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.
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,427.66,2025-10-10Specifications
- 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.
How to use this file for testing
“Fake Orders Dataset (CSV, 1000 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: seed 5678 · 1,000 rows · schema: id, user_id, product, amount, 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
- 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.
- 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.
- jsonColumnar Nulls Schema (JSON)JSON description of nullable columns in the null-heavy columnar fixtures.
- gexfDirected Acyclic Pipeline - GEXF 1.3A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.
Generated by generation/data_fake.py. Free for any use, no attribution required — license.