Using pyarrow from C++ and Cython Code

pyarrow provides both a Cython and C++ API, allowing your own native code to interact with pyarrow objects.

C++ API

The Arrow C++ header files are bundled with a pyarrow installation. To get the absolute path to this directory (like numpy.get_include()), use:

import pyarrow as pa
pa.get_include()

Assuming the path above is on your compiler’s include path, the pyarrow API can be included using the following directive:

#include <arrow/python/pyarrow.h>

This will not include other parts of the Arrow API, which you will need to include yourself (for example arrow/api.h).

When building C extensions that use the Arrow C++ libraries, you must add appropriate linker flags. We have provided functions pyarrow.get_libraries and pyarrow.get_library_dirs which return a list of library names and likely library install locations (if you installed pyarrow with pip or conda). These must be included when declaring your C extensions with setuptools (see below).

Initializing the API

int import_pyarrow()

Initialize inner pointers of the pyarrow API. On success, 0 is returned. Otherwise, -1 is returned and a Python exception is set.

It is mandatory to call this function before calling any other function in the pyarrow C++ API. Failing to do so will likely lead to crashes.

Wrapping and Unwrapping

pyarrow provides the following functions to go back and forth between Python wrappers (as exposed by the pyarrow Python API) and the underlying C++ objects.

bool arrow::py::is_array(PyObject *obj)

Return whether obj wraps an Arrow C++ Array pointer; in other words, whether obj is a pyarrow.Array instance.

bool arrow::py::is_batch(PyObject *obj)

Return whether obj wraps an Arrow C++ RecordBatch pointer; in other words, whether obj is a pyarrow.RecordBatch instance.

bool arrow::py::is_buffer(PyObject *obj)

Return whether obj wraps an Arrow C++ Buffer pointer; in other words, whether obj is a pyarrow.Buffer instance.

bool arrow::py::is_data_type(PyObject *obj)

Return whether obj wraps an Arrow C++ DataType pointer; in other words, whether obj is a pyarrow.DataType instance.

bool arrow::py::is_field(PyObject *obj)

Return whether obj wraps an Arrow C++ Field pointer; in other words, whether obj is a pyarrow.Field instance.

bool arrow::py::is_scalar(PyObject *obj)

Return whether obj wraps an Arrow C++ Scalar pointer; in other words, whether obj is a pyarrow.Scalar instance.

bool arrow::py::is_schema(PyObject *obj)

Return whether obj wraps an Arrow C++ Schema pointer; in other words, whether obj is a pyarrow.Schema instance.

bool arrow::py::is_table(PyObject *obj)

Return whether obj wraps an Arrow C++ Table pointer; in other words, whether obj is a pyarrow.Table instance.

bool arrow::py::is_tensor(PyObject *obj)

Return whether obj wraps an Arrow C++ Tensor pointer; in other words, whether obj is a pyarrow.Tensor instance.

bool arrow::py::is_sparse_coo_tensor(PyObject *obj)

Return whether obj wraps an Arrow C++ SparseCOOTensor pointer; in other words, whether obj is a pyarrow.SparseCOOTensor instance.

bool arrow::py::is_sparse_csc_matrix(PyObject *obj)

Return whether obj wraps an Arrow C++ SparseCSCMatrix pointer; in other words, whether obj is a pyarrow.SparseCSCMatrix instance.

bool arrow::py::is_sparse_csf_tensor(PyObject *obj)

Return whether obj wraps an Arrow C++ SparseCSFTensor pointer; in other words, whether obj is a pyarrow.SparseCSFTensor instance.

bool arrow::py::is_sparse_csr_matrix(PyObject *obj)

Return whether obj wraps an Arrow C++ SparseCSRMatrix pointer; in other words, whether obj is a pyarrow.SparseCSRMatrix instance.

The following functions expect a pyarrow object, unwrap the underlying Arrow C++ API pointer, and return it as a Result object. An error may be returned if the input object doesn’t have the expected type.

Result<std::shared_ptr<Array>> arrow::py::unwrap_array(PyObject *obj)

Unwrap and return the Arrow C++ Array pointer from obj.

Result<std::shared_ptr<RecordBatch>> arrow::py::unwrap_batch(PyObject *obj)

Unwrap and return the Arrow C++ RecordBatch pointer from obj.

