Hypothesis-awkward

Hypothesis strategies for Awkward Array.

Awkward Array represents deeply nested, variable-length, and mixed-type data — the kind of irregular structure common in scientific datasets. Its valid arrays therefore span a vast combinatorial space, and test data written by hand covers only a small corner of it. The edge cases that break code tend to hide in the parts no one thought to write down.

Property-based testing addresses this. Instead of asserting specific outputs for hand-picked inputs, you assert properties that should hold for any valid input and let the framework generate the inputs. Hypothesis is a property-based testing library for Python: its strategies are composable objects that describe how to build test data, and when a test fails Hypothesis shrinks it, searching for a minimal sample that still triggers the failure.

This package, hypothesis-awkward, brings property-based testing to Awkward Array with a collection of strategies for generating Awkward Arrays. Its main strategy, arrays(), generates nearly fully general Awkward Arrays: called with no arguments, it produces nested, variable-length, record, and union layouts; leaf values of any NumPy dtype Awkward Array supports, as well as strings and bytestrings; optional, masked, and missing values; and virtual arrays — with options to constrain any of these. The goal is full generality, so these strategies can surface edge cases in tools that use Awkward Array, and in Awkward Array itself.

Installation

You can install the package from PyPI using pip:

pip install hypothesis-awkward

This also installs Hypothesis and Awkward Array as dependencies unless they are already installed.

The strategy arrays()

The function arrays() is the main strategy. It generates Awkward Arrays with many options to control the output arrays.

Sample outputs of arrays()

You can see sample outputs of the current version of arrays() in the test case:

from hypothesis import given

import awkward as ak
import hypothesis_awkward.strategies as st_ak


@given(array=st_ak.constructors.arrays())
def test_array(array: ak.Array) -> None:
    print(f'{array=!r}')

For example, this might print:

array=<Array ['', '\U000c2f9f', ..., '@ú\x94j\U000c4364e'] type='4 * string'>
array=<Array [[], [], None, [], ..., [], [], None] type='42 * option[var * ?bytes]'>
array=<Array [??, ??, ??, ??, ??, ??] type='6 * var * unknown'>
array=<Array [[], [], [], [], [], [], [], []] type='8 * var * string'>
array=<Array [??, ??, ??, ??, ??, ??, ??, ??] type='8 * var * string'>
array=<Array [b'O\x01\x14\xecE\xdb_'] type='1 * bytes'>
array=<Array [??, ??] type='2 * var * bytes'>
array=<Array [None] type='1 * ?bytes'>
array=<Array [??, ??, ??, ??] type='4 * string'>
array=<Array [NaT, NaT, ..., -9223372036854773681] type='26 * datetime64[Y]'>
array=<Array [[??, ??], [??, ??], ..., [??, ??]] type='8 * 2 * var * timedelta64[fs]'>
array=<Array [[[[], [], [], [], []]]] type='1 * 1 * var * var * timedelta64[fs]'>
array=<Array [[[[[], [], [], [], []]]]] type='1 * 1 * 1 * var * var * var * bool'>
array=<Array [[16996], [10841], ..., [10841], None] type='7 * option[1 * uint16]'>
array=<Array [[0]] type='1 * option[1 * uint16]'>
array=<Array [??] type='1 * option[1 * uint16]'>
array=<Array [[None]] type='1 * 1 * option[1 * option[var * int16]]'>
array=<Array [[]] type='1 * option[var * 0 * union[timedelta64[D], 0 * unknown]]'>
array=<Array [??, ??] type='2 * datetime64[D]'>
array=<Array [??, ??, ??, ??] type='4 * ?timedelta64[us]'>
array=<Array [??, ??, ??, ??, ??, ??, ..., ??, ??, ??, ??, ??, ??] type='14 * bytes'>
array=<Array [[], [], [], [], ..., [], [], [], []] type='55 * option[var * var * ...'>
array=<Array [0.0, inf, 0.0, nan, 0.0] type='5 * float16'>
array=<Array [None, -768614336404561008-11, ..., None] type='6 * ?datetime64[M]'>
array=<Array [??, ??] type='2 * option[var * 1 * string]'>

In the type strings above, a ? marks an option type (e.g., ?int64), whose missing values print as None. Virtual arrays print as ??.

The options of arrays()

The strategy arrays() has many options to control the output arrays. You can find all options in the API reference:

• API reference: arrays()

Other strategies

In addition to arrays(), this package includes other strategies that generate Awkward Arrays and related data types, which can be found in the API reference:

• API reference

Citation

The citation for Hypothesis-awkward is as follows:

Tai Sakuma, Ianna Osborne, and Peter Elmer, Hypothesis-awkward: Property-Based Testing Strategies for Awkward Array, arXiv:2609.31820 (2026).