The Arrow PyCapsule Interface

Warning

The Arrow PyCapsule Interface should be considered experimental

Rationale

The C data interface and C stream interface allow moving Arrow data between different implementations of Arrow. However, these interfaces don’t specify how Python libraries should expose these structs to other libraries. Prior to this, many libraries simply provided export to PyArrow data structures, using the _import_from_c and _export_to_c methods. However, this always required PyArrow to be installed. In addition, those APIs could cause memory leaks if handled improperly.

This interface allows any library to export Arrow data structures to other libraries that understand the same protocol.

Goals

• Standardize the PyCapsule objects that represent ArrowSchema, ArrowArray, and ArrowArrayStream.

• Define standard methods that export Arrow data into such capsule objects, so that any Python library wanting to accept Arrow data as input can call the corresponding method instead of hardcoding support for specific Arrow producers.

Non-goals

• Standardize what public APIs should be used for import. This is left up to individual libraries.

PyCapsule Standard

When exporting Arrow data through Python, the C Data Interface / C Stream Interface structures should be wrapped in capsules. Capsules avoid invalid access by attaching a name to the pointer and avoid memory leaks by attaching a destructor. Thus, they are much safer than passing pointers as integers.

PyCapsule allows for a name to be associated with the capsule, allowing consumers to verify that the capsule contains the expected kind of data. To make sure Arrow structures are recognized, the following names must be used:

C Interface Type	PyCapsule Name
ArrowSchema	arrow_schema
ArrowArray	arrow_array
ArrowArrayStream	arrow_array_stream

Lifetime Semantics

The exported PyCapsules should have a destructor that calls the release callback of the Arrow struct, if it is not already null. This prevents a memory leak in case the capsule was never passed to another consumer.

If the capsule has been passed to a consumer, the consumer should have moved the data and marked the release callback as null, so there isn’t a risk of releasing data the consumer is using. Read more in the C Data Interface specification.

Just like in the C Data Interface, the PyCapsule objects defined here can only be consumed once.

For an example of a PyCapsule with a destructor, see Create a PyCapsule.

Export Protocol

The interface consists of three separate protocols:

• ArrowSchemaExportable, which defines the __arrow_c_schema__ method.

• ArrowArrayExportable, which defines the __arrow_c_array__ method.

• ArrowStreamExportable, which defines the __arrow_c_stream__ method.

ArrowSchema Export

Schemas, fields, and data types can implement the method __arrow_c_schema__.

__arrow_c_schema__(self) → object

Export the object as an ArrowSchema.

Returns:

A PyCapsule containing a C ArrowSchema representation of the object. The capsule must have a name of "arrow_schema".

ArrowArray Export

Arrays and record batches (contiguous tables) can implement the method __arrow_c_array__.

__arrow_c_array__(self, requested_schema: object | None = None) → Tuple[object, object]

Export the object as a pair of ArrowSchema and ArrowArray structures.

Parameters:

requested_schema (PyCapsule or None) – A PyCapsule containing a C ArrowSchema representation of a requested schema. Conversion to this schema is best-effort. See Schema Requests.

Returns:

A pair of PyCapsules containing a C ArrowSchema and ArrowArray, respectively. The schema capsule should have the name "arrow_schema" and the array capsule should have the name "arrow_array".

ArrowStream Export

Tables / DataFrames and streams can implement the method __arrow_c_stream__.

__arrow_c_stream__(self, requested_schema: object | None = None) → object

Export the object as an ArrowArrayStream.

Parameters:

requested_schema (PyCapsule or None) – A PyCapsule containing a C ArrowSchema representation of a requested schema. Conversion to this schema is best-effort. See Schema Requests.

Returns:

A PyCapsule containing a C ArrowArrayStream representation of the object. The capsule must have a name of "arrow_array_stream".

Schema Requests

In some cases, there might be multiple possible Arrow representations of the same data. For example, a library might have a single integer type, but Arrow has multiple integer types with different sizes and sign. As another example, Arrow has several possible encodings for an array of strings: 32-bit offsets, 64-bit offsets, string view, and dictionary-encoded. A sequence of strings could export to any one of these Arrow representations.

