Skip to content
Novus Examples
json5.5 KB

Fieldnote Supply Shop: Order export JSON

Order export JSON for Fieldnote Supply Shop. The same 12 orders as orders-export.csv with an added totals object. totals.taxableAmount is 922.00 and equals the sum of the per-order taxable_amount values.

json

application/json

5.5 KB
Document Set
ecommerce
Industry
retail
Source Kit
retail-commerce
Synthetic
true
As Of
2026-09-08
Rows
12

Binary json: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
ecommerce
Industry
retail
Source Kit
retail-commerce
Synthetic
true
As Of
2026-09-08
Rows
12
Encoding
UTF-8
Top Level
object

Testing contract

Expected to pass
Scenario
Parse the JSON export and assert the totals object against the order array.
Expected result
totals.orders is 12; summing orders[].taxable_amount gives 922.00 and orders[].tax_amount gives 96.34, matching totals exactly.

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

“Fieldnote Supply Shop: Order export JSON” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: 12 rows · UTF-8. 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("orders-export.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/industry_documents.py. Free for any use, no attribution required, license.