E-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.
[
{
"product_id": 1,
"sku": "SKU-00001",
"name": "Wireless Coffee Beans",
"category": "Home & Kitchen",
"price": 222.23,
"stock": 216,
"rating": 4.4
},
{
"product_id": 2,
"sku": "SKU-00002",
"name": "Deluxe Notebook",
"category": "Books",
"price": 487.92,
"stock": 47,
"rating": 4.5
},
{
"product_id": 3,
"sku": "SKU-00003",
"name": "Classic Coffee Beans",
"category": "Clothing",
"price": 68.41,
"stock": 419,
"rating": 3.7
},
{
"product_id": 4,
"sku": "SKU-00004",
"name": "Ergonomic Blender",
"category": "Sports",
"price": 323.7,
"stock": 463,
"rating": 4.6
},
{
"product_id": 5,
"sku": "SKU-00005",
"name": "Stainless Yoga Mat",
"category": "Toys",
"price": 117.47,
"stock": 46,
"rating": 3.1
},
{
"product_id": 6,
"sku": "SKU-00006",
"name": "Stainless Desk Lamp",Specifications
- Records
- 200
- Schema
- product_id, sku, name, category, price, stock, rating
- Domain
- e-commerce
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“E-commerce Products (JSON, 200 records)” 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: 200 records · schema: product_id, sku, name, category, price, stock, rating. 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 json
with open("products.json") as f:
data = json.load(f)
print(type(data), len(data))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.
- 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.
- jsonE-commerce Orders (JSON, 2000 records)The e-commerce orders table as a JSON array — the format twin of the CSV, for import and conversion testing.
- 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.
Generated by generation/data_realworld.py. Free for any use, no attribution required — license.