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‎.github/labeler.yml

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Automation:
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- changed-files:
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- any-glob-to-any-file:
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- .github/**
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CI:
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- changed-files:
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- any-glob-to-any-file:
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- ci/**
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dependencies:
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- changed-files:
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- any-glob-to-any-file:
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- ci/requirements/*
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topic-arrays:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/core/duck_array_ops.py
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topic-backends:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/backends/**
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topic-cftime:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/coding/*time*
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topic-CF conventions:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/conventions.py
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topic-dask:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/compat/dask*
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- xarray/core/parallel.py
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topic-DataTree:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/core/datatree*
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topic-documentation:
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- changed-files:
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- any-glob-to-any-file:
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- doc/*
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- "!doc/whats-new.rst"
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- doc/**/*
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topic-groupby:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/core/groupby.py
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topic-html-repr:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/core/formatting_html.py
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topic-hypothesis:
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- changed-files:
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- any-glob-to-any-file:
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- properties/**
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- xarray/testing/strategies.py
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topic-indexing:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/core/indexes.py
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- xarray/core/indexing.py
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topic-NamedArray:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/namedarray/*
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topic-performance:
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- changed-files:
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- any-glob-to-any-file:
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- asv_bench/benchmarks/**
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topic-plotting:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/plot/*
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- xarray/plot/**/*
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topic-rolling:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/computation/rolling.py
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- xarray/computation/rolling_exp.py
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topic-testing:
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- changed-files:
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- any-glob-to-any-file:
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- conftest.py
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- xarray/testing/*
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topic-typing:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/core/types.py
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topic-zarr:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/backends/zarr.py
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io:
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- changed-files:
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- any-glob-to-any-file:
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- xarray/backends/**

‎.github/workflows/benchmarks.yml

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jobs:
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benchmark:
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if: ${{ contains( github.event.pull_request.labels.*.name, 'run-benchmark') && github.event_name == 'pull_request' || github.event_name == 'workflow_dispatch' }}
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if: ${{ contains( github.event.pull_request.labels.*.name, 'run-benchmark') && github.event_name == 'pull_request' || contains( github.event.pull_request.labels.*.name, 'topic-performance') && github.event_name == 'pull_request' || github.event_name == 'workflow_dispatch' }}
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name: Linux
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runs-on: ubuntu-20.04
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env:

‎.github/workflows/label-prs.yml

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name: "PR Labeler"
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on:
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- pull_request_target
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jobs:
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label:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/labeler@v5
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with:
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repo-token: "${{ secrets.GITHUB_TOKEN }}"
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sync-labels: false

‎.github/workflows/nightly-wheels.yml

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fi
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- name: Upload wheel
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uses: scientific-python/upload-nightly-action@82396a2ed4269ba06c6b2988bb4fd568ef3c3d6b # 0.6.1
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uses: scientific-python/upload-nightly-action@b36e8c0c10dbcfd2e05bf95f17ef8c14fd708dbf # 0.6.2
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with:
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anaconda_nightly_upload_token: ${{ secrets.ANACONDA_NIGHTLY }}
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artifacts_path: dist

‎.pre-commit-config.yaml

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- id: text-unicode-replacement-char
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- repo: https://github.com/astral-sh/ruff-pre-commit
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# Ruff version.
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rev: v0.9.9
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rev: v0.11.4
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hooks:
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- id: ruff-format
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- id: ruff
@@ -69,12 +69,12 @@ repos:
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- id: taplo-format
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args: ["--option", "array_auto_collapse=false"]
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- repo: https://github.com/abravalheri/validate-pyproject
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rev: v0.23
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rev: v0.24.1
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hooks:
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- id: validate-pyproject
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additional_dependencies: ["validate-pyproject-schema-store[all]"]
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- repo: https://github.com/crate-ci/typos
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rev: dictgen-v0.3.1
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rev: v1
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hooks:
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- id: typos
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# https://github.com/crate-ci/typos/issues/347

‎asv_bench/benchmarks/repr.py

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self.ds._repr_html_()
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class ReprDataTree:
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def setup(self):
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# construct a datatree with 500 nodes
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number_of_files = 20
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number_of_groups = 25
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tree_dict = {}
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for f in range(number_of_files):
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for g in range(number_of_groups):
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tree_dict[f"file_{f}/group_{g}"] = xr.Dataset({"g": f * g})
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self.dt = xr.DataTree.from_dict(tree_dict)
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def time_repr(self):
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repr(self.dt)
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def time_repr_html(self):
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self.dt._repr_html_()
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class ReprMultiIndex:
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def setup(self):
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index = pd.MultiIndex.from_product(

