NumPy .npy — datetime64 Seconds Since the Epoch (.npy)
Five timestamps stored as datetime64 with second resolution, where the unit is part of the dtype descriptor and the values on disk are plain int64 epoch seconds. Dropping the unit turns 2026-01-01 into the integer 1767225600 without any complaint.
| Index | Value | Stored int64 |
|---|---|---|
| 0 | 2026-01-01T00:00:00 | 1767225600 |
| 1 | 2026-01-01T00:30:00 | 1767227400 |
| 2 | 2026-01-01T01:00:00 | 1767229200 |
| 3 | 2026-06-30T23:59:59 | 1782863999 |
| 4 | 2026-12-31T23:59:59 | 1798761599 |
Specifications
- Dtype
- datetime64[s]
- Elements
- 5
- Descr
- <M8[s]
- Storage
- int64 seconds since 1970-01-01T00:00:00
- Timezone
- naive (no offset stored)
- First Value
- 2026-01-01T00:00:00
Testing contract
Expected to pass- Scenario
- Parse the descriptor to recover the datetime unit, then decode the underlying int64 values to timestamps.
- Expected result
- The descr reads '<M8[s]', element 0 decodes to 2026-01-01T00:00:00 from the stored integer 1767225600, and no timezone offset is present anywhere in the file.
What is a .npy file?
NPY is NumPy's native binary format for a single array. A short header records the dtype, shape, and memory order, followed by the raw array bytes, so an array round-trips exactly without any text parsing. It is the standard way to persist embeddings, tensors, and numeric matrices in the Python data stack.
How to use this file
Use an example .npy to test array loaders (numpy.load), tensor and embedding pipelines, and converters between .npy, JSON, and columnar formats like Parquet.
How to use this file for testing
“NumPy .npy — datetime64 Seconds Since the Epoch (.npy)” is a deterministic Novus Examples fixture for Scientific data, Serialization testing, Time-series data. Citation catalogs (BibTeX, RIS), chemistry structures (MDL Molfile, PDB), and gridded binary data (NetCDF, FITS) — for testing reference managers, molecule viewers, and scientific-data loaders.
Documented properties for this file: NPY · 168 bytes. 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.
Scientific fixtures are small, valid, and fully synthetic — no real organism, patient, sample, or observation. Point your parser or loader at the file and check it reads the documented records, variables, or headers; binary formats ship a readable twin or metadata listing for comparison.
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