Merge branch 'main' into config-typed-dict

altair/utils/__init__.py

@@ -1,6 +1,5 @@
 from .core import (
     SHORTHAND_KEYS,
-    SchemaBase,
     display_traceback,
     infer_encoding_types,
     infer_vegalite_type_for_pandas,
@@ -13,7 +12,7 @@
 from .deprecation import AltairDeprecationWarning, deprecated, deprecated_warn
 from .html import spec_to_html
 from .plugin_registry import PluginRegistry
-from .schemapi import Optional, Undefined, is_undefined
+from .schemapi import Optional, SchemaBase, Undefined, is_undefined
 
 __all__ = (
     "SHORTHAND_KEYS",

altair/utils/_vegafusion_data.py

@@ -13,8 +13,6 @@
 )
 from weakref import WeakValueDictionary
 
-import narwhals.stable.v1 as nw
-
 from altair.utils._importers import import_vegafusion
 from altair.utils.core import DataFrameLike
 from altair.utils.data import (
@@ -71,10 +69,6 @@ def vegafusion_data_transformer(
     data: DataType | None = None, max_rows: int = 100000
 ) -> Callable[..., Any] | _VegaFusionReturnType:
     """VegaFusion Data Transformer."""
-    # Vegafusion does not support Narwhals, so if `data` is a Narwhals
-    # object, we make sure to extract the native object and let Vegafusion handle it.
-    # `strict=False` passes `data` through as-is if it is not a Narwhals object.
-    data = nw.to_native(data, strict=False)
     if data is None:
         return vegafusion_data_transformer
     elif isinstance(data, DataFrameLike) and not isinstance(data, SupportsGeoInterface):

altair/utils/data.py

@@ -314,12 +314,7 @@ def to_values(data: DataType) -> ToValuesReturnType:
     # `strict=False` passes `data` through as-is if it is not a Narwhals object.
     data_native = nw.to_native(data, strict=False)
     if isinstance(data_native, SupportsGeoInterface):
-        if _is_pandas_dataframe(data_native):
-            data_native = sanitize_pandas_dataframe(data_native)
-        # Maybe the type could be further clarified here that it is
-        # SupportGeoInterface and then the ignore statement is not needed?
-        data_sanitized = sanitize_geo_interface(data_native.__geo_interface__)
-        return {"values": data_sanitized}
+        return {"values": _from_geo_interface(data_native)}
     elif _is_pandas_dataframe(data_native):
         data_native = sanitize_pandas_dataframe(data_native)
         return {"values": data_native.to_dict(orient="records")}
@@ -350,32 +345,45 @@ def _compute_data_hash(data_str: str) -> str:
     return hashlib.sha256(data_str.encode()).hexdigest()[:32]
 
 
+def _from_geo_interface(data: SupportsGeoInterface | Any) -> dict[str, Any]:
+    """
+    Santize a ``__geo_interface__`` w/ pre-santize step for ``pandas`` if needed.
+
+    Notes
+    -----
+    Split out to resolve typing issues related to:
+    - Intersection types
+    - ``typing.TypeGuard``
+    - ``pd.DataFrame.__getattr__``
+    """
+    if _is_pandas_dataframe(data):
+        data = sanitize_pandas_dataframe(data)
+    return sanitize_geo_interface(data.__geo_interface__)
+
+
 def _data_to_json_string(data: DataType) -> str:
     """Return a JSON string representation of the input data."""
     check_data_type(data)
-    # `strict=False` passes `data` through as-is if it is not a Narwhals object.
-    data_native = nw.to_native(data, strict=False)
-    if isinstance(data_native, SupportsGeoInterface):
-        if _is_pandas_dataframe(data_native):
-            data_native = sanitize_pandas_dataframe(data_native)
-        data_native = sanitize_geo_interface(data_native.__geo_interface__)
-        return json.dumps(data_native)
-    elif _is_pandas_dataframe(data_native):
-        data = sanitize_pandas_dataframe(data_native)
-        return data_native.to_json(orient="records", double_precision=15)
-    elif isinstance(data_native, dict):
-        if "values" not in data_native:
+    if isinstance(data, SupportsGeoInterface):
+        return json.dumps(_from_geo_interface(data))
+    elif _is_pandas_dataframe(data):
+        data = sanitize_pandas_dataframe(data)
+        return data.to_json(orient="records", double_precision=15)
+    elif isinstance(data, dict):
+        if "values" not in data:
             msg = "values expected in data dict, but not present."
             raise KeyError(msg)
-        return json.dumps(data_native["values"], sort_keys=True)
-    elif isinstance(data, nw.DataFrame):
-        return json.dumps(data.rows(named=True))
-    else:
-        msg = "to_json only works with data expressed as " "a DataFrame or as a dict"
-        raise NotImplementedError(msg)
+        return json.dumps(data["values"], sort_keys=True)
+    try:
+        data_nw = nw.from_native(data, eager_only=True)
+    except TypeError as exc:
+        msg = "to_json only works with data expressed as a DataFrame or as a dict"
+        raise NotImplementedError(msg) from exc
+    data_nw = sanitize_narwhals_dataframe(data_nw)
+    return json.dumps(data_nw.rows(named=True))
 
