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Novus Examples
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Flat JSON Array

A flat JSON array of ten simple objects — the baseline case for JSON parsing and mapping.

JSON parsingData import
Preview — first 50 linesjson
[
  {
    "id": 1,
    "name": "Item 1",
    "active": false,
    "price": 1.25
  },
  {
    "id": 2,
    "name": "Item 2",
    "active": true,
    "price": 2.5
  },
  {
    "id": 3,
    "name": "Item 3",
    "active": false,
    "price": 3.75
  },
  {
    "id": 4,
    "name": "Item 4",
    "active": true,
    "price": 5.0
  },
  {
    "id": 5,
    "name": "Item 5",
    "active": false,
    "price": 6.25
  },
  {
    "id": 6,
    "name": "Item 6",
    "active": true,
    "price": 7.5
  },
  {
    "id": 7,
    "name": "Item 7",
    "active": false,
    "price": 8.75
  },
  {
    "id": 8,
    "name": "Item 8",
    "active": true,
    "price": 10.0
  },
  {
… 63 lines total — download for the full file.

Specifications

Structure
flat array of objects
Records
10
Valid
true

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

“Flat JSON Array” is a deterministic Novus Examples fixture for JSON parsing, Data import. Flat, deeply nested, JSON Lines, and intentionally invalid JSON for testing parsers and error handling.

Documented properties for this file: 10 records · flat array of objects. 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.

Feed the file to your parser and assert it handles the documented quirks — quoted delimiters, embedded newlines, ragged rows, or invalid syntax; the valid↔invalid distinction is labelled in the title.

Code examples

import json

with open("flat.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

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