Hugging Face's logo

Datasets:
THULab
/
knowable

Tasks:
Time Series Forecasting
Modalities:
Time-series
Tags:
tsfile
timeseries
time-series
format:tsfile
Dataset card Files
xet
 Community
knowable / README.md
THUgewu's picture
THUgewu
Add TsFile (converted from jwlutz/knowable)
c8a3ba6 verified
preview code
|
Raw History Blame Contribute Delete
4.06 kB
metadata 
task_categories:
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - time-series
  - format:tsfile
pretty_name: knowable
configs:
  - config_name: default
    data_files:
      - split: train
        path: congress_trades_2.tsfile
      - split: train
        path: congress_trades_3.tsfile
      - split: train
        path: congress_trades_4.tsfile
      - split: train
        path: congress_trades_5.tsfile
      - split: train
        path: congress_trades_6.tsfile
      - split: train
        path: congress_trades_7.tsfile
      - split: train
        path: congress_trades_8.tsfile
      - split: train
        path: house_filings_1.tsfile
      - split: train
        path: house_filings_2.tsfile
      - split: train
        path: house_filings_4.tsfile
      - split: train
        path: house_filings_5.tsfile
      - split: train
        path: house_filings_6.tsfile
      - split: train
        path: house_filings_8.tsfile
      - split: train
        path: house_filings_9.tsfile
      - split: train
        path: insider_transactions_2.tsfile
      - split: train
        path: insider_transactions_3.tsfile
      - split: train
        path: insider_transactions_5.tsfile
      - split: train
        path: insider_transactions_6.tsfile
      - split: train
        path: insider_transactions_7.tsfile
      - split: train
        path: insider_transactions_8.tsfile
      - split: train
        path: insider_transactions_9.tsfile
      - split: train
        path: senate_filings_1.tsfile
      - split: train
        path: senate_filings_2.tsfile
      - split: train
        path: senate_filings_3.tsfile
      - split: train
        path: senate_filings_4.tsfile
      - split: train
        path: senate_filings_5.tsfile
      - split: train
        path: senate_filings_6.tsfile
      - split: train
        path: senate_filings_7.tsfile
      - split: train
        path: senate_filings_8.tsfile
      - split: train
        path: senate_filings_9.tsfile

knowable (TsFile)

Apache TsFile version of jwlutz/knowable.

  • Converted rows: 2,986,261
  • Data files: ['congress_trades_2.tsfile', 'congress_trades_3.tsfile', 'congress_trades_4.tsfile', 'congress_trades_5.tsfile', 'congress_trades_6.tsfile', 'congress_trades_7.tsfile', 'congress_trades_8.tsfile', 'house_filings_1.tsfile', 'house_filings_2.tsfile', 'house_filings_4.tsfile', 'house_filings_5.tsfile', 'house_filings_6.tsfile', 'house_filings_8.tsfile', 'house_filings_9.tsfile', 'insider_transactions_2.tsfile', 'insider_transactions_3.tsfile', 'insider_transactions_5.tsfile', 'insider_transactions_6.tsfile', 'insider_transactions_7.tsfile', 'insider_transactions_8.tsfile', 'insider_transactions_9.tsfile', 'senate_filings_1.tsfile', 'senate_filings_2.tsfile', 'senate_filings_3.tsfile', 'senate_filings_4.tsfile', 'senate_filings_5.tsfile', 'senate_filings_6.tsfile', 'senate_filings_7.tsfile', 'senate_filings_8.tsfile', 'senate_filings_9.tsfile']

The fund_holdings table (~890 MB per daily release) is not included; converted tables are congress_trades, insider_transactions, house_filings and senate_filings. Overlapping daily releases are deduplicated.

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("congress_trades_2.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