 
-def _data_to_csv_string(data: dict | pd.DataFrame | DataFrameLike) -> str:
+def _data_to_csv_string(data: DataType) -> str:
     """Return a CSV string representation of the input data."""
     check_data_type(data)
     if isinstance(data, SupportsGeoInterface):
@@ -398,18 +406,12 @@ def _data_to_csv_string(data: dict | pd.DataFrame | DataFrameLike) -> str:
             msg = "pandas is required to convert a dict to a CSV string"
             raise ImportError(msg) from exc
         return pd.DataFrame.from_dict(data["values"]).to_csv(index=False)
-    elif isinstance(data, DataFrameLike):
-        # experimental interchange dataframe support
-        import pyarrow as pa
-        import pyarrow.csv as pa_csv
-
-        pa_table = arrow_table_from_dfi_dataframe(data)
-        csv_buffer = pa.BufferOutputStream()
-        pa_csv.write_csv(pa_table, csv_buffer)
-        return csv_buffer.getvalue().to_pybytes().decode()
-    else:
-        msg = "to_csv only works with data expressed as " "a DataFrame or as a dict"
-        raise NotImplementedError(msg)
+    try:
+        data_nw = nw.from_native(data, eager_only=True)
+    except TypeError as exc:
+        msg = "to_csv only works with data expressed as a DataFrame or as a dict"
+        raise NotImplementedError(msg) from exc
+    return data_nw.write_csv()
 
 
 def arrow_table_from_dfi_dataframe(dfi_df: DataFrameLike) -> pa.Table:

altair/utils/schemapi.py

@@ -25,6 +25,7 @@
     Sequence,
     TypeVar,
     Union,
+    cast,
     overload,
 )
 from typing_extensions import TypeAlias
@@ -113,7 +114,7 @@ def validate_jsonschema(
     rootschema: dict[str, Any] | None = ...,
     *,
     raise_error: Literal[True] = ...,
-) -> None: ...
+) -> Never: ...
 
 
 @overload
@@ -128,11 +129,11 @@ def validate_jsonschema(
 
 def validate_jsonschema(
     spec,
-    schema,
-    rootschema=None,
+    schema: dict[str, Any],
+    rootschema: dict[str, Any] | None = None,
     *,
-    raise_error=True,
-):
+    raise_error: bool = True,
+) -> jsonschema.exceptions.ValidationError | None:
     """
     Validates the passed in spec against the schema in the context of the rootschema.
 