Result<std::shared_ptr<Buffer>> arrow::py::unwrap_buffer(PyObject *obj)

Unwrap and return the Arrow C++ Buffer pointer from obj.

Result<std::shared_ptr<DataType>> arrow::py::unwrap_data_type(PyObject *obj)

Unwrap and return the Arrow C++ DataType pointer from obj.

Result<std::shared_ptr<Field>> arrow::py::unwrap_field(PyObject *obj)

Unwrap and return the Arrow C++ Field pointer from obj.

Result<std::shared_ptr<Scalar>> arrow::py::unwrap_scalar(PyObject *obj)

Unwrap and return the Arrow C++ Scalar pointer from obj.

Result<std::shared_ptr<Schema>> arrow::py::unwrap_schema(PyObject *obj)

Unwrap and return the Arrow C++ Schema pointer from obj.

Result<std::shared_ptr<Table>> arrow::py::unwrap_table(PyObject *obj)

Unwrap and return the Arrow C++ Table pointer from obj.

Result<std::shared_ptr<Tensor>> arrow::py::unwrap_tensor(PyObject *obj)

Unwrap and return the Arrow C++ Tensor pointer from obj.

Result<std::shared_ptr<SparseCOOTensor>> arrow::py::unwrap_sparse_coo_tensor(PyObject *obj)

Unwrap and return the Arrow C++ SparseCOOTensor pointer from obj.

Result<std::shared_ptr<SparseCSCMatrix>> arrow::py::unwrap_sparse_csc_matrix(PyObject *obj)

Unwrap and return the Arrow C++ SparseCSCMatrix pointer from obj.

Result<std::shared_ptr<SparseCSFTensor>> arrow::py::unwrap_sparse_csf_tensor(PyObject *obj)

Unwrap and return the Arrow C++ SparseCSFTensor pointer from obj.

Result<std::shared_ptr<SparseCSRMatrix>> arrow::py::unwrap_sparse_csr_matrix(PyObject *obj)

Unwrap and return the Arrow C++ SparseCSRMatrix pointer from obj.

The following functions take an Arrow C++ API pointer and wrap it in a pyarray object of the corresponding type. A new reference is returned. On error, NULL is returned and a Python exception is set.

PyObject *arrow::py::wrap_array(const std::shared_ptr<Array> &array)

Wrap the Arrow C++ array in a pyarrow.Array instance.

PyObject *arrow::py::wrap_batch(const std::shared_ptr<RecordBatch> &batch)

Wrap the Arrow C++ record batch in a pyarrow.RecordBatch instance.

PyObject *arrow::py::wrap_buffer(const std::shared_ptr<Buffer> &buffer)

Wrap the Arrow C++ buffer in a pyarrow.Buffer instance.

PyObject *arrow::py::wrap_data_type(const std::shared_ptr<DataType> &data_type)

Wrap the Arrow C++ data_type in a pyarrow.DataType instance.

PyObject *arrow::py::wrap_field(const std::shared_ptr<Field> &field)

Wrap the Arrow C++ field in a pyarrow.Field instance.

PyObject *arrow::py::wrap_scalar(const std::shared_ptr<Scalar> &scalar)

Wrap the Arrow C++ scalar in a pyarrow.Scalar instance.

PyObject *arrow::py::wrap_schema(const std::shared_ptr<Schema> &schema)

Wrap the Arrow C++ schema in a pyarrow.Schema instance.

PyObject *arrow::py::wrap_table(const std::shared_ptr<Table> &table)

Wrap the Arrow C++ table in a pyarrow.Table instance.

PyObject *arrow::py::wrap_tensor(const std::shared_ptr<Tensor> &tensor)

Wrap the Arrow C++ tensor in a pyarrow.Tensor instance.

PyObject *arrow::py::wrap_sparse_coo_tensor(const std::shared_ptr<SparseCOOTensor> &sparse_tensor)

Wrap the Arrow C++ sparse_tensor in a pyarrow.SparseCOOTensor instance.

PyObject *arrow::py::wrap_sparse_csc_matrix(const std::shared_ptr<SparseCSCMatrix> &sparse_tensor)

Wrap the Arrow C++ sparse_tensor in a pyarrow.SparseCSCMatrix instance.

