Time-series — Irregular Intervals (CSV)
Sensor readings at irregular minute gaps — tests time-series resampling and gap detection.
ts,sensor,value
2026-03-08T00:00:00Z,SAMPLE-A,10.0
2026-03-08T00:05:00Z,SAMPLE-A,11.7
2026-03-08T00:07:00Z,SAMPLE-A,13.4
2026-03-08T00:20:00Z,SAMPLE-A,15.1
2026-03-08T00:21:00Z,SAMPLE-A,16.8
2026-03-08T00:45:00Z,SAMPLE-A,18.5
2026-03-08T01:30:00Z,SAMPLE-A,20.2
2026-03-08T02:00:00Z,SAMPLE-A,21.9
Specifications
- Rows
- 8
- Edge
- irregular timestamps
- Seed
- 42042
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“Time-series — Irregular Intervals (CSV)” 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 · 8 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 pandas as pd
df = pd.read_csv("irregular-intervals.csv")
print(df.head())
print(df.dtypes)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.