dask 2025.1.0 compatibility (#324)

.github/workflows/tests.yaml

@@ -29,11 +29,9 @@ jobs:
       matrix:
         os: [ubuntu-latest]
         env:
+          - ci/envs/310-minimal.yaml
           - ci/envs/310-no-optional-deps.yaml
-          - ci/envs/39-minimal.yaml
-          - ci/envs/311-no-expr.yaml
           - ci/envs/311-latest.yaml
-          - ci/envs/311-latest-no-expr.yaml
           - ci/envs/312-latest.yaml
 
         include:
@@ -52,7 +50,7 @@ jobs:
         with:
           environment-file: ${{ matrix.env }}
           miniforge-version: latest
-          miniforge-variant: Mambaforge
+          miniforge-variant: Miniforge3
           use-mamba: true
 
       - name: Check and Log Environment

CHANGELOG.md

@@ -1,6 +1,15 @@
 Changelog
 =========
 
+Version 0.4.3 (January, 2025)
+----------------------------------
+
+Packaging:
+
+- `dask>=2025.1.0` is now required.
+- `python>=3.10` is now required.
+
+
 Version 0.4.2 (September 24, 2024)
 ----------------------------------
 

ci/envs/39-minimal.yaml → ci/envs/310-minimal.yaml

@@ -3,12 +3,12 @@ channels:
   - conda-forge
 dependencies:
   # required dependencies
-  - python=3.9
-  - numpy=1.23
-  - dask=2022.06.0
-  - distributed=2022.06.0
-  - geopandas=0.12
-  - pandas=1.5.3
+  - python=3.10
+  - numpy=1.24
+  - dask=2025.1.0
+  - distributed=2025.1.0
+  - geopandas=0.14.3
+  - pandas=2.0.0
   - shapely=2.0
   - pyproj=3.4
   - packaging

ci/envs/312-dev.yaml

@@ -28,4 +28,3 @@ dependencies:
       - git+https://github.com/shapely/shapely.git@main
       - git+https://github.com/geopandas/geopandas.git@main
       - git+https://github.com/dask/dask.git@main
-      - git+https://github.com/dask-contrib/dask-expr.git@main

dask_geopandas/__init__.py

@@ -1,58 +1,42 @@
 from ._version import get_versions
 
-from . import backends
-
-if backends.QUERY_PLANNING_ON:
-    from .expr import (
-        points_from_xy,
-        from_wkt,
-        from_wkb,
-        GeoDataFrame,
-        GeoSeries,
-        from_geopandas,
-        from_dask_dataframe,
-    )
-else:
-    from .core import (
-        points_from_xy,
-        from_wkt,
-        from_wkb,
-        GeoDataFrame,
-        GeoSeries,
-        from_geopandas,
-        from_dask_dataframe,
-    )
+from .expr import (
+    points_from_xy,
+    from_wkt,
+    from_wkb,
+    GeoDataFrame,
+    GeoSeries,
+    from_geopandas,
+    from_dask_dataframe,
+)
 from .io.file import read_file
 from .io.parquet import read_parquet, to_parquet
 from .io.arrow import read_feather, to_feather
 from .clip import clip
 from .sjoin import sjoin
+from . import backends as _  # needed to register dispatch functions with dask
 
 
 __version__ = get_versions()["version"]
 del get_versions
 
 __all__ = [
-    "points_from_xy",
-    "from_wkt",
-    "from_wkb",
     "GeoDataFrame",
     "GeoSeries",
-    "from_geopandas",
+    "clip",
     "from_dask_dataframe",
-    "read_file",
+    "from_geopandas",
+    "from_wkb",
+    "from_wkt",
+    "points_from_xy",
     "read_feather",
+    "read_file",
     "read_parquet",
+    "sjoin",
     "to_feather",
     "to_parquet",
-    "clip",
-    "sjoin",
 ]
 
 from . import _version
 
 __version__ = _version.get_versions()["version"]
-
-from . import _version
-
-__version__ = _version.get_versions()["version"]

dask_geopandas/backends.py

@@ -4,24 +4,6 @@
 import pandas as pd
 
 import dask
-from dask import config
-
-# Check if dask-dataframe is using dask-expr (mimix the logic of dask.dataframe
-# _dask_expr_enabled() - default of None means True as well if dask-expr is available)
-QUERY_PLANNING_ON = config.get("dataframe.query-planning", False)
-if QUERY_PLANNING_ON is None:
-    if Version(pd.__version__).major < 2:
-        QUERY_PLANNING_ON = False
-    else:
-        try:
-            import dask_expr  # noqa: F401
-        except ImportError:
-            # dask will raise error or warning depending on the config
-            QUERY_PLANNING_ON = False
-        else:
-            QUERY_PLANNING_ON = True
-
-
 from dask.base import normalize_token
 from dask.dataframe.backends import _nonempty_index, meta_nonempty_dataframe
 from dask.dataframe.core import get_parallel_type
@@ -34,7 +16,7 @@
 from geopandas.array import GeometryArray, GeometryDtype, from_shapely
 from shapely.geometry.base import BaseGeometry
 
-from .core import GeoDataFrame, GeoSeries
+from .expr import GeoDataFrame, GeoSeries
 
 get_parallel_type.register(geopandas.GeoDataFrame, lambda _: GeoDataFrame)
 get_parallel_type.register(geopandas.GeoSeries, lambda _: GeoSeries)
@@ -85,7 +67,6 @@ def tokenize_geometryarray(x):
 
 @pyarrow_schema_dispatch.register((geopandas.GeoDataFrame,))
 def get_pyarrow_schema_geopandas(obj):
-    import pandas as pd
     import pyarrow as pa
 
     df = pd.DataFrame(obj.copy())

dask_geopandas/clip.py

@@ -1,21 +1,16 @@
 import numpy as np
 
 from dask.base import tokenize
+from dask.dataframe import from_graph
 from dask.highlevelgraph import HighLevelGraph
 from dask.utils import derived_from
 
 import geopandas
 
-from . import backends
-
 
 @derived_from(geopandas.tools)
 def clip(gdf, mask, keep_geom_type=False):
-
-    if backends.QUERY_PLANNING_ON:
-        from .expr import GeoDataFrame, GeoSeries
-    else:
-        from .core import GeoDataFrame, GeoSeries
+    from dask_geopandas import GeoDataFrame, GeoSeries
 
     if isinstance(mask, (GeoDataFrame, GeoSeries)):
         raise NotImplementedError("Mask cannot be a Dask GeoDataFrame or GeoSeries.")
@@ -45,17 +40,8 @@ def clip(gdf, mask, keep_geom_type=False):
     }
     divisions = [None] * (len(dsk) + 1)
     graph = HighLevelGraph.from_collections(name, dsk, dependencies=[gdf])
-    if backends.QUERY_PLANNING_ON:
-        from dask_expr import from_graph
-
-        result = from_graph(graph, gdf._meta, tuple(divisions), dsk.keys(), "clip")
-    else:
-        from .core import GeoDataFrame, GeoSeries
 
-        if isinstance(gdf, GeoDataFrame):
-            result = GeoDataFrame(graph, name, gdf._meta, tuple(divisions))
-        elif isinstance(gdf, GeoSeries):
-            result = GeoSeries(graph, name, gdf._meta, tuple(divisions))
+    result = from_graph(graph, gdf._meta, tuple(divisions), dsk.keys(), "clip")
 
     result.spatial_partitions = new_spatial_partitions
     return result