Update documentation and testing.

python/doc/source/ipc.rst

@@ -317,9 +317,8 @@ An object can be reconstructed from its component-based representation using
 Serializing pandas Objects
 --------------------------
 
-We provide a serialization context that has optimized handling of pandas
-objects like ``DataFrame`` and ``Series``. This can be created with
-``pyarrow.pandas_serialization_context()``. Combined with component-based
+The default serialization context has optimized handling of pandas
+objects like ``DataFrame`` and ``Series``. Combined with component-based
 serialization above, this enables zero-copy transport of pandas DataFrame
 objects not containing any Python objects:
 
@@ -327,7 +326,7 @@ objects not containing any Python objects:
 
    import pandas as pd
    df = pd.DataFrame({'a': [1, 2, 3, 4, 5]})
-   context = pa.pandas_serialization_context()
+   context = pa.default_serialization_context()
    serialized_df = context.serialize(df)
    df_components = serialized_df.to_components()
    original_df = context.deserialize_components(df_components)

python/pyarrow/__init__.py

@@ -125,7 +125,6 @@ from pyarrow.ipc import (Message, MessageReader,
 localfs = LocalFileSystem.get_instance()
 
 from pyarrow.serialization import (default_serialization_context,
-                                   pandas_serialization_context,
                                    register_default_serialization_handlers,
                                    register_torch_serialization_handlers)
 

python/pyarrow/serialization.py

@@ -22,8 +22,7 @@ import sys
 import numpy as np
 
 from pyarrow.compat import builtin_pickle
-from pyarrow.lib import (SerializationContext, _default_serialization_context,
-                         frombuffer)
+from pyarrow.lib import SerializationContext, _default_serialization_context
 
 try:
     import cloudpickle
@@ -44,23 +43,6 @@ def _deserialize_numpy_array_list(data):
     return np.array(data[0], dtype=np.dtype(data[1]))
 
 
-def _pickle_to_buffer(x):
-    pickled = builtin_pickle.dumps(x, protocol=builtin_pickle.HIGHEST_PROTOCOL)
-    return frombuffer(pickled)
-
-
-def _load_pickle_from_buffer(data):
-    as_memoryview = memoryview(data)
-    if six.PY2:
-        return builtin_pickle.loads(as_memoryview.tobytes())
-    else:
-        return builtin_pickle.loads(as_memoryview)
-
-
-_serialize_numpy_array_pickle = _pickle_to_buffer
-_deserialize_numpy_array_pickle = _load_pickle_from_buffer
-
-
 # ----------------------------------------------------------------------
 # pandas-specific serialization matters
 
@@ -190,11 +172,3 @@ def default_serialization_context():
 
 
 register_default_serialization_handlers(_default_serialization_context)
-
-
-def pandas_serialization_context():
-    context = default_serialization_context()
-    context.register_type(np.ndarray, 'np.array',
-                          custom_serializer=_serialize_numpy_array_pickle,
-                          custom_deserializer=_deserialize_numpy_array_pickle)
-    return context

python/pyarrow/tests/test_convert_pandas.py

@@ -1613,7 +1613,7 @@ def _fully_loaded_dataframe_example():
 
 
 def _check_serialize_components_roundtrip(df):
-    ctx = pa.pandas_serialization_context()
+    ctx = pa.default_serialization_context()
 
     components = ctx.serialize(df).to_components()
     deserialized = ctx.deserialize_components(components)

python/pyarrow/tests/test_serialization.py

@@ -284,8 +284,6 @@ def test_clone():
 def test_primitive_serialization(large_buffer):
     for obj in PRIMITIVE_OBJECTS:
         serialization_roundtrip(obj, large_buffer)
-        serialization_roundtrip(obj, large_buffer,
-                                pa.pandas_serialization_context())
 
 
 def test_serialize_to_buffer():