Make mypy happy by making DataFrameLike protocol runtime checkable an…

…d using isinstance

altair/utils/core.py

@@ -37,7 +37,7 @@
 else:
     from typing_extensions import ParamSpec
 
-from typing import Literal, Protocol, TYPE_CHECKING
+from typing import Literal, Protocol, TYPE_CHECKING, runtime_checkable
 
 if TYPE_CHECKING:
     from pandas.core.interchange.dataframe_protocol import Column as PandasColumn
@@ -46,6 +46,7 @@
 P = ParamSpec("P")
 
 
+@runtime_checkable
 class DataFrameLike(Protocol):
     def __dataframe__(
         self, nan_as_null: bool = False, allow_copy: bool = True

altair/utils/data.py

@@ -105,7 +105,7 @@ def raise_max_rows_error():
             # mypy gets confused as it doesn't see Dict[Any, Any]
             # as equivalent to TDataType
             return data  # type: ignore[return-value]
-    elif hasattr(data, "__dataframe__"):
+    elif isinstance(data, DataFrameLike):
         pa_table = arrow_table_from_dfi_dataframe(data)
         if max_rows is not None and pa_table.num_rows > max_rows:
             raise_max_rows_error()
@@ -141,7 +141,7 @@ def sample(
         else:
             # Maybe this should raise an error or return something useful?
             return None
-    elif hasattr(data, "__dataframe__"):
+    elif isinstance(data, DataFrameLike):
         pa_table = arrow_table_from_dfi_dataframe(data)
         if not n:
             if frac is None:
@@ -229,7 +229,7 @@ def to_values(data: DataType) -> ToValuesReturnType:
         if "values" not in data:
             raise KeyError("values expected in data dict, but not present.")
         return data
-    elif hasattr(data, "__dataframe__"):
+    elif isinstance(data, DataFrameLike):
         pa_table = sanitize_arrow_table(arrow_table_from_dfi_dataframe(data))
         return {"values": pa_table.to_pylist()}
     else:
@@ -272,7 +272,7 @@ def _data_to_json_string(data: DataType) -> str:
         if "values" not in data:
             raise KeyError("values expected in data dict, but not present.")
         return json.dumps(data["values"], sort_keys=True)
-    elif hasattr(data, "__dataframe__"):
+    elif isinstance(data, DataFrameLike):
         pa_table = arrow_table_from_dfi_dataframe(data)
         return json.dumps(pa_table.to_pylist())
     else:
@@ -296,7 +296,7 @@ def _data_to_csv_string(data: Union[dict, pd.DataFrame, DataFrameLike]) -> str:
         if "values" not in data:
             raise KeyError("values expected in data dict, but not present")
         return pd.DataFrame.from_dict(data["values"]).to_csv(index=False)
-    elif hasattr(data, "__dataframe__"):
+    elif isinstance(data, DataFrameLike):
         # experimental interchange dataframe support
         import pyarrow as pa
         import pyarrow.csv as pa_csv