Time-series — Mixed Tz
Same nominal instant expressed in Z, -05:00, and +09:00 offsets.
[
{
"ts": "2026-02-03T08:00:00Z",
"v": 1
},
{
"ts": "2026-02-03T08:00:00-05:00",
"v": 2
},
{
"ts": "2026-02-03T08:00:00+09:00",
"v": 3
}
]
Specifications
- Edge
- mixed-tz
- Rows
- 3
- Seed
- 42042
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
“Time-series — Mixed Tz” is a deterministic Novus Examples fixture for Time-series data, Data import, CSV parsing. Irregular timestamps, DST gaps, and duplicate keys for time-series importers and charting libraries.
Documented properties for this file: seed 42042 · 3 rows. 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.
Valid and intentionally invalid siblings are labelled in title and description. Assert parsers accept the valid twin and fail loudly on the invalid one; for time series, check DST gaps and duplicate keys against the spec table.
Code examples
import json
with open("mixed-timezones.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- csvTime-series — Business HoursHourly order counts weekdays only (09–16 UTC) across three SAMPLE days.
- csvTime-series — DST Fall Back Duplicate Hour (CSV)Load readings with a repeated 01:00 local hour on fall-back DST — tests ambiguous timestamp handling.
- csvTime-series — DST Spring Forward Gap (CSV)Hourly load data skipping the 03:00 hour on DST spring-forward day — tests timezone/DST gap handling.
- csvTime-series — Duplicate Timestamp Keys (CSV)Two rows share the same timestamp key — tests deduplication and last-write-wins policies.
- jsonTime-series — Duplicate Timestamp Keys (JSON)JSON array with duplicate timestamp keys for ingestion pipeline tests.
- jsonTime-series — Epoch BoundaryTimestamps at Unix epoch and classic 32-bit Y2038 boundary.
Generated by generation/data_wave_f.py. Free for any use, no attribution required — license.