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Pine Property Services: Job cost report JSON

Job cost report JSON for Pine Property Services. The same 12 work orders as job-cost-report.csv with the margin rule stated explicitly. totals.variance is 1020.00, which is 85.00 times the 12 work orders.

json

application/json

6.1 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
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Parse the report and test the stated margin rule against every work order.
Expected result
workOrders has 12 members and every one satisfies the rule: estimate_labour minus actual_labour_cost is 65.00 and estimate_materials minus actual_materials is 20.00 throughout, so the variance column is 85.00 on all 12 and totals 1020.00.

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: Job cost report 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("job-cost-report.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.