Hypothesis Data Strategies

Generate synthetic data conforming to a schema using the hypothesis library. Install:

pip install 'pandera[strategies]'

DataArray strategies

import numpy as np
import pandera.xarray as pa
from pandera.strategies.xarray_strategies import (
    data_array_strategy,
    data_array_schema_strategy,
    dataset_strategy,
    dataset_schema_strategy,
)

schema = pa.DataArraySchema(
    dtype="float64",
    dims=("x", "y"),
    sizes={"x": 3, "y": 4},
    name="temp",
)

Use data_array_schema_strategy inside a Hypothesis test:

from hypothesis import given, settings

@given(data_array_schema_strategy(schema))
@settings(max_examples=10)
def test_generated_data(da):
    assert da.dims == ("x", "y")
    assert da.sizes["x"] == 3
    schema.validate(da)

test_generated_data()

Dataset strategies

ds_schema = pa.DatasetSchema(
    data_vars={
        "temperature": pa.DataVar(dtype="float64", dims=("x", "y")),
        "pressure": pa.DataVar(dtype="float64", dims=("x", "y")),
    },
    sizes={"x": 3, "y": 4},
)

@given(dataset_schema_strategy(ds_schema))
@settings(max_examples=10)
def test_generated_dataset(ds):
    assert "temperature" in ds.data_vars
    assert "pressure" in ds.data_vars
    ds_schema.validate(ds)

test_generated_dataset()

Low-level strategies

For more control, use the building blocks directly:

from pandera.strategies.xarray_strategies import (
    xarray_dtype_strategy,
    data_array_strategy,
    dataset_strategy,
)

# Generate float64 scalars
xarray_dtype_strategy("float64")

# Generate DataArrays with explicit settings
data_array_strategy(
    dtype="int32",
    dims=("time", "lat"),
    sizes={"time": 10, "lat": 180},
    coords={"time": {"dtype": "float64"}},
    name="obs",
)

# Generate Datasets
dataset_strategy(
    data_vars={
        "a": {"dtype": "float64", "dims": ("x",)},
        "b": {"dtype": "int32", "dims": ("x", "y")},
    },
    sizes={"x": 5, "y": 10},
)

Limitations

The xarray strategies currently generate data based on structural properties only — dtype, dims, sizes, coords, name, and nullable. They do not yet incorporate Check constraints when synthesizing values. For example, a schema with Check.in_range(0, 1) will produce arrays with arbitrary floats rather than values restricted to [0, 1].

This means generated data may not pass schema.validate() when the schema includes value-level checks. For now you can work around this by either:

• Adding a hypothesis.assume(...) filter in your test, or

• Using hypothesis.strategies.floats(min_value=..., max_value=...) with the low-level data_array_strategy to manually constrain the element domain.

Check-aware data generation (mirroring the pandas strategies integration) is planned for a future release.

Dask Integration

Chunked (Dask-backed) xarray objects are validated with structural checks by default (dtype, dims, sizes, coords) without triggering compute(). See Configuration for how to enable data-level checks on lazy arrays.