THULab/indian_domestic_airline_dataset

Indian Domestic Airline Flights 2018-2025 (TsFile)

Apache TsFile version of `Gokul99400/IndianDomesticAirlineDataset`.

Overview

Indian domestic airline schedule records covering 2018-2025 across ~100 airports: airline, flight number, route, operating days of week, scheduled departure/arrival times and the schedule validity window (validFrom..validTo). The repo ships two CSVs: Air-Clean.csv (33,734 deduplicated rows) and Air_full-Raw.csv (113,339 rows, incl. duplicates); both are converted into separate TsFiles so no data is dropped. Typical uses: flight schedule analysis, route popularity, seasonal trends.

• —Clean file: 33,734 rows, 14 airlines, 5,975 flight numbers.
• —Full raw file: 113,339 rows, 14 airlines, 9,177 flight numbers.
• —Validity period: 2018-10-28 .. 2025-10-25.
• —Note: the source column timezone always equals validTo (it stores the schedule-season end date); both columns are kept unchanged.
• —Within each file, identical duplicate schedule rows (e.g. 3,427 in the raw file) are kept and disambiguated by the seq TAG (stable source row order) instead of being dropped.

Schema (TsFile structure)

• —Time (INT64, milliseconds) — validFrom, the day the schedule period starts (naive).
• —airline, flightNumber, origin, destination, daysOfWeek, scheduledDepartureTime, scheduledArrivalTime, seq (TAG, STRING) — the flight schedule identity; scheduledArrivalTime is empty in the raw file where the source left it blank.
• —timezone, validTo, lastUpdated (FIELD, STRING) — validity window end and metadata dates.

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("indian_domestic_airline_dataset_clean.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/Gokul99400/IndianDomesticAirlineDataset>
• —License: not declared by the original dataset (card shows unknown); please defer to the original.