Merge branch 'main' into update-ruff-rules

.github/dependabot.yaml

@@ -0,0 +1,12 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "monthly"
+    groups:
+      github-actions:
+        patterns:
+          - "*"
+    commit-message:
+      prefix: "ci"

.github/workflows/build.yml

@@ -11,7 +11,7 @@ jobs:
         jsonschema-version: ["3.0", "latest"]
     name: py ${{ matrix.python-version }} js ${{ matrix.jsonschema-version }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:

.github/workflows/docbuild.yml

@@ -6,7 +6,7 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python 3.12
       uses: actions/setup-python@v5
       with:

.github/workflows/lint.yml

@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     name: ruff-mypy
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python 3.12
       uses: actions/setup-python@v5
       with:

altair/__init__.py

@@ -572,7 +572,6 @@
     "concat",
     "condition",
     "core",
-    "curry",
     "data",
     "data_transformers",
     "datum",
@@ -591,7 +590,6 @@
     "overload",
     "param",
     "parse_shorthand",
-    "pipe",
     "renderers",
     "repeat",
     "sample",

altair/_magics.py

@@ -10,7 +10,6 @@
 import IPython
 from IPython.core import magic_arguments
 import pandas as pd
-from toolz import curried
 
 from altair.vegalite import v5 as vegalite_v5
 
@@ -41,7 +40,9 @@ def _prepare_data(data, data_transformers):
     if data is None or isinstance(data, dict):
         return data
     elif isinstance(data, pd.DataFrame):
-        return curried.pipe(data, data_transformers.get())
+        if func := data_transformers.get():
+            data = func(data)
+        return data
     elif isinstance(data, str):
         return {"url": data}
     else:

altair/utils/_importers.py

@@ -1,12 +1,13 @@
 from __future__ import annotations
-from packaging.version import Version
+
 from importlib.metadata import version as importlib_version
 from typing import TYPE_CHECKING
 
+from packaging.version import Version
+
 if TYPE_CHECKING:
     from types import ModuleType
 
-
 def import_vegafusion() -> ModuleType:
     min_version = "1.5.0"
     try:

altair/utils/_vegafusion_data.py

@@ -1,23 +1,33 @@
 from __future__ import annotations
-from toolz import curried
 import uuid
 from weakref import WeakValueDictionary
-
 from typing import (
     Any,
+    Optional,
+    Union,
+    Dict,
+    Set,
     MutableMapping,
     TypedDict,
     Final,
     TYPE_CHECKING,
+    overload,
+    Callable,
 )
 
 from altair.utils._importers import import_vegafusion
 from altair.utils.core import DataFrameLike
-from altair.utils.data import DataType, ToValuesReturnType, MaxRowsError
+from altair.utils.data import (
+    DataType,
+    ToValuesReturnType,
+    MaxRowsError,
+    SupportsGeoInterface,
+)
 from altair.vegalite.data import default_data_transformer
 
 
 if TYPE_CHECKING:
+    import pandas as pd
     from vegafusion.runtime import ChartState  # type: ignore
 
 # Temporary storage for dataframes that have been extracted
@@ -36,21 +46,41 @@ class _ToVegaFusionReturnUrlDict(TypedDict):
     url: str
 
 
-@curried.curry
+_VegaFusionReturnType = Union[_ToVegaFusionReturnUrlDict, ToValuesReturnType]
+
+
+@overload
+def vegafusion_data_transformer(
+    data: None = ..., max_rows: int = ...
+) -> Callable[..., Any]: ...
+
+
+@overload
+def vegafusion_data_transformer(
+    data: DataFrameLike, max_rows: int
+) -> ToValuesReturnType: ...
+
+
+@overload
 def vegafusion_data_transformer(
-    data: DataType, max_rows: int = 100000
-) -> _ToVegaFusionReturnUrlDict | ToValuesReturnType:
+    data: Union[dict, pd.DataFrame, SupportsGeoInterface], max_rows: int
+) -> _VegaFusionReturnType: ...
+
+
+def vegafusion_data_transformer(
+    data: Optional[DataType] = None, max_rows: int = 100000
+) -> Union[Callable[..., Any], _VegaFusionReturnType]:
     """VegaFusion Data Transformer"""
-    if hasattr(data, "__geo_interface__"):
-        # Use default transformer for geo interface objects
-        # # (e.g. a geopandas GeoDataFrame)
-        return default_data_transformer(data)
-    elif isinstance(data, DataFrameLike):
+    if data is None:
+        return vegafusion_data_transformer
+    elif isinstance(data, DataFrameLike) and not isinstance(data, SupportsGeoInterface):
         table_name = f"table_{uuid.uuid4()}".replace("-", "_")
         extracted_inline_tables[table_name] = data
         return {"url": VEGAFUSION_PREFIX + table_name}
     else:
-        # Use default transformer if we don't recognize data type
+        # Use default transformer for geo interface objects
+        # # (e.g. a geopandas GeoDataFrame)
+        # Or if we don't recognize data type
         return default_data_transformer(data)