TYP: Column.null_count is a Python int

pandas/core/exchange/column.py

@@ -96,7 +96,7 @@ def offset(self) -> int:
         return 0
 
     @cache_readonly
-    def dtype(self):
+    def dtype(self) -> tuple[DtypeKind, int, str, str]:
         dtype = self._col.dtype
 
         if is_categorical_dtype(dtype):
@@ -138,7 +138,7 @@ def _dtype_from_pandasdtype(self, dtype) -> tuple[DtypeKind, int, str, str]:
             # Not a NumPy dtype. Check if it's a categorical maybe
             raise ValueError(f"Data type {dtype} not supported by exchange protocol")
 
-        return (kind, dtype.itemsize * 8, dtype_to_arrow_c_fmt(dtype), dtype.byteorder)
+        return kind, dtype.itemsize * 8, dtype_to_arrow_c_fmt(dtype), dtype.byteorder
 
     @property
     def describe_categorical(self):
@@ -181,10 +181,10 @@ def null_count(self) -> int:
         """
         Number of null elements. Should always be known.
         """
-        return self._col.isna().sum()
+        return self._col.isna().sum().item()
 
     @property
-    def metadata(self):
+    def metadata(self) -> dict[str, pd.Index]:
         """
         Store specific metadata of the column.
         """
@@ -196,7 +196,7 @@ def num_chunks(self) -> int:
         """
         return 1
 
-    def get_chunks(self, n_chunks=None):
+    def get_chunks(self, n_chunks: int | None = None):
         """
         Return an iterator yielding the chunks.
         See `DataFrame.get_chunks` for details on ``n_chunks``.

pandas/tests/exchange/test_spec_conformance.py

@@ -24,7 +24,9 @@ def test_only_one_dtype(test_data, df_from_dict):
 
     column_size = len(test_data[columns[0]])
     for column in columns:
-        assert dfX.get_column_by_name(column).null_count == 0
+        null_count = dfX.get_column_by_name(column).null_count
+        assert null_count == 0
+        assert isinstance(null_count, int)
         assert dfX.get_column_by_name(column).size == column_size
         assert dfX.get_column_by_name(column).offset == 0
 
@@ -49,6 +51,7 @@ def test_mixed_dtypes(df_from_dict):
     for column, kind in columns.items():
         colX = dfX.get_column_by_name(column)
         assert colX.null_count == 0
+        assert isinstance(colX.null_count, int)
         assert colX.size == 3
         assert colX.offset == 0
 
@@ -62,6 +65,7 @@ def test_na_float(df_from_dict):
     dfX = df.__dataframe__()
     colX = dfX.get_column_by_name("a")
     assert colX.null_count == 1
+    assert isinstance(colX.null_count, int)
 
 
 def test_noncategorical(df_from_dict):