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.
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,2025-07-17Specifications
- 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.
How to use this file for testing
“E-commerce Orders (CSV, 2000 rows)” is a deterministic Novus Examples fixture for Data import, Conversion testing, Data engineering. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 2,000 rows · 7 columns · schema: order_id, customer_id, product_id, quantity, total, status, order_date. 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("orders.csv")
print(df.head())
print(df.dtypes)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.
- csvStar Schema — Customer Dimension (CSV)The customer dimension of a star schema — surrogate key, name, city, country, and segment. Joins to the sales fact table on customer_key. Names are synthetic.
- csvStar Schema — Date Dimension (CSV)The date dimension of a star schema — one row per day with a YYYYMMDD surrogate key and calendar attributes (year, quarter, month, weekday). Joins to the sales fact table on date_key.
- csvStar Schema — Product Dimension (CSV)The product dimension of a star schema — surrogate key, SKU, name, category, and unit price. Joins to the sales fact table on product_key.
- csvStar Schema — Sales Fact Table (CSV, 300 rows)The sales fact table at the centre of a star schema — 300 sale lines with foreign keys to the date, product, and customer dimensions plus quantity and amount measures. A fixture for testing joins, star-schema imports, and BI tools.
Generated by generation/data_realworld.py. Free for any use, no attribution required — license.