wip

Author: MarcoGorelli

pandas/core/interchange/column.py

@@ -190,6 +190,8 @@ def describe_categorical(self):
 
     @property
     def describe_null(self):
+        if self._col.dtype in ("Int64", "Int64[pyarrow]"):
+            return ColumnNullType.USE_BYTEMASK, 1
         kind = self.dtype[0]
         try:
             null, value = _NULL_DESCRIPTION[kind]
@@ -298,7 +300,11 @@ def _get_data_buffer(
             DtypeKind.FLOAT,
             DtypeKind.BOOL,
         ):
-            np_arr = self._col.to_numpy()
+            arr = self._col.array
+            if hasattr(arr, "_data"):
+                np_arr = arr._data
+            else:
+                np_arr = arr._ndarray
             buffer = PandasBuffer(np_arr, allow_copy=self._allow_copy)
             dtype = self.dtype
         elif self.dtype[0] == DtypeKind.CATEGORICAL:
@@ -341,6 +347,32 @@ def _get_validity_buffer(self) -> tuple[PandasBuffer, Any]:
         """
         null, invalid = self.describe_null
 
+        if self._col.dtype == "Int64[pyarrow]":
+            buf, mask = self._col.array._pa_array.chunk(0).buffers()
+
+            # Convert the mask array to a Pandas "buffer" using
+            # a NumPy array as the backing store
+            buffer = PandasBuffer(mask, size=8)
+
+            # Define the dtype of the returned buffer
+            dtype = (DtypeKind.BOOL, 8, ArrowCTypes.BOOL, Endianness.NATIVE)
+
+            return buffer, dtype
+
+        if self._col.dtype == "Int64":
+            buf = self._col.array._data
+
+            mask = self._col.array._mask
+
+            # Convert the mask array to a Pandas "buffer" using
+            # a NumPy array as the backing store
+            buffer = PandasBuffer(mask)
+
+            # Define the dtype of the returned buffer
+            dtype = (DtypeKind.BOOL, 8, ArrowCTypes.BOOL, Endianness.NATIVE)
+
+            return buffer, dtype
+
         if self.dtype[0] == DtypeKind.STRING:
             # For now, use byte array as the mask.
             # TODO: maybe store as bit array to save space?..