csv31.2 KB
Fake Orders Dataset (CSV, 1000 rows)
A 1000-row orders dataset whose user_id references the users dataset — a relational fixture for testing joins and import flows. Paired with a JSON twin.
Preview — first 50 linescsv
id,user_id,product,amount,date
1,480,Reach,284.72,2025-03-29
2,254,Actually,258.4,2025-07-23
3,471,White,309.38,2025-07-03
4,487,Maintain,286.3,2025-08-28
5,325,Successful,279.48,2025-02-25
6,144,Leg,236.42,2025-12-22
7,54,Option,465.57,2025-03-15
8,306,Somebody,126.71,2025-09-25
9,302,Medical,198.58,2025-11-03
10,155,On,138.78,2025-12-16
11,454,Effort,468.43,2025-06-04
12,176,Official,192.05,2025-05-03
13,334,Heart,25.08,2025-04-29
14,388,Special,152.83,2025-02-20
15,178,You,228.87,2025-02-17
16,352,Federal,445.48,2025-12-29
17,33,Everything,313.66,2025-07-01
18,218,Fine,294.76,2025-04-28
19,294,Teach,329.56,2025-07-12
20,300,Gas,258.08,2025-10-26
21,455,Make,39.14,2025-10-03
22,97,Attention,129.76,2025-07-21
23,140,Remain,61.0,2025-04-27
24,48,Organization,143.89,2025-01-07
25,282,Well,174.9,2025-05-10
26,297,Mind,390.04,2025-06-04
27,233,Treatment,169.26,2025-05-08
28,483,Her,121.96,2025-11-29
29,346,Method,288.47,2025-04-12
30,344,Series,385.93,2025-05-20
31,151,Notice,143.36,2025-08-02
32,309,White,456.0,2025-07-22
33,254,Charge,360.56,2025-09-09
34,287,Energy,337.83,2025-05-01
35,223,Bill,377.44,2025-05-22
36,385,Mr,208.1,2025-08-04
37,320,When,245.06,2025-10-08
38,19,Side,448.2,2025-10-20
39,158,Take,392.1,2025-01-02
40,73,Military,329.41,2025-08-27
41,29,Several,112.96,2025-02-21
42,353,Job,42.29,2025-05-28
43,180,Speak,313.5,2025-03-12
44,134,Child,197.35,2025-08-13
45,116,Evening,201.54,2025-12-21
46,128,Sport,365.31,2025-05-16
47,220,Star,274.64,2025-11-24
48,128,Economic,206.16,2025-07-01
49,119,Agent,427.66,2025-10-10… 1002 lines total — download for the full file.
Specifications
- Rows
- 1000
- Schema
- id, user_id, product, amount, date
- Relates To
- users.id
- Seed
- 5678
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.
Related files
- csvFake Users Dataset (CSV, 500 rows)A 500-row dataset of fake but realistic users (name, email, address, date of birth), generated with a fixed seed. Paired with a JSON twin.
- jsonFake Users Dataset (JSON, 500 records)The 500-row users dataset as a JSON array — the format twin of the CSV version, for testing import and conversion.
- 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_fake.py. Free for any use, no attribution required — license.