‎ci/install-upstream-wheels.sh

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git+https://github.com/dask/dask \
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git+https://github.com/dask/dask-expr \
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git+https://github.com/dask/distributed \
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git+https://github.com/zarr-developers/zarr \
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git+https://github.com/zarr-developers/zarr-python \
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git+https://github.com/Unidata/cftime \
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git+https://github.com/pypa/packaging \
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git+https://github.com/hgrecco/pint \

‎ci/requirements/min-all-deps.yml

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- pandas=2.1
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- pint=0.22
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- pip
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- pydap=3.4
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- pydap=3.5.0
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- pytest
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- pytest-cov
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- pytest-env

‎design_notes/flexible_indexes_notes.md

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Like for the indexes, explicit coordinate creation should be preferred over implicit coordinate creation. However, there may be some situations where we would like to keep creating coordinates implicitly for backwards compatibility.
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For example, it is currently possible to pass a `pandas.MulitIndex` object as a coordinate to the Dataset/DataArray constructor:
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For example, it is currently possible to pass a `pandas.MultiIndex` object as a coordinate to the Dataset/DataArray constructor:
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```python
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>>> midx = pd.MultiIndex.from_arrays([['a', 'b'], [0, 1]], names=['lvl1', 'lvl2'])

‎doc/api-hidden.rst

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Index.rename
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Index.copy
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indexes.RangeIndex.start
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indexes.RangeIndex.stop
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indexes.RangeIndex.step
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backends.NetCDF4DataStore.close
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backends.NetCDF4DataStore.encode
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backends.NetCDF4DataStore.encode_attribute

‎doc/api.rst

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:toctree: generated/
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CFTimeIndex
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indexes.RangeIndex
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Creating custom indexes
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-----------------------
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cftime_range
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date_range
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date_range_like
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indexes.RangeIndex.arange
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indexes.RangeIndex.linspace
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Tutorial
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========

‎doc/getting-started-guide/faq.rst

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:header: "File Format", "Open via", " Related Packages"
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:widths: 15, 45, 15
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"NetCDF (.nc, .nc4, .cdf)","``open_dataset()`` OR ``open_mfdataset()``", "`netCDF4 <https://pypi.org/project/netCDF4/>`_, `netcdf <https://pypi.org/project/netcdf/>`_ , `cdms2 <https://cdms.readthedocs.io/en/latest/cdms2.html>`_"
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"NetCDF (.nc, .nc4, .cdf)","``open_dataset()`` OR ``open_mfdataset()``", "`netCDF4 <https://pypi.org/project/netCDF4/>`_, `cdms2 <https://cdms.readthedocs.io/en/latest/cdms2.html>`_"
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"HDF5 (.h5, .hdf5)","``open_dataset()`` OR ``open_mfdataset()``", "`h5py <https://www.h5py.org/>`_, `pytables <https://www.pytables.org/>`_ "
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"GRIB (.grb, .grib)", "``open_dataset()``", "`cfgrib <https://pypi.org/project/cfgrib/>`_, `pygrib <https://pypi.org/project/pygrib/>`_"
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"CSV (.csv)","``open_dataset()``", "`pandas`_ , `dask <https://www.dask.org/>`_"

‎doc/getting-started-guide/quick-overview.rst

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data[:-1] - data[:1]
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For more, see :ref:`comput`.
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For more, see :ref:`compute`.
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GroupBy
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-------

‎doc/internals/how-to-create-custom-index.rst

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coordinates.
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For example, :py:class:`~xarray.core.indexes.PandasIndex` accepts only one coordinate and
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:py:class:`~xarray.core.indexes.PandasMultiIndex` accepts one or more 1-dimensional coordinates that must all
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For example, :py:class:`~xarray.indexes.PandasIndex` accepts only one coordinate and
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:py:class:`~xarray.indexes.PandasMultiIndex` accepts one or more 1-dimensional coordinates that must all
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‎doc/internals/internal-design.rst