In order to allow the caller to request a specific representation, the __arrow_c_array__() and __arrow_c_stream__() methods take an optional requested_schema parameter. This parameter is a PyCapsule containing an ArrowSchema.

The callee should attempt to provide the data in the requested schema. However, if the callee cannot provide the data in the requested schema, they may return with the same schema as if None were passed to requested_schema.

If the caller requests a schema that is not compatible with the data, say requesting a schema with a different number of fields, the callee should raise an exception. The requested schema mechanism is only meant to negotiate between different representations of the same data and not to allow arbitrary schema transformations.

Protocol Typehints

The following typehints can be copied into your library to annotate that a function accepts an object implementing one of these protocols.

from typing import Tuple, Protocol
from typing_extensions import Self

class ArrowSchemaExportable(Protocol):
    def __arrow_c_schema__(self) -> object: ...

class ArrowArrayExportable(Protocol):
    def __arrow_c_array__(
        self,
        requested_schema: object | None = None
    ) -> Tuple[object, object]:
        ...

class ArrowStreamExportable(Protocol):
    def __arrow_c_stream__(
        self,
        requested_schema: object | None = None
    ) -> object:
        ...

Examples

Create a PyCapsule

To create a PyCapsule, use the PyCapsule_New function. The function must be passed a destructor function that will be called to release the data the capsule points to. It must first call the release callback if it is not null, then free the struct.

Below is the code to create a PyCapsule for an ArrowSchema. The code for ArrowArray and ArrowArrayStream is similar.

C

#include <Python.h>

void ReleaseArrowSchemaPyCapsule(PyObject* capsule) {
    struct ArrowSchema* schema =
        (struct ArrowSchema*)PyCapsule_GetPointer(capsule, "arrow_schema");
    if (schema->release != NULL) {
        schema->release(schema);
    }
    free(schema);
}

PyObject* ExportArrowSchemaPyCapsule() {
    struct ArrowSchema* schema =
        (struct ArrowSchema*)malloc(sizeof(struct ArrowSchema));
    // Fill in ArrowSchema fields
    // ...
    return PyCapsule_New(schema, "arrow_schema", ReleaseArrowSchemaPyCapsule);
}

Cython

cimport cpython
from libc.stdlib cimport malloc, free

cdef void release_arrow_schema_py_capsule(object schema_capsule):
    cdef ArrowSchema* schema = <ArrowSchema*>cpython.PyCapsule_GetPointer(
        schema_capsule, 'arrow_schema'
    )
    if schema.release != NULL:
        schema.release(schema)

    free(schema)

cdef object export_arrow_schema_py_capsule():
    cdef ArrowSchema* schema = <ArrowSchema*>malloc(sizeof(ArrowSchema))
    # It's recommended to immediately wrap the struct in a capsule, so
    # if subsequent lines raise an exception memory will not be leaked.
    schema.release = NULL
    capsule = cpython.PyCapsule_New(
        <void*>schema, 'arrow_schema', release_arrow_schema_py_capsule
    )
    # Fill in ArrowSchema fields:
    # schema.format = ...
    # ...
    return capsule

Consume a PyCapsule

To consume a PyCapsule, use the PyCapsule_GetPointer function to get the pointer to the underlying struct. Import the struct using your system’s Arrow C Data Interface import function. Only after that should the capsule be freed.

The below example shows how to consume a PyCapsule for an ArrowSchema. The code for ArrowArray and ArrowArrayStream is similar.

C

#include <Python.h>

// If the capsule is not an ArrowSchema, will return NULL and set an exception.
struct ArrowSchema* GetArrowSchemaPyCapsule(PyObject* capsule) {
  return PyCapsule_GetPointer(capsule, "arrow_schema");
}

Cython

cimport cpython

cdef ArrowSchema* get_arrow_schema_py_capsule(object capsule) except NULL:
    return <ArrowSchema*>cpython.PyCapsule_GetPointer(capsule, 'arrow_schema')

Backwards Compatibility with PyArrow

When interacting with PyArrow, the PyCapsule interface should be preferred over the _export_to_c and _import_from_c methods. However, many libraries will want to support a range of PyArrow versions. This can be done via Duck typing.