@@ -149,7 +150,7 @@ def validate_jsonschema(
 
         # Nothing special about this first error but we need to choose one
         # which can be raised
-        main_error = next(iter(grouped_errors.values()))[0]
+        main_error: Any = next(iter(grouped_errors.values()))[0]
         # All errors are then attached as a new attribute to ValidationError so that
         # they can be used in SchemaValidationError to craft a more helpful
         # error message. Setting a new attribute like this is not ideal as
@@ -833,6 +834,41 @@ def is_undefined(obj: Any) -> TypeIs[UndefinedType]:
     return obj is Undefined
 
 
+@overload
+def _shallow_copy(obj: _CopyImpl) -> _CopyImpl: ...
+@overload
+def _shallow_copy(obj: Any) -> Any: ...
+def _shallow_copy(obj: _CopyImpl | Any) -> _CopyImpl | Any:
+    if isinstance(obj, SchemaBase):
+        return obj.copy(deep=False)
+    elif isinstance(obj, (list, dict)):
+        return obj.copy()
+    else:
+        return obj
+
+
+@overload
+def _deep_copy(obj: _CopyImpl, by_ref: set[str]) -> _CopyImpl: ...
+@overload
+def _deep_copy(obj: Any, by_ref: set[str]) -> Any: ...
+def _deep_copy(obj: _CopyImpl | Any, by_ref: set[str]) -> _CopyImpl | Any:
+    copy = partial(_deep_copy, by_ref=by_ref)
+    if isinstance(obj, SchemaBase):
+        if copier := getattr(obj, "__deepcopy__", None):
+            with debug_mode(False):
+                return copier(obj)
+        args = (copy(arg) for arg in obj._args)
+        kwds = {k: (copy(v) if k not in by_ref else v) for k, v in obj._kwds.items()}
+        with debug_mode(False):
+            return obj.__class__(*args, **kwds)
+    elif isinstance(obj, list):
+        return [copy(v) for v in obj]
+    elif isinstance(obj, dict):
+        return {k: (copy(v) if k not in by_ref else v) for k, v in obj.items()}
+    else:
+        return obj
+
+
 class SchemaBase:
     """
     Base class for schema wrappers.
@@ -870,7 +906,7 @@ def __init__(self, *args: Any, **kwds: Any) -> None:
         if DEBUG_MODE and self._class_is_valid_at_instantiation:
             self.to_dict(validate=True)
 
-    def copy(  # noqa: C901
+    def copy(
         self, deep: bool | Iterable[Any] = True, ignore: list[str] | None = None
     ) -> Self:
         """
@@ -887,53 +923,11 @@ def copy(  # noqa: C901
             A list of keys for which the contents should not be copied, but
             only stored by reference.
         """
-
-        def _shallow_copy(obj):
-            if isinstance(obj, SchemaBase):
-                return obj.copy(deep=False)
-            elif isinstance(obj, list):
-                return obj[:]
-            elif isinstance(obj, dict):
-                return obj.copy()
-            else:
-                return obj
-
-        def _deep_copy(obj, ignore: list[str] | None = None):
-            if ignore is None:
-                ignore = []
-            if isinstance(obj, SchemaBase):
-                args = tuple(_deep_copy(arg) for arg in obj._args)
-                kwds = {
-                    k: (_deep_copy(v, ignore=ignore) if k not in ignore else v)
-                    for k, v in obj._kwds.items()
-                }
-                with debug_mode(False):
-                    return obj.__class__(*args, **kwds)
-            elif isinstance(obj, list):
-                return [_deep_copy(v, ignore=ignore) for v in obj]
-            elif isinstance(obj, dict):
-                return {
-                    k: (_deep_copy(v, ignore=ignore) if k not in ignore else v)
-                    for k, v in obj.items()
-                }
-            else:
-                return obj
-
-        try:
-            deep = list(deep)  # type: ignore[arg-type]
-        except TypeError:
-            deep_is_list = False
-        else:
-            deep_is_list = True
-
-        if deep and not deep_is_list:
-            return _deep_copy(self, ignore=ignore)
-
+        if deep is True:
+            return cast("Self", _deep_copy(self, set(ignore) if ignore else set()))
         with debug_mode(False):
             copy = self.__class__(*self._args, **self._kwds)
-        if deep_is_list:
-            # Assert statement is for the benefit of Mypy
-            assert isinstance(deep, list)
+        if _is_iterable(deep):
             for attr in deep:
                 copy[attr] = _shallow_copy(copy._get(attr))
         return copy
@@ -953,7 +947,7 @@ def __getattr__(self, attr):
             return self._kwds[attr]
         else:
             try:
-                _getattr = super().__getattr__
+                _getattr = super().__getattr__  # pyright: ignore[reportAttributeAccessIssue]
             except AttributeError:
                 _getattr = super().__getattribute__
             return _getattr(attr)
@@ -1202,9 +1196,7 @@ def validate(
             schema = cls._schema
         # For the benefit of mypy
         assert schema is not None
-        return validate_jsonschema(
-            instance, schema, rootschema=cls._rootschema or cls._schema
-        )
+        validate_jsonschema(instance, schema, rootschema=cls._rootschema or cls._schema)
 