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The simplest data structure used by most users is :py:class:`~xarray.DataArray`.
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A :py:class:`~xarray.DataArray` is a composite object consisting of multiple
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:py:class:`~xarray.core.variable.Variable` objects which store related data.
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:py:class:`~xarray.Variable` objects which store related data.
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A single :py:class:`~xarray.core.Variable` is referred to as the "data variable", and stored under the :py:attr:`~xarray.DataArray.variable`` attribute.
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A single :py:class:`~xarray.Variable` is referred to as the "data variable", and stored under the :py:attr:`~xarray.DataArray.variable`` attribute.
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A :py:class:`~xarray.DataArray` inherits all of the properties of this data variable, i.e. ``dims``, ``data``, ``attrs`` and ``encoding``,
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all of which are implemented by forwarding on to the underlying ``Variable`` object.
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variable but is stored on the wrapping ``DataArray`` class.
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The ``name`` attribute is primarily used when one or more :py:class:`~xarray.DataArray` objects are promoted into a :py:class:`~xarray.Dataset`
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(e.g. via :py:meth:`~xarray.DataArray.to_dataset`).
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Note that the underlying :py:class:`~xarray.core.Variable` objects are all unnamed, so they can always be referred to uniquely via a
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Note that the underlying :py:class:`~xarray.Variable` objects are all unnamed, so they can always be referred to uniquely via a
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dict-like mapping.
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.. _internal design.dataset:

‎doc/internals/interoperability.rst

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- :ref:`Custom file backends <add_a_backend>` via the :py:class:`~xarray.backends.BackendEntrypoint` system,
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- Numpy-like :ref:`"duck" array wrapping <internals.duckarrays>`, which supports the `Python Array API Standard <https://data-apis.org/array-api/latest/>`_,
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- :ref:`Chunked distributed array computation <internals.chunkedarrays>` via the :py:class:`~xarray.core.parallelcompat.ChunkManagerEntrypoint` system,
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- :ref:`Chunked distributed array computation <internals.chunkedarrays>` via the :py:class:`~xarray.namedarray.parallelcompat.ChunkManagerEntrypoint` system,
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- Custom :py:class:`~xarray.Index` objects for :ref:`flexible label-based lookups <internals.custom indexes>`,
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- Extending xarray objects with domain-specific methods via :ref:`custom accessors <internals.accessors>`.
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‎doc/internals/time-coding.rst

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.. note::
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Note that in the future the default value of ``decode_timedelta`` will be
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``False`` rather than ``None``.
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.. ipython:: python
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:suppress:
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# Cleanup
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import os
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for f in [
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"test-datetimes1.nc",
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"test-datetimes2.nc",
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"test-timedeltas1.nc",
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"test-timedeltas2.nc",
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]:
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if os.path.exists(f):
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os.remove(f)

‎doc/user-guide/combining.rst

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Concatenate
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~~~~~~~~~~~
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To combine :py:class:`~xarray.Dataset` / :py:class:`~xarray.DataArray` objects along an existing or new dimension
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into a larger object, you can use :py:func:`~xarray.concat`. ``concat``
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takes an iterable of ``DataArray`` or ``Dataset`` objects, as well as a
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‎doc/user-guide/computation.rst

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.. currentmodule:: xarray
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.. _comput:
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.. _compute:
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###########
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Computation
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If desired, you can disable this behavior by invoking the aggregation method
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.. _comput.rolling:
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.. _compute.rolling:
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Rolling window operations
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=========================
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# arr_window is a view of x
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...
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.. _comput.rolling_exp:
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.. _compute.rolling_exp:
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While ``rolling`` provides a simple moving average, ``DataArray`` also supports
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To avoid this, use ``skipna=False`` as the above example.
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.. _comput.weighted:
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.. _compute.weighted:
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Weighted array reductions
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=========================
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Similarly to index based alignment, the result has the intersection of all
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.. _comput.wrapping-custom:
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.. _compute.wrapping-custom:
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Wrapping custom computation
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===========================

‎doc/user-guide/dask.rst

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The only aspect of this example that is different from standard usage of
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``apply_ufunc()`` is that we needed to supply the ``output_dtypes`` arguments.
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(Read up on :ref:`comput.wrapping-custom` for an explanation of the
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(Read up on :ref:`compute.wrapping-custom` for an explanation of the
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"core dimensions" listed in ``input_core_dims``.)
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Our new ``spearman_correlation()`` function achieves near linear speedup

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