BUG: Ignore warning for duplicate columns in to_dict when orient='tight' (#58335)

* Ignore warning for duplicate columns in to_dict when orient='tight'

* Add whatsnew

* Update doc/source/whatsnew/v3.0.0.rst

Co-authored-by: Abdulaziz Aloqeely <[email protected]>

* Update whatsnew and redefine duplicate columns

* Use assert instead

* assert not raise and equal

---------

Co-authored-by: Abdulaziz Aloqeely <[email protected]>

Author: Zhengbo Wang

doc/source/whatsnew/v3.0.0.rst

@@ -416,6 +416,7 @@ MultiIndex
 I/O
 ^^^
 - Bug in :class:`DataFrame` and :class:`Series` ``repr`` of :py:class:`collections.abc.Mapping`` elements. (:issue:`57915`)
+- Bug in :meth:`DataFrame.to_dict` raises unnecessary ``UserWarning`` when columns are not unique and ``orient='tight'``. (:issue:`58281`)
 - Bug in :meth:`DataFrame.to_excel` when writing empty :class:`DataFrame` with :class:`MultiIndex` on both axes (:issue:`57696`)
 - Bug in :meth:`DataFrame.to_string` that raised ``StopIteration`` with nested DataFrames. (:issue:`16098`)
 - Bug in :meth:`read_csv` raising ``TypeError`` when ``index_col`` is specified and ``na_values`` is a dict containing the key ``None``. (:issue:`57547`)

pandas/core/methods/to_dict.py

@@ -148,7 +148,7 @@ def to_dict(
         Return a collections.abc.MutableMapping object representing the
         DataFrame. The resulting transformation depends on the `orient` parameter.
     """
-    if not df.columns.is_unique:
+    if orient != "tight" and not df.columns.is_unique:
         warnings.warn(
             "DataFrame columns are not unique, some columns will be omitted.",
             UserWarning,

pandas/tests/frame/methods/test_to_dict.py

@@ -513,6 +513,20 @@ def test_to_dict_masked_native_python(self):
         result = df.to_dict(orient="records")
         assert isinstance(result[0]["a"], int)
 
+    def test_to_dict_tight_no_warning_with_duplicate_column(self):
+        # GH#58281
+        df = DataFrame([[1, 2], [3, 4], [5, 6]], columns=["A", "A"])
+        with tm.assert_produces_warning(None):
+            result = df.to_dict(orient="tight")
+        expected = {
+            "index": [0, 1, 2],
+            "columns": ["A", "A"],
+            "data": [[1, 2], [3, 4], [5, 6]],
+            "index_names": [None],
+            "column_names": [None],
+        }
+        assert result == expected
+
 
 @pytest.mark.parametrize(
     "val", [Timestamp(2020, 1, 1), Timedelta(1), Period("2020"), Interval(1, 2)]