cleanup

python/benchmarks/convert_pandas.py

@@ -99,8 +99,3 @@ def setup(self):
     def time_Table_from_pandas(self):
         for _ in range(50):
             pa.Table.from_pandas(self.df, nthreads=1)
-
-
-class ArrowTableToPandasDataframe():
-
-

python/pyarrow/array.pxi

@@ -706,8 +706,7 @@ cdef class _PandasConvertible(_Weakrefable):
             bint self_destruct=False,
             str maps_as_pydicts=None,
             types_mapper=None,
-            bint coerce_temporal_nanoseconds=False,
-            bint use_blocks=True,
+            bint coerce_temporal_nanoseconds=False
     ):
         """
         Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
@@ -868,8 +867,7 @@ cdef class _PandasConvertible(_Weakrefable):
             split_blocks=split_blocks,
             self_destruct=self_destruct,
             maps_as_pydicts=maps_as_pydicts,
-            coerce_temporal_nanoseconds=coerce_temporal_nanoseconds,
-            use_blocks=use_blocks,
+            coerce_temporal_nanoseconds=coerce_temporal_nanoseconds
         )
         return self._to_pandas(options, categories=categories,
                                ignore_metadata=ignore_metadata,

python/pyarrow/pandas-shim.pxi

@@ -38,7 +38,7 @@ cdef class _PandasAPIShim(object):
         object _array_like_types, _is_extension_array_dtype, _lock
         bint has_sparse
         bint _pd024
-        bint _is_v1, _is_ge_v21
+        bint _is_v1, _is_ge_v21, _is_ge_v3
 
     def __init__(self):
         self._lock = Lock()
@@ -79,6 +79,7 @@ cdef class _PandasAPIShim(object):
 
         self._is_v1 = self._loose_version < Version('2.0.0')
         self._is_ge_v21 = self._loose_version >= Version('2.1.0')
+        self._is_ge_v3 = self._loose_version >= Version('3.0.0.dev0')
 
         self._compat_module = pdcompat
         self._data_frame = pd.DataFrame
@@ -169,6 +170,10 @@ cdef class _PandasAPIShim(object):
         self._check_import()
         return self._is_ge_v21
 
+    def is_ge_v3(self):
+        self._check_import()
+        return self._is_ge_v3
+
     @property
     def categorical_type(self):
         self._check_import()

python/pyarrow/pandas_compat.py

@@ -778,7 +778,18 @@ def table_to_dataframe(
     column_names = table.column_names
     result = pa.lib.table_to_blocks(options, table, categories,
                                     list(ext_columns_dtypes.keys()))
-    if options["use_blocks"]:
+    if _pandas_api.is_ge_v3():
+        print("using new API")
+        from pandas.api.internals import create_dataframe_from_blocks
+
+        blocks = [
+            _reconstruct_block(
+                item, column_names, ext_columns_dtypes, return_block=False)
+            for item in result
+        ]
+        df = create_dataframe_from_blocks(blocks, index=index, columns=columns)
+        return df
+    else:
         from pandas.core.internals import BlockManager
         from pandas import DataFrame
 
@@ -793,16 +804,6 @@ def table_to_dataframe(
         else:
             df = DataFrame(mgr)
         return df
-    else:
-        from pandas.api.internals import create_dataframe_from_blocks
-
-        blocks = [
-            _reconstruct_block(
-                item, column_names, ext_columns_dtypes, return_block=False)
-            for item in result
-        ]
-        df = create_dataframe_from_blocks(blocks, index=index, columns=columns)
-        return df
 
 
 # Set of the string repr of all numpy dtypes that can be stored in a pandas