diff --git a/janitor/utils.py b/janitor/utils.py
index 8de607651..c3939b7b1 100644
--- a/janitor/utils.py
+++ b/janitor/utils.py
@@ -144,7 +144,8 @@ def import_message(
 
     Generic message for indicating to the user when a function relies on an
     optional module / package that is not currently installed. Includes
-    installation instructions. Used in `chemistry.py` and `biology.py`.
+    installation instructions. Used in `chemistry.py`, `biology.py`, and
+    `xarray/functions.py`.
 
     Args:
         submodule: `pyjanitor` submodule that needs an external dependency.
diff --git a/janitor/xarray/functions.py b/janitor/xarray/functions.py
index da6a2e037..0af5c8345 100644
--- a/janitor/xarray/functions.py
+++ b/janitor/xarray/functions.py
@@ -9,6 +9,18 @@
 import pandas_flavor as pf
 import xarray as xr
 
+from ..utils import import_message
+
+try:
+    import sparse
+except ImportError:
+    import_message(
+        submodule="xarray",
+        package="sparse",
+        conda_channel="conda-forge",
+        pip_install=True,
+    )
+
 
 @pf.register_xarray_dataarray_method
 def clone_using(
@@ -148,3 +160,117 @@ def convert_datetime_to_number(
     times = da_or_ds.coords[dim].data / np.timedelta64(1, time_units)
 
