Datasets:
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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 ({'nome'}) and 2 missing columns ({'id_producao', 'papel'}).
This happened while the json dataset builder was generating data using
hf://datasets/Willgnner-Santos/BD_Producao_Artistica/pessoa.jsonl (at revision 51d1ebde41871bd14e760d432daf05152f06413f)
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
id_pessoa: int64
nome: string
to
{'id_producao': Value('int64'), 'id_pessoa': Value('int64'), 'papel': 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 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 1 new columns ({'nome'}) and 2 missing columns ({'id_producao', 'papel'}).
This happened while the json dataset builder was generating data using
hf://datasets/Willgnner-Santos/BD_Producao_Artistica/pessoa.jsonl (at revision 51d1ebde41871bd14e760d432daf05152f06413f)
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)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id_producao int64 | id_pessoa int64 | papel string |
|---|---|---|
715,190 | 37,497 | Nadine's Client |
715,190 | 124,692 | null |
715,190 | 258,179 | Reporter |
715,190 | 516,564 | The Butler |
715,190 | 683,371 | Palace Guard |
715,190 | 874,192 | Man with Umbrella |
715,191 | 22,384 | Man |
715,191 | 30,244 | Officer #1 |
715,191 | 59,786 | null |
715,191 | 78,882 | Teammate |
715,191 | 111,131 | Mr. Orvac |
715,191 | 112,755 | null |
715,191 | 117,787 | The vicar |
715,191 | 123,253 | Tailor |
715,191 | 128,064 | Mr. Vickers |
1,455,526 | 614,599 | null |
715,191 | 143,791 | Wicket Keeper |
715,191 | 148,692 | English photographer |
715,191 | 155,719 | Cavalier |
715,191 | 191,435 | Farrow |
715,191 | 234,010 | English Reporter |
715,191 | 234,266 | Vincent |
715,191 | 234,977 | Officer of the Week |
715,191 | 248,743 | English photographer |
715,191 | 249,585 | Michael |
715,191 | 252,922 | Whately |
715,191 | 290,085 | Englishman |
715,191 | 295,441 | null |
715,191 | 382,904 | Officer |
715,191 | 409,492 | Franz |
715,191 | 425,595 | Radio Operator |
715,191 | 463,131 | null |
715,191 | 479,895 | British lieutenant |
715,191 | 489,682 | Senior Flying Officer Wales |
715,191 | 506,716 | Winfred Langford |
715,191 | 510,252 | null |
715,191 | 516,056 | null |
715,191 | 516,485 | Actor |
715,191 | 516,554 | British Man |
715,191 | 548,767 | Courtier |
715,191 | 559,557 | Campbell |
715,191 | 564,482 | Restaurant Captain |
715,191 | 569,167 | Butler |
715,191 | 569,218 | Grosvenor the Butler |
715,191 | 581,462 | Powell/Arthur Thurston-Wells |
715,191 | 586,259 | null |
715,191 | 638,417 | Briefing Officer |
715,191 | 696,626 | Physician |
715,191 | 713,265 | Desk man |
715,191 | 713,458 | Reporter |
715,191 | 728,949 | Reporter |
715,191 | 728,952 | Waiter |
715,191 | 751,731 | Sandy |
715,191 | 751,732 | Sandy |
715,191 | 751,734 | 1st Man |
715,191 | 785,661 | Submarine Captain |
715,191 | 786,873 | Charlie |
715,191 | 801,812 | Man |
715,191 | 849,678 | Inspector Mac Ritchie |
715,191 | 881,872 | Head Croupier |
715,191 | 899,899 | Maitre d' |
715,191 | 944,091 | null |
715,191 | 950,656 | null |
715,191 | 950,703 | Train Guard |
715,191 | 971,617 | RAF Wing Commander |
715,191 | 971,620 | Policeman |
715,191 | 971,629 | Shullendorf |
715,191 | 971,632 | British Officer |
715,191 | 971,651 | Barkeep |
715,191 | 998,036 | null |
715,191 | 998,660 | Gruber |
715,192 | 58,013 | Danny Avery |
715,193 | 477,774 | Danny Avery |
715,193 | 606,175 | Dennis Hicks |
715,194 | 92,366 | Government messenger |
715,194 | 168,560 | 1st Guest |
715,194 | 363,604 | null |
715,194 | 416,805 | null |
715,194 | 418,242 | Police Chief Richards |
715,194 | 478,373 | Lover |
715,194 | 821,004 | null |
715,195 | 663,055 | Himself |
715,195 | 926,656 | Himself |
715,196 | 7,972 | Gardener |
715,197 | 22,738 | null |
715,197 | 109,212 | null |
715,197 | 242,878 | Jack |
715,197 | 356,860 | Deserter |
715,197 | 475,271 | Runner |
715,197 | 519,084 | Police Driver |
715,197 | 856,948 | Safety Officer |
715,198 | 159,730 | null |
715,198 | 167,675 | null |
715,198 | 331,870 | Himself |
715,198 | 331,883 | Himself |
715,198 | 354,173 | Himself |
715,199 | 198,284 | Gunther |
715,200 | 7,058 | Kriminalkommissar Dengern |
715,200 | 47,372 | Tom Wilkins |
715,200 | 110,236 | Baron von Reiffenberg |
BD_Producao_Artistica
Base relacional em formato tabular contendo informações sobre produções artísticas, pessoas e o vínculo de equipe (papéis) entre pessoas e produções.
Os dados foram convertidos a partir de arquivos .txt (delimitador ##) para JSONL (.jsonl, 1 registro por linha), visando facilitar ingestão e processamento em pipelines de dados.
Projeto/Trabalho relacionado
Este dataset foi utilizado no trabalho final da disciplinade Administrador de Banco de Dados (DBA).
Repositório do trabalho (código, instruções e entregáveis):
https://github.com/Willgnner-Santos/IT-Residence-TJGO/tree/main/Disciplines/Eixo%20An%C3%A1lise%20e%20Ci%C3%AAncia%20de%20Dados/Administrador%20de%20Banco%20de%20Dados%20(DBA)/Trabalho-Final
Dataset Summary
- Formato: JSONL (
.jsonl) - Entidades (tabelas):
producao(obras/produções)pessoa(pessoas associadas)equipe(relação N:N entre pessoa e produção + papel)
- Chaves de relacionamento:
pessoa.id_pessoa↔equipe.id_pessoaproducao.id_producao↔equipe.id_producao
Tamanho (contagem de registros)
Conversão realizada localmente com resultado:
equipe: 12,849,041 registrospessoa: 2,062,495 registrosproducao: 1,023,616 registros
Total: 15,935,152 registros (soma das 3 tabelas)
Dataset Structure
Data Files
Os arquivos do dataset são:
equipe.jsonlpessoa.jsonlproducao.jsonl
Recomenda-se streaming no consumo, pois os arquivos podem ser grandes.
Data Schema
pessoa.jsonl
Campos:
id_pessoa(string/int): identificador da pessoanome(string): nome da pessoa
Exemplo:
{"id_pessoa":"123","nome":"FULANO DE TAL"}
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