Error code:   ConfigNamesError
Exception:    ValueError
Message:      Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train']
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                      path=dataset,
                      token=hf_token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                      path,
                  ...<4 lines>...
                      **download_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1215, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1190, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 646, in get_module
                  patterns = sanitize_patterns(next(iter(metadata_configs.values()))["data_files"])
                File "/usr/local/lib/python3.14/site-packages/datasets/data_files.py", line 151, in sanitize_patterns
                  raise ValueError(f"Some splits are duplicated in data_files: {splits}")
              ValueError: Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train']

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ipl_enriched_dataset (TsFile)

Apache TsFile version of prasad-gade05/ipl-enriched-dataset.

Converted rows: 296,975
Data files: ['ball_by_ball_1.tsfile', 'ball_by_ball_2.tsfile', 'ball_by_ball_3.tsfile', 'ball_by_ball_4.tsfile', 'ball_by_ball_5.tsfile', 'ball_by_ball_6.tsfile', 'ball_by_ball_7.tsfile', 'ball_by_ball_8.tsfile', 'ball_by_ball_9.tsfile', 'match_summary_1.tsfile', 'match_summary_2.tsfile', 'match_summary_3.tsfile', 'match_summary_4.tsfile', 'match_summary_5.tsfile', 'match_summary_6.tsfile', 'match_summary_7.tsfile', 'match_summary_8.tsfile', 'match_summary_9.tsfile']

Converted tables are ball_by_ball (the core event stream; per-ball times use the match date plus a deterministic 1 ms ordinal offset per (match, innings) device) and match_summary. The 19 auxiliary/derived parquet tables (over_summary, partnerships, dot_sequences, matchups, dismissal tables, player tables, innings_tags, ball_by_ball_cleaned) are not converted; they remain available in the original dataset.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("ball_by_ball_1.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

Original dataset: https://huggingface.co/datasets/prasad-gade05/ipl-enriched-dataset
Author / publisher: prasad-gade05
License: cc0-1.0

Downloads last month: -

Total file size:

40.1 MB