     @classmethod
     def resolve_references(cls, schema: dict[str, Any] | None = None) -> dict[str, Any]:
@@ -1230,7 +1222,7 @@ def validate_property(
         np_opt = sys.modules.get("numpy")
         value = _todict(value, context={}, np_opt=np_opt, pd_opt=pd_opt)
         props = cls.resolve_references(schema or cls._schema).get("properties", {})
-        return validate_jsonschema(
+        validate_jsonschema(
             value, props.get(name, {}), rootschema=cls._rootschema or cls._schema
         )
 
@@ -1240,6 +1232,13 @@ def __dir__(self) -> list[str]:
 
 TSchemaBase = TypeVar("TSchemaBase", bound=SchemaBase)
 
+_CopyImpl = TypeVar("_CopyImpl", SchemaBase, Dict[Any, Any], List[Any])
+"""
+Types which have an implementation in ``SchemaBase.copy()``.
+
+All other types are returned **by reference**.
+"""
+
 
 def _is_dict(obj: Any | dict[Any, Any]) -> TypeIs[dict[Any, Any]]:
     return isinstance(obj, dict)
@@ -1325,11 +1324,11 @@ def from_dict(
     @overload
     def from_dict(
         self,
-        dct: dict[str, Any],
-        tp: None = ...,
+        dct: dict[str, Any] | list[dict[str, Any]],
+        tp: Any = ...,
         schema: Any = ...,
-        rootschema: None = ...,
-        default_class: type[TSchemaBase] = ...,
+        rootschema: Any = ...,
+        default_class: type[TSchemaBase] = ...,  # pyright: ignore[reportInvalidTypeVarUse]
     ) -> TSchemaBase: ...
     @overload
     def from_dict(
@@ -1365,15 +1364,15 @@ def from_dict(
         schema: dict[str, Any] | None = None,
         rootschema: dict[str, Any] | None = None,
         default_class: Any = _passthrough,
-    ) -> TSchemaBase:
+    ) -> TSchemaBase | SchemaBase:
         """Construct an object from a dict representation."""
-        target_tp: type[TSchemaBase]
+        target_tp: Any
         current_schema: dict[str, Any]
         if isinstance(dct, SchemaBase):
-            return dct  # type: ignore[return-value]
+            return dct
         elif tp is not None:
             current_schema = tp._schema
-            root_schema = rootschema or tp._rootschema or current_schema
+            root_schema: dict[str, Any] = rootschema or tp._rootschema or current_schema
             target_tp = tp
         elif schema is not None:
             # If there are multiple matches, we use the first one in the dict.

altair/vegalite/v5/api.py

@@ -23,6 +23,7 @@
 from typing_extensions import TypeAlias
 
 import jsonschema
+import narwhals.stable.v1 as nw
 
 from altair import utils
 from altair.expr import core as _expr_core
@@ -274,7 +275,7 @@ def _prepare_data(
     # convert dataframes  or objects with __geo_interface__ to dict
     elif not isinstance(data, dict) and _is_data_type(data):
         if func := data_transformers.get():
-            data = func(data)
+            data = func(nw.to_native(data, strict=False))
 
     # convert string input to a URLData
     elif isinstance(data, str):
@@ -1060,6 +1061,9 @@ def to_dict(self, *args: Any, **kwds: Any) -> _Conditional[_C]:  # type: ignore[
         m = super().to_dict(*args, **kwds)
         return _Conditional(condition=m["condition"])
 
+    def __deepcopy__(self, memo: Any) -> Self:
+        return type(self)(_Conditional(condition=_deepcopy(self.condition)))
+
 
 class ChainedWhen(_BaseWhen):
     """