csv2.6 KB
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.
Preview — first 32 linescsv
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.
Code examples
import pandas as pd
df = pd.read_csv("flights.csv")
print(df.head())
print(df.dtypes)Related files
- csvBank Transactions (CSV, 60 rows)A bank-transaction statement — 60 debits and credits across three accounts (masked numbers) with running balances, categories, and merchants. Synthetic data for testing statement parsers, categorisation, and reconciliation.
- jsonBank Transactions (JSON, 60 records)The bank transactions as a JSON array — the format twin of the CSV, for import and reconciliation testing.
- csvE-commerce Customers (CSV, 500 rows)A realistic e-commerce customer directory (500 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
- jsonE-commerce Customers (JSON, 500 records)The e-commerce customers table as a JSON array — the format twin of the CSV, for import and conversion testing.
- sqlE-commerce Database Schema (SQL)A relational SQL schema (products, customers, orders with primary and foreign keys) plus sample INSERTs — the DDL twin of the e-commerce dataset, for testing schema import and migrations.
- csvE-commerce Orders (CSV, 2000 rows)A realistic e-commerce order lines (customer_id → customers, product_id → products) (2000 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
Generated by generation/data_domains.py. Free for any use, no attribution required — license.