Backport PR pandas-dev#56771: BUG: to_stata not handling ea dtypes co…

…rrectly

doc/source/whatsnew/v2.2.0.rst

@@ -847,6 +847,7 @@ I/O
 - Bug in :func:`read_json` not handling dtype conversion properly if ``infer_string`` is set (:issue:`56195`)
 - Bug in :meth:`DataFrame.to_excel`, with ``OdsWriter`` (``ods`` files) writing Boolean/string value (:issue:`54994`)
 - Bug in :meth:`DataFrame.to_hdf` and :func:`read_hdf` with ``datetime64`` dtypes with non-nanosecond resolution failing to round-trip correctly (:issue:`55622`)
+- Bug in :meth:`DataFrame.to_stata` raising for extension dtypes (:issue:`54671`)
 - Bug in :meth:`~pandas.read_excel` with ``engine="odf"`` (``ods`` files) when a string cell contains an annotation (:issue:`55200`)
 - Bug in :meth:`~pandas.read_excel` with an ODS file without cached formatted cell for float values (:issue:`55219`)
 - Bug where :meth:`DataFrame.to_json` would raise an ``OverflowError`` instead of a ``TypeError`` with unsupported NumPy types (:issue:`55403`)

pandas/io/stata.py

@@ -47,9 +47,11 @@
 )
 from pandas.util._exceptions import find_stack_level
 
+from pandas.core.dtypes.base import ExtensionDtype
 from pandas.core.dtypes.common import (
     ensure_object,
     is_numeric_dtype,
+    is_string_dtype,
 )
 from pandas.core.dtypes.dtypes import CategoricalDtype
 
@@ -62,8 +64,6 @@
     to_datetime,
     to_timedelta,
 )
-from pandas.core.arrays.boolean import BooleanDtype
-from pandas.core.arrays.integer import IntegerDtype
 from pandas.core.frame import DataFrame
 from pandas.core.indexes.base import Index
 from pandas.core.indexes.range import RangeIndex
@@ -591,17 +591,22 @@ def _cast_to_stata_types(data: DataFrame) -> DataFrame:
 
     for col in data:
         # Cast from unsupported types to supported types
-        is_nullable_int = isinstance(data[col].dtype, (IntegerDtype, BooleanDtype))
+        is_nullable_int = (
+            isinstance(data[col].dtype, ExtensionDtype)
+            and data[col].dtype.kind in "iub"
+        )
         # We need to find orig_missing before altering data below
         orig_missing = data[col].isna()
         if is_nullable_int:
-            missing_loc = data[col].isna()
-            if missing_loc.any():
-                # Replace with always safe value
-                fv = 0 if isinstance(data[col].dtype, IntegerDtype) else False
-                data.loc[missing_loc, col] = fv
+            fv = 0 if data[col].dtype.kind in "iu" else False
             # Replace with NumPy-compatible column
-            data[col] = data[col].astype(data[col].dtype.numpy_dtype)
+            data[col] = data[col].fillna(fv).astype(data[col].dtype.numpy_dtype)
+        elif isinstance(data[col].dtype, ExtensionDtype):
+            if getattr(data[col].dtype, "numpy_dtype", None) is not None:
+                data[col] = data[col].astype(data[col].dtype.numpy_dtype)
+            elif is_string_dtype(data[col].dtype):
+                data[col] = data[col].astype("object")
+
         dtype = data[col].dtype
         empty_df = data.shape[0] == 0
         for c_data in conversion_data:

pandas/tests/io/test_stata.py

@@ -11,6 +11,8 @@
 import numpy as np
 import pytest
 
+import pandas.util._test_decorators as td
+
 import pandas as pd
 from pandas import CategoricalDtype
 import pandas._testing as tm
@@ -1921,6 +1923,41 @@ def test_writer_118_exceptions(self):
             with pytest.raises(ValueError, match="You must use version 119"):
                 StataWriterUTF8(path, df, version=118)
 
+    @pytest.mark.parametrize(
+        "dtype_backend",
+        ["numpy_nullable", pytest.param("pyarrow", marks=td.skip_if_no("pyarrow"))],
+    )
+    def test_read_write_ea_dtypes(self, dtype_backend):
+        df = DataFrame(
+            {
+                "a": [1, 2, None],
+                "b": ["a", "b", "c"],
+                "c": [True, False, None],
+                "d": [1.5, 2.5, 3.5],
+                "e": pd.date_range("2020-12-31", periods=3, freq="D"),
+            },
+            index=pd.Index([0, 1, 2], name="index"),
+        )
+        df = df.convert_dtypes(dtype_backend=dtype_backend)
+        df.to_stata("test_stata.dta", version=118)
+
+        with tm.ensure_clean() as path:
+            df.to_stata(path)
+            written_and_read_again = self.read_dta(path)
+
+        expected = DataFrame(
+            {
+                "a": [1, 2, np.nan],
+                "b": ["a", "b", "c"],
+                "c": [1.0, 0, np.nan],
+                "d": [1.5, 2.5, 3.5],
+                "e": pd.date_range("2020-12-31", periods=3, freq="D"),
+            },
+            index=pd.Index([0, 1, 2], name="index", dtype=np.int32),
+        )
+
+        tm.assert_frame_equal(written_and_read_again.set_index("index"), expected)
+
 
 @pytest.mark.parametrize("version", [105, 108, 111, 113, 114])
 def test_backward_compat(version, datapath):