Datasets:

Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ValueError
Message:      Some splits are duplicated in data_files: ['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']

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SG_Carpark (TsFile)

Apache TsFile version of the SG_Carpark sub-dataset of Real-TSF/TIME-ProcessedCSV.

Overview

SG_Carpark is one of the processed time-series collections bundled in TIME-ProcessedCSV, a multi-domain repository of cleaned CSV series (energy, transport, weather, finance, health and more), each with a timestamp column and one or more measurement columns per file.

  • Source files: 1 CSV file(s) under https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV/tree/main/SG_Carpark
  • Converted rows: 5,131,230 (long format: one row per measurement)
  • Data files: ['sg_carpark_1.tsfile', 'sg_carpark_10.tsfile', 'sg_carpark_11.tsfile', 'sg_carpark_2.tsfile', 'sg_carpark_3.tsfile', 'sg_carpark_4.tsfile', 'sg_carpark_5.tsfile', 'sg_carpark_6.tsfile', 'sg_carpark_7.tsfile', 'sg_carpark_8.tsfile', 'sg_carpark_9.tsfile']

Schema (TsFile structure)

  • Time (INT64, milliseconds) — the source timestamp column.
  • freq (TAG) — source sampling-frequency directory (e.g. H, 15T, D).
  • series (TAG) — source file stem (e.g. item0, NRSROT).
  • channel (TAG) — source measurement column name.
  • measurement (FIELD, FLOAT) — the measurement value.

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("sg_carpark_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

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