BUG: Fix convert_dtype complex (#62960)

Author: Ma-gi-cian

doc/source/whatsnew/v3.0.0.rst

@@ -1111,6 +1111,7 @@ Conversion
 - Bug in :meth:`DataFrame.astype` not casting ``values`` for Arrow-based dictionary dtype correctly (:issue:`58479`)
 - Bug in :meth:`DataFrame.update` bool dtype being converted to object (:issue:`55509`)
 - Bug in :meth:`Series.astype` might modify read-only array inplace when casting to a string dtype (:issue:`57212`)
+- Bug in :meth:`Series.convert_dtypes` and :meth:`DataFrame.convert_dtypes` raising ``TypeError`` when called on data with complex dtype (:issue:`60129`)
 - Bug in :meth:`Series.convert_dtypes` and :meth:`DataFrame.convert_dtypes` removing timezone information for objects with :class:`ArrowDtype` (:issue:`60237`)
 - Bug in :meth:`Series.reindex` not maintaining ``float32`` type when a ``reindex`` introduces a missing value (:issue:`45857`)
 - Bug in :meth:`to_datetime` and :meth:`to_timedelta` with input ``None`` returning ``None`` instead of ``NaT``, inconsistent with other conversion methods (:issue:`23055`)

pandas/core/dtypes/cast.py

@@ -934,6 +934,9 @@ def convert_dtypes(
     if (
         convert_string or convert_integer or convert_boolean or convert_floating
     ) and isinstance(input_array, np.ndarray):
+        if input_array.dtype.kind == "c":
+            return input_array.dtype
+
         if input_array.dtype == object:
             inferred_dtype = lib.infer_dtype(input_array)
         else:
@@ -954,7 +957,7 @@ def convert_dtypes(
                 inferred_dtype = NUMPY_INT_TO_DTYPE.get(
                     input_array.dtype, target_int_dtype
                 )
-            elif input_array.dtype.kind in "fcb":
+            elif input_array.dtype.kind in "fb":
                 # TODO: de-dup with maybe_cast_to_integer_array?
                 arr = input_array[notna(input_array)]
                 if len(arr) < len(input_array) and not is_nan_na():
@@ -972,7 +975,7 @@ def convert_dtypes(
                 inferred_dtype = target_int_dtype
 
         if convert_floating:
-            if input_array.dtype.kind in "fcb":
+            if input_array.dtype.kind in "fb":
                 # i.e. numeric but not integer
                 from pandas.core.arrays.floating import NUMPY_FLOAT_TO_DTYPE
 
@@ -1028,11 +1031,11 @@ def convert_dtypes(
 
         if (
             (convert_integer and inferred_dtype.kind in "iu")
-            or (convert_floating and inferred_dtype.kind in "fc")
+            or (convert_floating and inferred_dtype.kind in "f")
             or (convert_boolean and inferred_dtype.kind == "b")
             or (convert_string and isinstance(inferred_dtype, StringDtype))
             or (
-                inferred_dtype.kind not in "iufcb"
+                inferred_dtype.kind not in "iufb"
                 and not isinstance(inferred_dtype, StringDtype)
                 and not isinstance(inferred_dtype, CategoricalDtype)
             )

pandas/tests/frame/methods/test_convert_dtypes.py

@@ -228,3 +228,15 @@ def test_convert_dtype_pyarrow_timezone_preserve(self):
         result = df.convert_dtypes(dtype_backend="pyarrow")
         expected = df.copy()
         tm.assert_frame_equal(result, expected)
+
+    def test_convert_dtypes_complex(self):
+        # GH 60129
+        df = pd.DataFrame({"a": [1.0 + 5.0j, 1.5 - 3.0j], "b": [1, 2]})
+        expected = pd.DataFrame(
+            {
+                "a": pd.array([1.0 + 5.0j, 1.5 - 3.0j], dtype="complex128"),
+                "b": pd.array([1, 2], dtype="Int64"),
+            }
+        )
+        result = df.convert_dtypes()
+        tm.assert_frame_equal(result, expected)

pandas/tests/series/methods/test_convert_dtypes.py

@@ -332,3 +332,9 @@ def test_convert_dtype_pyarrow_timezone_preserve(self):
         result = ser.convert_dtypes(dtype_backend="pyarrow")
         expected = ser.copy()
         tm.assert_series_equal(result, expected)
+
+    def test_convert_dtypes_complex(self):
+        # GH 60129
+        ser = pd.Series([1.5 + 3.0j, 1.5 - 3.0j])
+        result = ser.convert_dtypes()
+        tm.assert_series_equal(result, ser)