BUG: Interchange object data buffer has the wrong dtype / from_datafr…

…ame incorrect (pandas-dev#57570)

string

.pre-commit-config.yaml

@@ -53,13 +53,11 @@ repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0
     hooks:
-    -   id: check-ast
     -   id: check-case-conflict
     -   id: check-toml
     -   id: check-xml
     -   id: check-yaml
         exclude: ^ci/meta.yaml$
-    -   id: debug-statements
     -   id: end-of-file-fixer
         exclude: \.txt$
     -   id: mixed-line-ending

doc/source/whatsnew/v3.0.0.rst

@@ -289,6 +289,7 @@ Other
 - Bug in :func:`tseries.api.guess_datetime_format` would fail to infer time format when "%Y" == "%H%M" (:issue:`57452`)
 - Bug in :meth:`DataFrame.sort_index` when passing ``axis="columns"`` and ``ignore_index=True`` and ``ascending=False`` not returning a :class:`RangeIndex` columns (:issue:`57293`)
 - Bug in :meth:`DataFrame.where` where using a non-bool type array in the function would return a ``ValueError`` instead of a ``TypeError`` (:issue:`56330`)
+- Bug in Dataframe Interchange Protocol implementation was returning incorrect results for data buffers' associated dtype, for string and datetime columns (:issue:`54781`)
 
 .. ***DO NOT USE THIS SECTION***
 

pandas/core/interchange/column.py

@@ -278,20 +278,28 @@ def _get_data_buffer(
         """
         Return the buffer containing the data and the buffer's associated dtype.
         """
-        if self.dtype[0] in (
-            DtypeKind.INT,
-            DtypeKind.UINT,
-            DtypeKind.FLOAT,
-            DtypeKind.BOOL,
-            DtypeKind.DATETIME,
-        ):
+        if self.dtype[0] == DtypeKind.DATETIME:
             # self.dtype[2] is an ArrowCTypes.TIMESTAMP where the tz will make
             # it longer than 4 characters
-            if self.dtype[0] == DtypeKind.DATETIME and len(self.dtype[2]) > 4:
+            if len(self.dtype[2]) > 4:
                 np_arr = self._col.dt.tz_convert(None).to_numpy()
             else:
                 np_arr = self._col.to_numpy()
             buffer = PandasBuffer(np_arr, allow_copy=self._allow_copy)
+            dtype = (
+                DtypeKind.INT,
+                64,
+                ArrowCTypes.INT64,
+                Endianness.NATIVE,
+            )
+        elif self.dtype[0] in (
+            DtypeKind.INT,
+            DtypeKind.UINT,
+            DtypeKind.FLOAT,
+            DtypeKind.BOOL,
+        ):
+            np_arr = self._col.to_numpy()
+            buffer = PandasBuffer(np_arr, allow_copy=self._allow_copy)
             dtype = self.dtype
         elif self.dtype[0] == DtypeKind.CATEGORICAL:
             codes = self._col.values._codes
@@ -314,7 +322,12 @@ def _get_data_buffer(
             # Define the dtype for the returned buffer
             # TODO: this will need correcting
             # https://github.com/pandas-dev/pandas/issues/54781
-            dtype = self.dtype
+            dtype = (
+                DtypeKind.UINT,
+                8,
+                ArrowCTypes.UINT8,
+                Endianness.NATIVE,
+            )  # note: currently only support native endianness
         else:
             raise NotImplementedError(f"Data type {self._col.dtype} not handled yet")
 

pandas/tests/interchange/test_impl.py

@@ -435,3 +435,48 @@ def test_empty_dataframe():
     result = pd.api.interchange.from_dataframe(dfi, allow_copy=False)
     expected = pd.DataFrame({"a": []}, dtype="int8")
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("data", "expected_dtype", "expected_buffer_dtype"),
+    [
+        (
+            pd.Series(["a", "b", "a"], dtype="category"),
+            (DtypeKind.CATEGORICAL, 8, "c", "="),
+            (DtypeKind.INT, 8, "c", "|"),
+        ),
+        (
+            pd.Series(
+                [datetime(2022, 1, 1), datetime(2022, 1, 2), datetime(2022, 1, 3)]
+            ),
+            (DtypeKind.DATETIME, 64, "tsn:", "="),
+            (DtypeKind.INT, 64, ArrowCTypes.INT64, "="),
+        ),
+        (
+            pd.Series(["a", "bc", None]),
+            (DtypeKind.STRING, 8, ArrowCTypes.STRING, "="),
+            (DtypeKind.UINT, 8, ArrowCTypes.UINT8, "="),
+        ),
+        (
+            pd.Series([1, 2, 3]),
+            (DtypeKind.INT, 64, ArrowCTypes.INT64, "="),
+            (DtypeKind.INT, 64, ArrowCTypes.INT64, "="),
+        ),
+        (
+            pd.Series([1.5, 2, 3]),
+            (DtypeKind.FLOAT, 64, ArrowCTypes.FLOAT64, "="),
+            (DtypeKind.FLOAT, 64, ArrowCTypes.FLOAT64, "="),
+        ),
+    ],
+)
+def test_buffer_dtype_categorical(
+    data: pd.Series,
+    expected_dtype: tuple[DtypeKind, int, str, str],
+    expected_buffer_dtype: tuple[DtypeKind, int, str, str],
+) -> None:
+    # https://github.com/pandas-dev/pandas/issues/54781
+    df = pd.DataFrame({"data": data})
+    dfi = df.__dataframe__()
+    col = dfi.get_column_by_name("data")
+    assert col.dtype == expected_dtype
+    assert col.get_buffers()["data"][1] == expected_buffer_dtype