Datasets:

smilegeng
/

airespucrs_sales

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 304, in _scan_metadata
                  from tsfile.constants import TIME_COLUMN, ColumnCategory
              ModuleNotFoundError: No module named 'tsfile'
              
              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 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/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.

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Time-Series Data (Teeny-Tiny Castle) — TsFile

Daily product sales time series, converted to Apache TsFile format from the original parquet dataset.

Original dataset: AiresPucrs/time-series-data
Part of: Teeny-Tiny Castle — open-source educational tools for AI Ethics and Safety research
License: apache-2.0
Coverage: 2020-01-01 → 2023-01-02, daily granularity (1098 days)
Scale: 1098 rows · 1 product (chocolate)

About the original dataset

The source is a single parquet file (data/train-*.parquet), 1098 rows × 3 columns. It records daily sales for a single product.

Column	Meaning	dtype	range
`dates`	calendar day, `YYYY-MM-DD`	string	2020-01-01 .. 2023-01-02 (1098 distinct)
`product_id`	product name	string	single value: `chocolate`
`sales`	units sold that day	float64	0 / 103.35 / 466

(product_id, dates) is unique — exactly one row per product per day, no duplicates, no nulls.

How it is stored in this TsFile

One table airespucrs_sales. The product (product_id) is the device/series identity; sales is the measurement over time.

TsFile column	Category	Type	From	Notes
`Time`	TIME	INT64	`dates`	epoch milliseconds (UTC midnight)
`product_id`	TAG	STRING	`product_id`	series identity (device dimension)
`sales`	FIELD	DOUBLE	`sales`	units sold, values unchanged

Modifications made during conversion

Every change is listed below; no other transformation was performed.

Format: parquet → TsFile (single file airespucrs_sales.tsfile).
product_id set as a TAG column (the device/series dimension). The source has a single product, so the TsFile contains one time-series; the tag is kept so the product identity is preserved and the layout generalises to multiple products.
dates → Time: the YYYY-MM-DD string is parsed to INT64 epoch milliseconds (UTC midnight) to form a real time axis.
Original dates string column is dropped. Its information is fully preserved in Time (lossless), so it is not stored as a separate field.
sales precision is preserved: stored as DOUBLE (the source is float64); values are copied verbatim, nothing rounded or rescaled.

Not changed / not dropped

Apart from the dates string column (item 4, losslessly folded into Time), nothing is removed. product_id and sales are retained and the row count is preserved exactly.

Verification

Round-trip checked with the TsFile Python SDK:

row count: TsFile read-back 1,098 == 1,098 staged Parquet ✓
product_id reported as category=TAG; sales as FIELD ✓
sales stored as DOUBLE (source float64) ✓

Usage

from tsfile import TsFileReader
reader = TsFileReader("airespucrs_sales.tsfile")
schemas = reader.get_all_table_schemas()          # table: "airespucrs_sales"
# query field/tag columns; Time is added automatically by the reader

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