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Pine Property Services: Work order export JSON

Work order export JSON for Pine Property Services. The same 12 work orders as work-order-export.csv with a byProperty rollup. Each of the 6 properties carries exactly 2 work orders and the totalCost figures sum to 2838.00.

json

application/json

5.9 KB
Document Set
construction
Industry
trades-property
Source Kit
trades-property
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
construction
Industry
trades-property
Source Kit
trades-property
Synthetic
true
As Of
2026-09-08
Rows
12
Properties
6
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Parse the export and rebuild the byProperty rollup from the workOrders array.
Expected result
Grouping the 12 work orders by property_id reproduces all 6 byProperty members exactly, each with 2 work orders, and the totalCost values sum to 2838.00.

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 Property Services: Work 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("work-order-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.