57e
79,804
dourados
MS
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5,017
sao paulo
SP
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75,388
trindade
GO
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85,808
cascavel
PR
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60,140
fortaleza
CE
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72,270
brasilia
DF
03f846ad03437d864a8d2a22976dcafe
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2,075
sao paulo
SP
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96,015
pelotas
RS
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90,010
porto alegre
RS
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22,440
rio de janeiro
RJ
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13,323
salto
SP
26acee41e2f75689a5615892f06ea0bd
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30,190
belo horizonte
MG
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13,212
jundiai
SP
7ab7a537b678b6dd73d825ff6ee7be9d
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29,307
cachoeiro de itapemirim
ES
7300450cedf7e4c35c243c4a03c1e8a6
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12,280
cacapava
SP
4c7241af24b5344cb01fe687643de4fe
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60,336
fortaleza
CE
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11,310
sao vicente
SP
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38,408
uberlandia
MG
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37,720
botelhos
MG
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24,431
sao goncalo
RJ
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5,890
sao paulo
SP
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3,733
sao paulo
SP
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83,709
araucaria
PR
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11,347
sao vicente
SP
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26,272
nova iguacu
RJ
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5,415
sao paulo
SP
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59,655
areia branca
RN
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4,548
sao paulo
SP
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28,010
campos dos goytacazes
RJ
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13,573
sao carlos
SP
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2,175
sao paulo
SP
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37,500
itajuba
MG
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90,670
porto alegre
RS
3f6ede29d4c69cd3316d2035b6cec1fb
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9,890
sao bernardo do campo
SP
6bed27564bd99d78d09c1fac13da56fd
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13,321
salto
SP
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44,380
cruz das almas
BA
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27,700
vassouras
RJ
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44,033
feira de santana
BA
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4,537
sao paulo
SP
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b4d6e1b900d99b52e901860bc1f44e35
71,540
brasilia
DF
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809353196a0456095716566dd226bb48
13,569
sao carlos
SP
23e96758fd640560e9b1fbcda90abfc4
9e1f719fe5b17b9c51905fee6d6385c1
5,565
sao paulo
SP
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94b731a41867b47c3856e324840c4c99
3,636
sao paulo
SP
5f8b4882b5a4ec7bf6d2107e6cd0cf29
694cb45ff29b603ac2acd51016770097
24,120
niteroi
RJ
ad6891a1937cb8723a2c08ba1ae59873
9dbb05f5577e862337b93feb8f358839
65,058
sao luis
MA
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