PyObject *arrow::py::wrap_sparse_csf_tensor(const std::shared_ptr<SparseCSFTensor> &sparse_tensor)

Wrap the Arrow C++ sparse_tensor in a pyarrow.SparseCSFTensor instance.

PyObject *arrow::py::wrap_sparse_csr_matrix(const std::shared_ptr<SparseCSRMatrix> &sparse_tensor)

Wrap the Arrow C++ sparse_tensor in a pyarrow.SparseCSRMatrix instance.

Cython API

The Cython API more or less mirrors the C++ API, but the calling convention can be different as required by Cython. In Cython, you don’t need to initialize the API as that will be handled automatically by the cimport directive.

Note

Classes from the Arrow C++ API are renamed when exposed in Cython, to avoid named clashes with the corresponding Python classes. For example, C++ Arrow arrays have the CArray type and Array is the corresponding Python wrapper class.

Wrapping and Unwrapping

The following functions expect a pyarrow object, unwrap the underlying Arrow C++ API pointer, and return it. NULL is returned (without setting an exception) if the input is not of the right type.

pyarrow.pyarrow_unwrap_array(obj) → shared_ptr[CArray]

Unwrap the Arrow C++ Array pointer from obj.

pyarrow.pyarrow_unwrap_batch(obj) → shared_ptr[CRecordBatch]

Unwrap the Arrow C++ RecordBatch pointer from obj.

pyarrow.pyarrow_unwrap_buffer(obj) → shared_ptr[CBuffer]

Unwrap the Arrow C++ Buffer pointer from obj.

pyarrow.pyarrow_unwrap_data_type(obj) → shared_ptr[CDataType]

Unwrap the Arrow C++ CDataType pointer from obj.

pyarrow.pyarrow_unwrap_field(obj) → shared_ptr[CField]

Unwrap the Arrow C++ Field pointer from obj.

pyarrow.pyarrow_unwrap_scalar(obj) → shared_ptr[CScalar]

Unwrap the Arrow C++ Scalar pointer from obj.

pyarrow.pyarrow_unwrap_schema(obj) → shared_ptr[CSchema]

Unwrap the Arrow C++ Schema pointer from obj.

pyarrow.pyarrow_unwrap_table(obj) → shared_ptr[CTable]

Unwrap the Arrow C++ Table pointer from obj.

pyarrow.pyarrow_unwrap_tensor(obj) → shared_ptr[CTensor]

Unwrap the Arrow C++ Tensor pointer from obj.

pyarrow.pyarrow_unwrap_sparse_coo_tensor(obj) → shared_ptr[CSparseCOOTensor]

Unwrap the Arrow C++ SparseCOOTensor pointer from obj.

pyarrow.pyarrow_unwrap_sparse_csc_matrix(obj) → shared_ptr[CSparseCSCMatrix]

Unwrap the Arrow C++ SparseCSCMatrix pointer from obj.

pyarrow.pyarrow_unwrap_sparse_csf_tensor(obj) → shared_ptr[CSparseCSFTensor]

Unwrap the Arrow C++ SparseCSFTensor pointer from obj.

pyarrow.pyarrow_unwrap_sparse_csr_matrix(obj) → shared_ptr[CSparseCSRMatrix]

Unwrap the Arrow C++ SparseCSRMatrix pointer from obj.

The following functions take a Arrow C++ API pointer and wrap it in a pyarray object of the corresponding type. An exception is raised on error.

pyarrow.pyarrow_wrap_array(const shared_ptr[CArray]& array) → object

Wrap the Arrow C++ array in a Python pyarrow.Array instance.

pyarrow.pyarrow_wrap_batch(const shared_ptr[CRecordBatch]& batch) → object

Wrap the Arrow C++ record batch in a Python pyarrow.RecordBatch instance.

pyarrow.pyarrow_wrap_buffer(const shared_ptr[CBuffer]& buffer) → object

Wrap the Arrow C++ buffer in a Python pyarrow.Buffer instance.

pyarrow.pyarrow_wrap_data_type(const shared_ptr[CDataType]& data_type) → object

Wrap the Arrow C++ data_type in a Python pyarrow.DataType instance.

pyarrow.pyarrow_wrap_field(const shared_ptr[CField]& field) → object

Wrap the Arrow C++ field in a Python pyarrow.Field instance.

pyarrow.pyarrow_wrap_resizable_buffer(const shared_ptr[CResizableBuffer]& buffer) → object

