Xarray Data Validation¶

xarray provides labelled multi-dimensional arrays DataArray, collections of aligned arrays Dataset, and collections of datasets with DataTree.

Pandera validates them with the same patterns as the other dataframe backends: schema objects, optional Check instances, and global configuration.

Installation¶

pip install 'pandera[xarray]'

Quick start¶

import numpy as np
import xarray as xr
import pandera.xarray as pa

schema = pa.DatasetSchema(
    data_vars={
        "temperature": pa.DataVar(dtype=np.float64, dims=("x", "y")),
        "pressure": pa.DataVar(dtype=np.float64, dims=("x", "y")),
    },
    coords={"x": pa.Coordinate(dtype=np.float64)},
)

ds = xr.Dataset(
    {
        "temperature": (("x", "y"), np.random.rand(3, 4)),
        "pressure": (("x", "y"), np.random.rand(3, 4)),
    },
    coords={"x": np.arange(3, dtype=np.float64)},
)
schema.validate(ds)

<xarray.Dataset> Size: 216B
Dimensions:      (x: 3, y: 4)
Coordinates:
  * x            (x) float64 24B 0.0 1.0 2.0
Dimensions without coordinates: y
Data variables:
    temperature  (x, y) float64 96B 0.477 0.4206 0.4485 ... 0.718 0.4396 0.5664
    pressure     (x, y) float64 96B 0.4769 0.4384 0.2959 ... 0.05203 0.6396 0.49

Dataset Model¶

from pandera.typing.xarray import Coordinate

class Surface(pa.DatasetModel):
    temperature: np.float64 = pa.Field(dims=("x", "y"))
    pressure: np.float64 = pa.Field(dims=("x", "y"))
    x: Coordinate[np.float64]

Surface.validate(ds)

<xarray.Dataset> Size: 216B
Dimensions:      (x: 3, y: 4)
Coordinates:
  * x            (x) float64 24B 0.0 1.0 2.0
Dimensions without coordinates: y
Data variables:
    temperature  (x, y) float64 96B 0.477 0.4206 0.4485 ... 0.718 0.4396 0.5664
    pressure     (x, y) float64 96B 0.4769 0.4384 0.2959 ... 0.05203 0.6396 0.49

Guide contents¶

DataArray Schemas — validating a single DataArray
Dataset Schemas — validating a Dataset with DataVar and Coordinate
DataTree Validation — validating a DataTree hierarchy
Data Models — class-based DataArrayModel, DatasetModel, and DataTreeModel
Checks and Parsers — checks, parsers, and lazy validation
Decorators — check_input, check_output, check_io, and check_types
Configuration — validation depth, Dask, and environment variables
Dask and Duck Arrays — chunked, array_type, validation depth, and lazy data checks
Encoding Validation — validate .encoding dicts on DataArrays, DataVars, and Datasets
Error reports and lazy validation — SchemaError / SchemaErrors, lazy validation, and failure cases
CF Convention Checks — CF standard name, units, and cf_xarray checks
Schema Inference — automatically infer schemas from data
IO Serialization — save and load schemas as YAML or JSON
Hypothesis Data Strategies — generate synthetic data with Hypothesis