Flight Schedule (CSV, 30 rows)
A flight-schedule dataset — 30 flights with airline, origin/destination airports, departure/arrival times, aircraft, gate, and status. Synthetic data for testing travel apps, schedule parsers, and status boards.
flight_no,airline,origin,destination,departure,arrival,aircraft,gate,status
EX623,Example Airways,ORD,LAX,2026-02-01T20:53:00,2026-02-02T04:43:00,A320,B27,delayed
DM837,Demo Wings,DXB,LAX,2026-02-01T07:21:00,2026-02-01T12:24:00,A350-900,E9,departed
EX785,Example Airways,LHR,LAX,2026-02-01T13:36:00,2026-02-01T21:35:00,E190,F6,scheduled
SM303,Sample Jet,SIN,DXB,2026-02-01T23:33:00,2026-02-02T09:41:00,E190,D17,landed
SM486,Sample Jet,DXB,LAX,2026-02-01T07:24:00,2026-02-01T08:47:00,B787-9,F7,boarding
NV292,Novus Air,FRA,JFK,2026-02-01T19:38:00,2026-02-02T08:03:00,A320,F13,delayed
EX304,Example Airways,ORD,JFK,2026-02-01T06:48:00,2026-02-01T18:07:00,A350-900,E4,landed
SM386,Sample Jet,ORD,GRU,2026-02-01T17:50:00,2026-02-02T06:24:00,E190,D12,landed
SM231,Sample Jet,GRU,LHR,2026-02-01T17:40:00,2026-02-01T20:58:00,B737-800,D7,departed
EX410,Example Airways,YYZ,ORD,2026-02-01T11:42:00,2026-02-01T14:15:00,A350-900,C26,scheduled
DM377,Demo Wings,DXB,HND,2026-02-01T18:19:00,2026-02-02T06:44:00,A320,A8,departed
NV426,Novus Air,YYZ,GRU,2026-02-01T07:16:00,2026-02-01T19:09:00,B787-9,A22,landed
EX302,Example Airways,LHR,JFK,2026-02-01T07:05:00,2026-02-01T08:17:00,B787-9,D5,departed
SM284,Sample Jet,YYZ,JFK,2026-02-01T21:14:00,2026-02-02T06:37:00,A321neo,B5,scheduled
SM522,Sample Jet,JFK,FRA,2026-02-01T13:25:00,2026-02-01T22:44:00,B787-9,A24,delayed
DM915,Demo Wings,LAX,CDG,2026-02-01T18:43:00,2026-02-02T00:00:00,E190,B21,scheduled
SM945,Sample Jet,SYD,DXB,2026-02-01T07:49:00,2026-02-01T19:07:00,B737-800,E4,departed
SM751,Sample Jet,LAX,HND,2026-02-01T17:54:00,2026-02-02T00:52:00,A321neo,C26,scheduled
DM337,Demo Wings,CDG,DXB,2026-02-01T15:36:00,2026-02-02T03:29:00,B787-9,C6,landed
SM366,Sample Jet,HND,ORD,2026-02-01T13:26:00,2026-02-01T14:51:00,E190,A1,scheduled
DM263,Demo Wings,JFK,HND,2026-02-01T12:02:00,2026-02-01T17:27:00,E190,C3,boarding
NV116,Novus Air,HND,LHR,2026-02-01T20:15:00,2026-02-02T02:17:00,B787-9,A15,boarding
EX979,Example Airways,LAX,FRA,2026-02-02T00:47:00,2026-02-02T08:46:00,B737-800,C21,departed
SM917,Sample Jet,GRU,JFK,2026-02-01T18:47:00,2026-02-02T00:58:00,A321neo,A13,boarding
DM862,Demo Wings,SYD,DXB,2026-02-01T10:00:00,2026-02-01T21:30:00,B737-800,E17,delayed
SM606,Sample Jet,CDG,JFK,2026-02-01T22:33:00,2026-02-02T05:28:00,A350-900,E1,delayed
DM241,Demo Wings,CDG,LHR,2026-02-01T11:07:00,2026-02-01T22:12:00,A350-900,B9,scheduled
NV200,Novus Air,SYD,GRU,2026-02-01T22:50:00,2026-02-02T06:28:00,B737-800,D10,delayed
SM509,Sample Jet,DXB,JFK,2026-02-01T17:08:00,2026-02-01T21:44:00,B737-800,C19,landed
EX613,Example Airways,LAX,CDG,2026-02-01T09:16:00,2026-02-01T16:42:00,B787-9,B11,cancelled
Specifications
- Rows
- 30
- Schema
- flight_no, airline, origin, destination, departure, arrival, aircraft, gate, status
- Domain
- travel
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“Flight Schedule (CSV, 30 rows)” is a deterministic Novus Examples fixture for Data import, Conversion testing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 30 rows · schema: flight_no, airline, origin, destination, departure, arrival, aircraft, gate, status. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import pandas as pd
df = pd.read_csv("flights.csv")
print(df.head())
print(df.dtypes)Related files
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- jsonColumnar Nulls Schema (JSON)JSON description of nullable columns in the null-heavy columnar fixtures.
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Generated by generation/data_domains.py. Free for any use, no attribution required — license.