CLN: Use type_mapper instead of manual conversion (#51766)

Author: phofl

pandas/io/feather_format.py

@@ -13,10 +13,8 @@
 from pandas.compat._optional import import_optional_dependency
 from pandas.util._decorators import doc
 
-from pandas import (
-    arrays,
-    get_option,
-)
+import pandas as pd
+from pandas import get_option
 from pandas.core.api import DataFrame
 from pandas.core.shared_docs import _shared_docs
 
@@ -140,11 +138,4 @@ def read_feather(
             return pa_table.to_pandas(types_mapper=_arrow_dtype_mapping().get)
 
         elif dtype_backend == "pyarrow":
-            return DataFrame(
-                {
-                    col_name: arrays.ArrowExtensionArray(pa_col)
-                    for col_name, pa_col in zip(
-                        pa_table.column_names, pa_table.itercolumns()
-                    )
-                }
-            )
+            return pa_table.to_pandas(types_mapper=pd.ArrowDtype)

pandas/io/json/_json.py

@@ -42,6 +42,7 @@
 from pandas.core.dtypes.generic import ABCIndex
 
 from pandas import (
+    ArrowDtype,
     DataFrame,
     MultiIndex,
     Series,
@@ -963,16 +964,8 @@ def read(self) -> DataFrame | Series:
                 pa_table = pyarrow_json.read_json(self.data)
                 if self.use_nullable_dtypes:
                     if get_option("mode.dtype_backend") == "pyarrow":
-                        from pandas.arrays import ArrowExtensionArray
-
-                        return DataFrame(
-                            {
-                                col_name: ArrowExtensionArray(pa_col)
-                                for col_name, pa_col in zip(
-                                    pa_table.column_names, pa_table.itercolumns()
-                                )
-                            }
-                        )
+                        return pa_table.to_pandas(types_mapper=ArrowDtype)
+
                     elif get_option("mode.dtype_backend") == "pandas":
                         from pandas.io._util import _arrow_dtype_mapping
 

pandas/io/orc.py

@@ -25,8 +25,7 @@
     is_unsigned_integer_dtype,
 )
 
-from pandas.core.arrays import ArrowExtensionArray
-from pandas.core.frame import DataFrame
+import pandas as pd
 
 from pandas.io.common import (
     get_handle,
@@ -40,6 +39,8 @@
         WriteBuffer,
     )
 
+    from pandas.core.frame import DataFrame
+
 
 def read_orc(
     path: FilePath | ReadBuffer[bytes],
@@ -127,14 +128,7 @@ def read_orc(
     if use_nullable_dtypes:
         dtype_backend = get_option("mode.dtype_backend")
         if dtype_backend == "pyarrow":
-            df = DataFrame(
-                {
-                    col_name: ArrowExtensionArray(pa_col)
-                    for col_name, pa_col in zip(
-                        pa_table.column_names, pa_table.itercolumns()
-                    )
-                }
-            )
+            df = pa_table.to_pandas(types_mapper=pd.ArrowDtype)
         else:
             from pandas.io._util import _arrow_dtype_mapping
 

pandas/io/parquet.py

@@ -17,10 +17,10 @@
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import doc
 
+import pandas as pd
 from pandas import (
     DataFrame,
     MultiIndex,
-    arrays,
     get_option,
 )
 from pandas.core.shared_docs import _shared_docs
@@ -252,14 +252,11 @@ def read(
             if dtype_backend == "pandas":
                 result = pa_table.to_pandas(**to_pandas_kwargs)
             elif dtype_backend == "pyarrow":
-                result = DataFrame(
-                    {
-                        col_name: arrays.ArrowExtensionArray(pa_col)
-                        for col_name, pa_col in zip(
-                            pa_table.column_names, pa_table.itercolumns()
-                        )
-                    }
-                )
+                # Incompatible types in assignment (expression has type
+                # "Type[ArrowDtype]", target has type overloaded function
+                to_pandas_kwargs["types_mapper"] = pd.ArrowDtype  # type: ignore[assignment]  # noqa
+                result = pa_table.to_pandas(**to_pandas_kwargs)
+
             if manager == "array":
                 result = result._as_manager("array", copy=False)
             return result

pandas/io/parsers/arrow_parser_wrapper.py

@@ -6,9 +6,9 @@
 
 from pandas.core.dtypes.inference import is_integer
 
+import pandas as pd
 from pandas import (
     DataFrame,
-    arrays,
     get_option,
 )
 
@@ -156,12 +156,7 @@ def read(self) -> DataFrame:
             self.kwds["use_nullable_dtypes"]
             and get_option("mode.dtype_backend") == "pyarrow"
         ):
-            frame = DataFrame(
-                {
-                    col_name: arrays.ArrowExtensionArray(pa_col)
-                    for col_name, pa_col in zip(table.column_names, table.itercolumns())
-                }
-            )
+            frame = table.to_pandas(types_mapper=pd.ArrowDtype)
         else:
             frame = table.to_pandas()
         return self._finalize_pandas_output(frame)

pandas/tests/io/test_parquet.py

@@ -1034,14 +1034,7 @@ def test_read_use_nullable_types_pyarrow_config(self, pa, df_full):
         df["bool_with_none"] = [True, None, True]
 
         pa_table = pyarrow.Table.from_pandas(df)
-        expected = pd.DataFrame(
-            {
-                col_name: pd.arrays.ArrowExtensionArray(pa_column)
-                for col_name, pa_column in zip(
-                    pa_table.column_names, pa_table.itercolumns()
-                )
-            }
-        )
+        expected = pa_table.to_pandas(types_mapper=pd.ArrowDtype)
         # pyarrow infers datetimes as us instead of ns
         expected["datetime"] = expected["datetime"].astype("timestamp[us][pyarrow]")
         expected["datetime_with_nat"] = expected["datetime_with_nat"].astype(
@@ -1059,6 +1052,20 @@ def test_read_use_nullable_types_pyarrow_config(self, pa, df_full):
                 expected=expected,
             )
 
+    def test_read_use_nullable_types_pyarrow_config_index(self, pa):
+        df = pd.DataFrame(
+            {"a": [1, 2]}, index=pd.Index([3, 4], name="test"), dtype="int64[pyarrow]"
+        )
+        expected = df.copy()
+
+        with pd.option_context("mode.dtype_backend", "pyarrow"):
+            check_round_trip(
+                df,
+                engine=pa,
+                read_kwargs={"use_nullable_dtypes": True},
+                expected=expected,
+            )
+
 
 class TestParquetFastParquet(Base):
     def test_basic(self, fp, df_full):