Skip to content
Novus Examples
json6.9 KB

Pine Assembly Workshop: Shipment export JSON

Shipment export JSON for Pine Assembly Workshop. The same 16 shipments as shipments-export.csv with a totals object. totals.parcels is 56 and totals.grossWeightKg is 683.200, the sum of the per-shipment weights.

json

application/json

6.9 KB
Document Set
logistics
Industry
logistics
Source Kit
inventory-manufacturing
Synthetic
true
As Of
2026-09-08
Rows
16

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

Specifications

Document Set
logistics
Industry
logistics
Source Kit
inventory-manufacturing
Synthetic
true
As Of
2026-09-08
Rows
16
Encoding
UTF-8
Top Level
object

Testing contract

Expected to pass
Scenario
Parse the JSON export and assert the totals object against the shipment array.
Expected result
shipments has 16 members; summing parcels gives 56 and summing gross_weight_kg gives 683.200, both 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

“Pine Assembly Workshop: Shipment 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: 16 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("shipments-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.