     return da_or_ds.assign_coords({dim: times})
+
+
+@pf.register_xarray_dataarray_method
+def to_scipy_sparse(da: xr.DataArray):
+    """
+    Convert a 2-dimensional `DataArray` to a `scipy.sparse` matrix.
+
+    Uses the [sparse](https://github.com/pydata/sparse) package as an
+    intermediate step, so only non-zero entries are kept. `scipy.sparse`
+    only supports 2-dimensional matrices, so `da` must be 2-dimensional.
+
+    Examples:
+        Converting a mostly-zero `DataArray` to a `scipy.sparse` matrix:
+
+        >>> import numpy as np
+        >>> import xarray as xr
+        >>> import janitor.xarray
+        >>> da = xr.DataArray(
+        ...     np.array([[0, 0, 3], [4, 0, 0]]),
+        ...     dims=["row", "col"],
+        ... )
+        >>> mat = da.to_scipy_sparse()  # doctest: +SKIP
+        >>> mat.toarray()  # doctest: +SKIP
+        array([[0, 0, 3],
+               [4, 0, 0]])
+
+    Args:
+        da: The `DataArray` supplied by the method itself. Must have
+            exactly 2 dimensions.
+
+    Raises:
+        ValueError: If `da` does not have exactly 2 dimensions.
+
+    Returns:
+        A `scipy.sparse` CSR matrix containing the non-zero values of `da`.
+    """
+    if da.ndim != 2:
+        raise ValueError(
+            "`to_scipy_sparse` only supports 2-dimensional DataArrays, "
+            f"but the supplied DataArray has {da.ndim} dimension(s)."
+        )
+
+    if isinstance(da.data, sparse.COO):
+        return da.data.tocsr()
+
+    return sparse.COO.from_numpy(np.asarray(da.data)).tocsr()
+
+
+@pf.register_xarray_dataarray_method
+def from_scipy_sparse(
+    da: xr.DataArray,
+    scipy_sparse_mat,
+    use_coords: bool = False,
+    use_attrs: bool = False,
+    new_name: str = None,
+) -> xr.DataArray:
+    """
+    Given a 2-dimensional `scipy.sparse` matrix, return a `DataArray`
+    that mirrors the dimension names (and, optionally, coordinates and
+    attrs) of the supplied `DataArray`.
+
+    This is the inverse of `to_scipy_sparse`, and is implemented as a
+    thin wrapper around `clone_using`, converting `scipy_sparse_mat` to
+    a `sparse.COO`-backed array first. Since `scipy_sparse_mat` will
+    generally not have the same shape as `da`, `use_coords` defaults to
+    `False` here (unlike in `clone_using`), so shapes are not required
+    to match unless you explicitly ask for coordinates to be copied over.
+
+    Examples:
+        Rebuilding a `DataArray` from a `scipy.sparse` matrix, keeping
+        only the dimension names of the original:
+
+        >>> import numpy as np
+        >>> import scipy.sparse
+        >>> import xarray as xr
+        >>> import janitor.xarray
+        >>> da = xr.DataArray(
+        ...     np.zeros((2, 3)),
+        ...     dims=["row", "col"],
+        ...     name="original",
+        ... )
+        >>> mat = scipy.sparse.csr_matrix([[0, 0, 3], [4, 0, 0]])
+        >>> new_da = da.from_scipy_sparse(mat)  # doctest: +SKIP
+        >>> new_da.dims  # doctest: +SKIP
+        ('row', 'col')
+        >>> new_da.data.todense()  # doctest: +SKIP
+        array([[0, 0, 3],
+               [4, 0, 0]])
+
+    Args:
+        da: The `DataArray` supplied by the method itself, used as the
+            template for dimension names and (optionally) coordinates
+            and attrs.
+        scipy_sparse_mat: A 2-dimensional `scipy.sparse` matrix.
+        use_coords: If `True`, use the coordinates of `da` for the
+            coordinates of the newly-generated array. `da` and
+            `scipy_sparse_mat` must have the same shape in that case.
+            Defaults to `False`.
+        use_attrs: If `True`, copy over the `attrs` from `da`.
+        new_name: If set, use as the name of the returned `DataArray`.
+            Otherwise, use the name of `da`.
+
+    Returns:
+        A new `DataArray`, with `sparse.COO`-backed data restored from
+            `scipy_sparse_mat`.
+    """
+    coo = sparse.COO.from_scipy_sparse(scipy_sparse_mat)
+
+    return da.clone_using(
+        coo,
+        use_coords=use_coords,
+        use_attrs=use_attrs,
+        new_name=new_name,
+    )
diff --git a/pixi.lock b/pixi.lock
index 8b8f1a44c..20295cf97 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,4 +1,20 @@
-version: 6
+version: 7
+platforms:
+- name: linux-64
+  virtual-packages:
+  - __unix=0=0
+  - __linux=4.18
+  - __glibc=2.28
+  - __archspec=0=x86_64
+- name: osx-arm64
+  virtual-packages:
+  - __unix=0=0
+  - __osx=13.0
+  - __archspec=0=m1
+- name: win-64
+  virtual-packages:
+  - __win=10.0
+  - __archspec=0=x86_64
 environments:
   biology:
     channels:
@@ -6,20 +22,11 @@ environments:
     - url: https://conda.anaconda.org/conda-forge/label/pandas_rc/
     indexes:
     - https://pypi.org/simple
-    options:
-      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://prefix.dev/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
-      - conda: https://prefix.dev/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
       - conda: https://prefix.dev/conda-forge/linux-64/alsa-lib-1.2.15.3-hb03c661_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda
       - conda: https://prefix.dev/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda
-      - conda: https://prefix.dev/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
       - conda: https://prefix.dev/conda-forge/linux-64/aws-c-auth-0.10.3-h3aafcba_1.conda
       - conda: https://prefix.dev/conda-forge/linux-64/aws-c-cal-0.9.14-h8e43964_1.conda
       - conda: https://prefix.dev/conda-forge/linux-64/aws-c-common-0.13.1-hb03c661_0.conda
@@ -38,106 +45,34 @@ environments:
       - conda: https://prefix.dev/conda-forge/linux-64/azure-storage-blobs-cpp-12.17.0-hf824e48_1.conda
       - conda: https://prefix.dev/conda-forge/linux-64/azure-storage-common-cpp-12.13.0-ha7a2c86_0.conda
       - conda: https://prefix.dev/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.15.0-h1e5b466_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda
-      - conda: https://prefix.dev/conda-forge/noarch/backports.zstd-1.5.0-py314h680f03e_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
       - conda: https://prefix.dev/conda-forge/linux-64/biopython-1.87-py314h5bd0f2a_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda
-      - conda: https://prefix.dev/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda
       - conda: https://prefix.dev/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda
       - conda: https://prefix.dev/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda
       - conda: https://prefix.dev/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
       - conda: https://prefix.dev/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
       - conda: https://prefix.dev/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
-      - conda: https://prefix.dev/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda
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@@ -225,23 +160,11 @@ environments:
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-      - conda: https://prefix.dev/conda-forge/noarch/mpmath-1.4.1-pyhd8ed1ab_0.conda
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diff --git a/pyproject.toml b/pyproject.toml
index f2619f625..7fd071617 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,6 +56,7 @@ docs = [
     "tqdm",
     "unyt",
     "pyspark",
+    "sparse",
 ]
 test = [
     "pytest-cov",
@@ -79,6 +80,9 @@ engineering = [
 spark = [
     "pyspark",
 ]
+sparse = [
+    "sparse",
+]
 all = [
     "pip-tools",
     "pre-commit",
@@ -96,6 +100,7 @@ all = [
     "tqdm",
     "unyt",
     "pyspark",
+    "sparse",
     "pytest-cov",
     "pytest-xdist",
     "pytest>=3.4.2",
@@ -176,6 +181,9 @@ unyt = ">=3.1.0,<4"
 [tool.pixi.feature.spark.dependencies]
 pyspark = ">=4.1.1,<5"
 
+[tool.pixi.feature.sparse.dependencies]
+sparse = "*"
+
 [tool.pixi.feature.py311.dependencies]
 python = "3.11.*"
 
