Datasets:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
tsfile.exceptions.FileOpenError: 28:
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
scan = self._scan_metadata(all_files)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
with self._open_reader(file) as reader:
~~~~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
return TsFileReader(file)
File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
Stephen Curry Game Log (TsFile)
This dataset is an Apache TsFile conversion of
jawwaadsabree/CurryData.
Modalities: Time-series.
Overview
Per-game NBA stat lines for Stephen Curry across seasons 2009–2016.
Game statistics (
PTS,REB,AST,MIN,FG/3PT/FTsplits, etc.) plus engineered cyclic date features.Each season is a device identified by the
seasonTAG.Converted observations: 1,204 rows across 1 TsFile file(s)
Source format: csv
TsFile schema
- Time — source
Date(epoch nanoseconds), converted to INT64 milliseconds.
| Column | Role | Type | Meaning |
|---|---|---|---|
Time |
TIME | INT64 (ms) | sample timestamp |
season |
TAG | STRING | season (e.g. 2009_2010) |
Result |
FIELD | FLOAT | win/loss |
MIN |
FIELD | FLOAT | — |
REB |
FIELD | FLOAT | — |
AST |
FIELD | FLOAT | — |
BLK |
FIELD | FLOAT | — |
STL |
FIELD | FLOAT | — |
PF |
FIELD | FLOAT | — |
TO |
FIELD | FLOAT | — |
PTS |
FIELD | FLOAT | points |
FG_Made |
FIELD | FLOAT | — |
FG_Attempts |
FIELD | FLOAT | — |
c_3PT_Made |
FIELD | FLOAT | — |
c_3PT_Attempts |
FIELD | FLOAT | — |
FT_Made |
FIELD | FLOAT | — |
FT_Attempts |
FIELD | FLOAT | — |
Opponent_1 |
FIELD | FLOAT | — |
Opponent_2 |
FIELD | FLOAT | — |
Opponent_3 |
FIELD | FLOAT | — |
Opponent_4 |
FIELD | FLOAT | — |
Opponent_5 |
FIELD | FLOAT | — |
Opponent_6 |
FIELD | FLOAT | — |
Opponent_7 |
FIELD | FLOAT | — |
Opponent_8 |
FIELD | FLOAT | — |
Opponent_9 |
FIELD | FLOAT | — |
Opponent_10 |
FIELD | FLOAT | — |
Opponent_11 |
FIELD | FLOAT | — |
Opponent_12 |
FIELD | FLOAT | — |
Opponent_13 |
FIELD | FLOAT | — |
Opponent_14 |
FIELD | FLOAT | — |
Opponent_15 |
FIELD | FLOAT | — |
Opponent_16 |
FIELD | FLOAT | — |
Opponent_17 |
FIELD | FLOAT | — |
Opponent_18 |
FIELD | FLOAT | — |
Opponent_19 |
FIELD | FLOAT | — |
Opponent_20 |
FIELD | FLOAT | — |
Opponent_21 |
FIELD | FLOAT | — |
Opponent_22 |
FIELD | FLOAT | — |
Opponent_23 |
FIELD | FLOAT | — |
Opponent_24 |
FIELD | FLOAT | — |
Opponent_25 |
FIELD | FLOAT | — |
Opponent_26 |
FIELD | FLOAT | — |
Opponent_27 |
FIELD | FLOAT | — |
Opponent_28 |
FIELD | FLOAT | — |
Opponent_29 |
FIELD | FLOAT | — |
Opponent_30 |
FIELD | FLOAT | — |
Is_Home |
FIELD | FLOAT | — |
Year |
FIELD | FLOAT | — |
Month_Sin |
FIELD | FLOAT | — |
Month_Cos |
FIELD | FLOAT | — |
Day_Sin |
FIELD | FLOAT | — |
Day_Cos |
FIELD | FLOAT | — |
Day_of_Week_Sin |
FIELD | FLOAT | — |
Day_of_Week_Cos |
FIELD | FLOAT | — |
Day_of_Year_Sin |
FIELD | FLOAT | — |
Day_of_Year_Cos |
FIELD | FLOAT | — |
Is_Playoff |
FIELD | FLOAT | — |
Is_Regular_Season |
FIELD | FLOAT | — |
Is_Preseason |
FIELD | FLOAT | — |
Conversion notes
season(from the source file name) is a TAG so each season is a separate device.- 57 game-stat / engineered-feature columns kept as FLOAT/INT64; no columns dropped.
Source & license
- Original dataset: https://huggingface.co/datasets/jawwaadsabree/CurryData
- Author / publisher: jawwaadsabree
- License: not declared by the original dataset; please defer to the original
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("curry_data.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())
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