REGR: Fix conversion of mixed dtype DataFrame to numpy str

doc/source/whatsnew/v1.1.1.rst

@@ -15,6 +15,7 @@ including other versions of pandas.
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
 
+- Fixed regression where :meth:`DataFrame.to_numpy` would raise a ``RuntimeError`` for mixed dtypes when converting to ``str`` (:issue:`35455`)
 - Fixed regression where :func:`read_csv` would raise a ``ValueError`` when ``pandas.options.mode.use_inf_as_na`` was set to ``True`` (:issue:`35493`).
 - Fixed regression in :class:`pandas.core.groupby.RollingGroupby` where column selection was ignored (:issue:`35486`)
 - Fixed regression in :meth:`DataFrame.shift` with ``axis=1`` and heterogeneous dtypes (:issue:`35488`)

pandas/core/frame.py

@@ -1371,6 +1371,8 @@ def to_numpy(
         result = self._mgr.as_array(
             transpose=self._AXIS_REVERSED, dtype=dtype, copy=copy, na_value=na_value
         )
+        if result.dtype is not dtype:
+            result = np.array(result, dtype=dtype, copy=False)
 
         return result
 

pandas/core/internals/managers.py

@@ -19,6 +19,7 @@
 from pandas.core.dtypes.common import (
     DT64NS_DTYPE,
     is_datetimelike_v_numeric,
+    is_dtype_equal,
     is_extension_array_dtype,
     is_list_like,
     is_numeric_v_string_like,
@@ -865,6 +866,8 @@ def _interleave(self, dtype=None, na_value=lib.no_default) -> np.ndarray:
             dtype = dtype.subtype
         elif is_extension_array_dtype(dtype):
             dtype = "object"
+        elif is_dtype_equal(dtype, str):
+            dtype = "object"
 
         result = np.empty(self.shape, dtype=dtype)
 

pandas/tests/frame/test_api.py

@@ -367,6 +367,13 @@ def test_to_numpy_copy(self):
         assert df.to_numpy(copy=False).base is arr
         assert df.to_numpy(copy=True).base is not arr
 
+    def test_to_numpy_mixed_dtype_to_str(self):
+        # https://github.com/pandas-dev/pandas/issues/35455
+        df = pd.DataFrame([[pd.Timestamp("2020-01-01 00:00:00"), 100.0]])
+        result = df.to_numpy(dtype=str)
+        expected = np.array([["2020-01-01 00:00:00", "100.0"]], dtype=str)
+        tm.assert_numpy_array_equal(result, expected)
+
     def test_swapaxes(self):
         df = DataFrame(np.random.randn(10, 5))
         tm.assert_frame_equal(df.T, df.swapaxes(0, 1))