N-Dimensional Array Generator
Create nested numeric arrays for testing tensor and data-processing code.
Also known as: multidimensional array · tensor data
seeded
Output
About this tool, tips & examples
What it does
The N-Dimensional Array Generator produces nested numeric arrays of any
practical shape — enter dimensions like 3,4,5 and get a 3×4×5 tensor of
values drawn from your min/max range at chosen precision. Seeded output
regenerates identically, and JSON export drops straight into NumPy,
TensorFlow, or JavaScript test code.
Common use cases
- Tensor fixtures — known-shape inputs for testing ML preprocessing, reshaping, and broadcasting logic.
- API testing — nested-array payloads for endpoints that accept multidimensional data.
- Data-science teaching — concrete examples of shapes, axes, and ranks that students can inspect by eye.
- Serialization tests — deeply nested numeric structures for JSON parsers and schema validators.
Settings
- Dimensions — comma-separated sizes, e.g.
10(vector),3,3(matrix),2,4,8(3-D tensor). - Minimum / Maximum — the value range.
- Decimal places — 0 to 10; zero yields integer tensors.
- Seed — identical seed + settings = identical tensor, so numeric tests can assert exact values.
Privacy note
Arrays are computed locally in your browser and never uploaded. Values are uniform random draws — synthetic numbers with no meaning beyond your test.
FAQ
How do I load the output into NumPy?
Export JSON and np.array(json.load(f)) — the nested lists convert
directly, with the shape you specified.
Is there a size limit? Total element count is what matters: 100×100×100 is a million values. Keep test fixtures small enough to diff; generate big tensors only for performance runs.
Just need 2-D or 1-D? The Random Matrix generator does rectangular matrices with CSV export; Numeric Series covers single sequences with more distribution options.