For example, if your library had an import method such as:

# OLD METHOD
def from_arrow(arr: pa.Array)
    array_import_ptr = make_array_import_ptr()
    schema_import_ptr = make_schema_import_ptr()
    arr._export_to_c(array_import_ptr, schema_import_ptr)
    return import_c_data(array_import_ptr, schema_import_ptr)

You can rewrite this method to support both PyArrow and other libraries that implement the PyCapsule interface:

# NEW METHOD
def from_arrow(arr)
    # Newer versions of PyArrow as well as other libraries with Arrow data
    # implement this method, so prefer it over _export_to_c.
    if hasattr(arr, "__arrow_c_array__"):
         schema_ptr, array_ptr = arr.__arrow_c_array__()
         return import_c_capsule_data(schema_ptr, array_ptr)
    elif isinstance(arr, pa.Array):
         # Deprecated method, used for older versions of PyArrow
         array_import_ptr = make_array_import_ptr()
         schema_import_ptr = make_schema_import_ptr()
         arr._export_to_c(array_import_ptr, schema_import_ptr)
         return import_c_data(array_import_ptr, schema_import_ptr)
    else:
        raise TypeError(f"Cannot import {type(arr)} as Arrow array data.")

You may also wish to accept objects implementing the protocol in your constructors. For example, in PyArrow, the array() and record_batch() constructors accept any object that implements the __arrow_c_array__() method protocol. Similarly, the PyArrow’s schema() constructor accepts any object that implements the __arrow_c_schema__() method.

Now if your library has an export to PyArrow function, such as:

# OLD METHOD
def to_arrow(self) -> pa.Array:
    array_export_ptr = make_array_export_ptr()
    schema_export_ptr = make_schema_export_ptr()
    self.export_c_data(array_export_ptr, schema_export_ptr)
    return pa.Array._import_from_c(array_export_ptr, schema_export_ptr)

You can rewrite this function to use the PyCapsule interface by passing your object to the array() constructor, which accepts any object that implements the protocol. An easy way to check if the PyArrow version is new enough to support this is to check whether pa.Array has the __arrow_c_array__ method.

import warnings

# NEW METHOD
def to_arrow(self) -> pa.Array:
    # PyArrow added support for constructing arrays from objects implementing
    # __arrow_c_array__ in the same version it added the method for it's own
    # arrays. So we can use hasattr to check if the method is available as
    # a proxy for checking the PyArrow version.
    if hasattr(pa.Array, "__arrow_c_array__"):
        return pa.array(self)
    else:
        array_export_ptr = make_array_export_ptr()
        schema_export_ptr = make_schema_export_ptr()
        self.export_c_data(array_export_ptr, schema_export_ptr)
        return pa.Array._import_from_c(array_export_ptr, schema_export_ptr)

Comparison with Other Protocols

Comparison to DataFrame Interchange Protocol

The DataFrame Interchange Protocol is another protocol in Python that allows for the sharing of data between libraries. This protocol is complementary to the DataFrame Interchange Protocol. Many of the objects that implement this protocol will also implement the DataFrame Interchange Protocol.

This protocol is specific to Arrow-based data structures, while the DataFrame Interchange Protocol allows non-Arrow data frames and arrays to be shared as well. Because of this, these PyCapsules can support Arrow-specific features such as nested columns.

This protocol is also much more minimal than the DataFrame Interchange Protocol. It just handles data export, rather than defining accessors for details like number of rows or columns.

In summary, if you are implementing this protocol, you should also consider implementing the DataFrame Interchange Protocol.

Comparison to __arrow_array__ protocol

The Controlling conversion to pyarrow.Array with the __arrow_array__ protocol protocol is a dunder method that defines how PyArrow should import an object as an Arrow array. Unlike this protocol, it is specific to PyArrow and isn’t used by other libraries. It is also limited to arrays and does not support schemas, tabular structures, or streams.