Weather Forecast — 30 days (JSON, 30 records)
The daily forecast as a JSON array — the format twin of the CSV, for import and charting tests.
[
{
"date": "2026-03-01",
"city": "Lisbon",
"temp_high_c": 8.7,
"temp_low_c": 4.6,
"condition": "showers",
"precip_mm": 6.0,
"wind_kph": 24.6,
"humidity_pct": 77
},
{
"date": "2026-03-02",
"city": "Lisbon",
"temp_high_c": 14.8,
"temp_low_c": 10.3,
"condition": "snow",
"precip_mm": 4.6,
"wind_kph": 43.0,
"humidity_pct": 92
},
{
"date": "2026-03-03",
"city": "Lisbon",
"temp_high_c": 12.2,
"temp_low_c": 4.3,
"condition": "rain",
"precip_mm": 7.0,
"wind_kph": 27.5,
"humidity_pct": 74
},
{
"date": "2026-03-04",
"city": "Lisbon",
"temp_high_c": 15.5,
"temp_low_c": 10.9,
"condition": "showers",
"precip_mm": 0.0,
"wind_kph": 20.9,
"humidity_pct": 48
},
{
"date": "2026-03-05",
"city": "Lisbon",
"temp_high_c": 10.2,
"temp_low_c": 5.6,
"condition": "cloudy",
"precip_mm": 0.0,
"wind_kph": 7.3,
"humidity_pct": 65Specifications
- Records
- 30
- Schema
- date, city, temp_high_c, temp_low_c, condition, precip_mm, wind_kph, humidity_pct
- Domain
- weather
- Seed
- 1104
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
“Weather Forecast — 30 days (JSON, 30 records)” is a deterministic Novus Examples fixture for Data import, Time-series data, Conversion testing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: seed 1104 · 30 records · schema: date, city, temp_high_c, temp_low_c, condition, precip_mm, wind_kph, humidity_pct. 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("forecast.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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Generated by generation/data_domains.py. Free for any use, no attribution required — license.