Xarray

2026.2.0 · active · verified Sun Apr 05

Xarray (pronounced 'ex-array') is an open-source Python package that simplifies working with labelled multi-dimensional arrays and datasets. It introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, enabling a more intuitive and less error-prone experience for scientific computing and data analysis, particularly for earth sciences. As of February 2026, the current version is 2026.2.0. Xarray maintains a regular release cadence, with minor versions typically released monthly or bi-monthly.

Warnings

breaking Default attribute preservation behavior changed in `xarray` v2025.11.0. All operations now preserve attributes by default. Previously, attributes were dropped unless `keep_attrs=True` was explicitly set. Binary operations now combine attributes using `drop_conflicts` instead of keeping only the left operand's attributes.
Fix: Review code that relies on attributes being dropped by default. If the old behavior is desired, explicitly set `keep_attrs=False` or manually manage attributes.
breaking Direct application of certain NumPy ufuncs to `xarray.DataArray` objects may now raise `NotImplementedError` due to the `__array_ufunc__` protocol. This affects ufuncs that previously implicitly converted `DataArray` to a NumPy array.
Fix: Explicitly convert the `DataArray` to a NumPy array using `.values` before applying the NumPy ufunc (e.g., `np.add.reduce(da.values)`).
breaking The behavior of `Dataset.identical()`, `DataArray.identical()`, and `testing.assert_identical()` changed to include comparison of indexes. Two objects with identical data but different indexes will no longer be considered identical.
Fix: If only data identity is required, compare the underlying data (`.values` or by casting to `pandas.DataFrame`). Adjust identity checks if index differences are now considered relevant.

Install

pip install xarray Core installation
pip install "xarray[complete]" Full installation with all optional dependencies

Imports

DataArray

import xarray as xr; da = xr.DataArray(...)

Dataset

import xarray as xr; ds = xr.Dataset(...)

xray
```
import xarray as xr
```
The library was renamed from 'xray' to 'xarray' in January 2016. The 'xray' module no longer exists.

Quickstart

This quickstart demonstrates the creation of a basic `DataArray` and a `Dataset`, including dimension names, coordinates, and attributes. It then shows how to perform a simple aggregation (mean) on a variable within the `Dataset` along a specified dimension.

import xarray as xr
import numpy as np
import pandas as pd

# Create a DataArray
data_array = xr.DataArray(
    np.random.rand(2, 3),
    coords={"x": [10, 20], "y": ["a", "b", "c"]},
    dims=("x", "y"),
    name="random_data"
)

# Create a Dataset with two DataArrays sharing coordinates
temp = xr.DataArray(
    25 + 10 * np.random.randn(2, 3, 4),
    coords={
        "time": pd.to_datetime(["2026-01-01", "2026-01-02"]),
        "lat": [40, 50],
        "lon": [100, 110, 120, 130]
    },
    dims=("time", "lat", "lon"),
    name="temperature",
    attrs={"units": "Celsius", "long_name": "Air Temperature"}
)

precip = xr.DataArray(
    5 * np.random.rand(2, 3, 4),
    coords=temp.coords, # Share coordinates from temp
    dims=temp.dims,
    name="precipitation",
    attrs={"units": "mm", "long_name": "Precipitation Rate"}
)

dataset = xr.Dataset({"temp": temp, "precip": precip})

# Perform a simple operation (e.g., mean over 'time' dimension)
mean_temp = dataset["temp"].mean(dim="time")

print("DataArray:\n", data_array)
print("\nDataset:\n", dataset)
print("\nMean temperature over time:\n", mean_temp)

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