@@ -216,6 +224,7 @@ biology = { features = ["tests", "setup", "biology"] }
 chemistry = { features = ["tests", "setup", "chemistry"] }
 engineering = { features = ["tests", "setup", "engineering"] }
 spark = { features = ["tests", "setup", "spark"] }
+sparse = { features = ["tests", "setup", "sparse"] }
 py311 = { features = ["tests", "setup", "py311"] }
 py312 = { features = ["tests", "setup", "py312"] }
 py313 = { features = ["tests", "setup", "py313"] }
diff --git a/tests/xarray/test_from_scipy_sparse.py b/tests/xarray/test_from_scipy_sparse.py
new file mode 100644
index 000000000..80af1b961
--- /dev/null
+++ b/tests/xarray/test_from_scipy_sparse.py
@@ -0,0 +1,71 @@
+"""Tests for from_scipy_sparse."""
+
+import importlib
+
+import numpy as np
+import pytest
+import scipy.sparse
+import xarray as xr
+from helpers import running_on_ci
+
+import janitor  # noqa: F401
+
+# Skip all tests if the `sparse` package is not installed
+pytestmark = pytest.mark.skipif(
+    (importlib.util.find_spec("sparse") is None) & ~running_on_ci(),
+    reason="Sparse-conversion tests relying on the `sparse` package "
+    "only required for CI",
+)
+
+
+@pytest.mark.xarray
+def test_from_scipy_sparse_restores_values():
+    """The values of the scipy.sparse matrix are restored correctly."""
+
+    values = np.array([[0, 0, 3], [4, 0, 0]])
+    template = xr.DataArray(np.zeros((2, 3)), dims=["row", "col"])
+    mat = scipy.sparse.csr_matrix(values)
+
+    new_da = template.from_scipy_sparse(mat)
+
+    np.testing.assert_array_equal(new_da.data.todense(), values)
+
+
+@pytest.mark.xarray
+def test_from_scipy_sparse_keeps_dim_names():
+    """Dimension names are taken from the template DataArray."""
+
+    template = xr.DataArray(np.zeros((2, 3)), dims=["row", "col"])
+    mat = scipy.sparse.csr_matrix(np.array([[0, 0, 3], [4, 0, 0]]))
+
+    new_da = template.from_scipy_sparse(mat)
+
+    assert new_da.dims == ("row", "col")
+
+
+@pytest.mark.xarray
+def test_from_scipy_sparse_does_not_require_matching_shape_by_default():
+    """Shapes of template and scipy_sparse_mat need not match, since
+    use_coords defaults to False."""
+
+    template = xr.DataArray(
+        np.zeros((5, 5)),
+        dims=["row", "col"],
+        coords=dict(row=range(5), col=range(5)),
+    )
+    mat = scipy.sparse.csr_matrix(np.array([[0, 0, 3], [4, 0, 0]]))
+
+    new_da = template.from_scipy_sparse(mat)
+
+    assert new_da.shape == (2, 3)
+
+
+@pytest.mark.xarray
+def test_from_scipy_sparse_roundtrips_with_to_scipy_sparse(da):
+    """to_scipy_sparse followed by from_scipy_sparse restores the
+    original data."""
+
+    mat = da.to_scipy_sparse()
+    new_da = da.from_scipy_sparse(mat, use_coords=True)
+
+    np.testing.assert_array_equal(new_da.data.todense(), da.data)
diff --git a/tests/xarray/test_to_scipy_sparse.py b/tests/xarray/test_to_scipy_sparse.py
new file mode 100644
index 000000000..b5fddc03f
--- /dev/null
+++ b/tests/xarray/test_to_scipy_sparse.py
@@ -0,0 +1,51 @@
+"""Tests for to_scipy_sparse."""
+
+import importlib
+
+import numpy as np
+import pytest
+import scipy.sparse
+import xarray as xr
+from helpers import running_on_ci
+
+import janitor  # noqa: F401
+
+# Skip all tests if the `sparse` package is not installed
+pytestmark = pytest.mark.skipif(
+    (importlib.util.find_spec("sparse") is None) & ~running_on_ci(),
+    reason="Sparse-conversion tests relying on the `sparse` package "
+    "only required for CI",
+)
+
+
+@pytest.mark.xarray
+def test_to_scipy_sparse_roundtrips_dense_values(da):
+    """Converting to scipy.sparse and back to a dense array is lossless."""
+
+    mat = da.to_scipy_sparse()
+
+    assert scipy.sparse.issparse(mat)
+    np.testing.assert_array_equal(mat.toarray(), da.data)
+
+
+@pytest.mark.xarray
+def test_to_scipy_sparse_keeps_only_nonzero_entries():
+    """Only the non-zero entries of the DataArray are stored."""
+
+    values = np.array([[0, 0, 3], [4, 0, 0]])
+    da = xr.DataArray(values, dims=["row", "col"])
+
+    mat = da.to_scipy_sparse()
+
+    assert mat.nnz == 2
+    np.testing.assert_array_equal(mat.toarray(), values)
+
+
+@pytest.mark.xarray
+def test_to_scipy_sparse_raises_on_wrong_ndim():
+    """to_scipy_sparse only supports 2-dimensional DataArrays."""
+
+    da_1d = xr.DataArray(np.array([0, 1, 2]), dims=["row"])
+
+    with pytest.raises(ValueError):
+        da_1d.to_scipy_sparse()