csv77.6 KB
E-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.
Preview — first 50 linescsv
order_id,customer_id,product_id,quantity,total,status,order_date
1,150,171,2,705.98,paid,2025-10-19
2,174,36,1,25.59,refunded,2025-06-09
3,31,1,2,444.46,shipped,2025-04-27
4,293,51,4,881.44,delivered,2025-08-22
5,431,127,3,1105.89,shipped,2025-03-12
6,295,190,5,234.85,delivered,2025-05-03
7,336,52,5,2229.65,refunded,2025-05-26
8,236,54,4,207.0,pending,2025-04-29
9,160,35,3,882.36,pending,2025-05-20
10,160,86,4,1292.08,refunded,2025-04-28
11,468,156,3,225.24,shipped,2025-12-26
12,470,28,1,184.09,refunded,2025-02-19
13,147,148,1,160.37,pending,2025-01-24
14,287,11,2,138.6,pending,2025-03-27
15,132,197,3,1439.4,shipped,2025-05-22
16,24,159,2,54.18,pending,2025-12-06
17,318,171,1,352.99,paid,2025-06-19
18,449,156,1,75.08,delivered,2025-05-06
19,489,90,2,373.82,paid,2025-03-28
20,116,99,2,784.78,shipped,2025-12-25
21,145,31,1,227.42,pending,2025-05-25
22,82,83,5,1321.25,pending,2025-01-08
23,138,23,3,842.16,shipped,2025-12-13
24,206,6,2,635.32,shipped,2025-11-28
25,234,111,4,1441.12,refunded,2025-09-01
26,115,106,2,899.36,paid,2025-05-13
27,458,120,2,206.2,shipped,2025-08-19
28,167,173,4,1446.92,paid,2025-03-06
29,124,149,2,828.88,refunded,2025-05-13
30,248,97,1,364.22,shipped,2025-03-03
31,96,173,4,1446.92,pending,2025-12-21
32,160,121,1,22.89,pending,2025-05-19
33,45,5,5,587.35,pending,2025-08-01
34,409,187,1,337.44,pending,2025-03-31
35,354,31,1,227.42,delivered,2025-01-26
36,45,96,2,140.76,shipped,2025-09-16
37,344,47,3,356.85,refunded,2025-09-03
38,141,99,1,392.39,refunded,2025-05-09
39,229,107,4,741.08,pending,2025-01-09
40,498,180,4,1119.76,refunded,2025-12-10
41,125,59,2,23.78,refunded,2025-05-01
42,485,112,3,961.02,delivered,2025-11-07
43,40,182,5,295.4,paid,2025-01-21
44,75,147,5,162.5,paid,2025-06-08
45,57,110,4,969.12,delivered,2025-09-04
46,258,60,2,130.6,shipped,2025-09-02
47,412,128,2,980.38,pending,2025-01-04
48,368,173,1,361.73,paid,2025-05-19
49,352,77,1,81.03,pending,2025-07-17… 1656 lines total — download for the full file.
Specifications
- Rows
- 2000
- Columns
- 7
- Schema
- order_id, customer_id, product_id, quantity, total, status, order_date
- Domain
- e-commerce
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
- 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.
- csvE-commerce Products (CSV, 200 rows)A realistic e-commerce product catalogue (200 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.
- jsonE-commerce Products (JSON, 200 records)The e-commerce products table as a JSON array — the format twin of the CSV, for import and conversion testing.
- parquetE-commerce Products (Parquet, 200 rows)The e-commerce products table as Apache Parquet — the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).
Generated by generation/data_realworld.py. Free for any use, no attribution required — license.