Wrap the Arrow C++ resizable buffer in a Python pyarrow.ResizableBuffer instance.

pyarrow.pyarrow_wrap_scalar(const shared_ptr[CScalar]& scalar) → object

Wrap the Arrow C++ scalar in a Python pyarrow.Scalar instance.

pyarrow.pyarrow_wrap_schema(const shared_ptr[CSchema]& schema) → object

Wrap the Arrow C++ schema in a Python pyarrow.Schema instance.

pyarrow.pyarrow_wrap_table(const shared_ptr[CTable]& table) → object

Wrap the Arrow C++ table in a Python pyarrow.Table instance.

pyarrow.pyarrow_wrap_tensor(const shared_ptr[CTensor]& tensor) → object

Wrap the Arrow C++ tensor in a Python pyarrow.Tensor instance.

pyarrow.pyarrow_wrap_sparse_coo_tensor(const shared_ptr[CSparseCOOTensor]& sparse_tensor) → object

Wrap the Arrow C++ COO sparse tensor in a Python pyarrow.SparseCOOTensor instance.

pyarrow.pyarrow_wrap_sparse_csc_matrix(const shared_ptr[CSparseCSCMatrix]& sparse_tensor) → object

Wrap the Arrow C++ CSC sparse tensor in a Python pyarrow.SparseCSCMatrix instance.

pyarrow.pyarrow_wrap_sparse_csf_tensor(const shared_ptr[CSparseCSFTensor]& sparse_tensor) → object

Wrap the Arrow C++ COO sparse tensor in a Python pyarrow.SparseCSFTensor instance.

pyarrow.pyarrow_wrap_sparse_csr_matrix(const shared_ptr[CSparseCSRMatrix]& sparse_tensor) → object

Wrap the Arrow C++ CSR sparse tensor in a Python pyarrow.SparseCSRMatrix instance.

Example

The following Cython module shows how to unwrap a Python object and call the underlying C++ object’s API.

# distutils: language=c++

from pyarrow.lib cimport *


def get_array_length(obj):
    # Just an example function accessing both the pyarrow Cython API
    # and the Arrow C++ API
    cdef shared_ptr[CArray] arr = pyarrow_unwrap_array(obj)
    if arr.get() == NULL:
        raise TypeError("not an array")
    return arr.get().length()

To build this module, you will need a slightly customized setup.py file (this is assuming the file above is named example.pyx):

from setuptools import setup
from Cython.Build import cythonize

import os
import numpy as np
import pyarrow as pa


ext_modules = cythonize("example.pyx")

for ext in ext_modules:
    # The Numpy C headers are currently required
    ext.include_dirs.append(np.get_include())
    ext.include_dirs.append(pa.get_include())
    ext.libraries.extend(pa.get_libraries())
    ext.library_dirs.extend(pa.get_library_dirs())

    if os.name == 'posix':
        ext.extra_compile_args.append('-std=c++11')

    # Try uncommenting the following line on Linux
    # if you get weird linker errors or runtime crashes
    # ext.define_macros.append(("_GLIBCXX_USE_CXX11_ABI", "0"))


setup(ext_modules=ext_modules)

Compile the extension:

python setup.py build_ext --inplace

Building Extensions against PyPI Wheels

The Python wheels have the Arrow C++ libraries bundled in the top level pyarrow/ install directory. On Linux and macOS, these libraries have an ABI tag like libarrow.so.17 which means that linking with -larrow using the linker path provided by pyarrow.get_library_dirs() will not work right out of the box. To fix this, you must run pyarrow.create_library_symlinks() once as a user with write access to the directory where pyarrow is installed. This function will attempt to create symlinks like pyarrow/libarrow.so. For example:

pip install pyarrow
python -c "import pyarrow; pyarrow.create_library_symlinks()"

Toolchain Compatibility (Linux)

The Python wheels for Linux are built using the PyPA manylinux images which use the CentOS devtoolset-9. In addition to the other notes above, if you are compiling C++ using these shared libraries, you will need to make sure you use a compatible toolchain as well or you might see a segfault during runtime.

Also, if you encounter errors when linking or loading the library, consider setting the _GLIBCXX_USE_CXX11_ABI preprocessor macro to 0 (for example by adding -D_GLIBCXX_USE_CXX11_ABI=0 to CFLAGS).

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