nanoarrow for Python

The nanoarrow Python package provides bindings to the nanoarrow C library. Like the nanoarrow C library, it provides tools to facilitate the use of the Arrow C Data and Arrow C Stream interfaces.

Installation

The nanoarrow Python bindings are available from PyPI and conda-forge:

pip install nanoarrow
conda install nanoarrow -c conda-forge

Development versions (based on the main branch) are also available:

pip install --extra-index-url https://pypi.fury.io/arrow-nightlies/ \
    --prefer-binary --pre nanoarrow

If you can import the namespace, you're good to go!

import nanoarrow as na

Data types, arrays, and array streams

The Arrow C Data and Arrow C Stream interfaces are comprised of three structures: the ArrowSchema which represents a data type of an array, the ArrowArray which represents the values of an array, and an ArrowArrayStream, which represents zero or more ArrowArrays with a common ArrowSchema. These concepts map to the nanoarrow.Schema, nanoarrow.Array, and nanoarrow.ArrayStream in the Python package.

na.int32()

<Schema> int32

na.Array([1, 2, 3], na.int32())

nanoarrow.Array<int32>[3]
1
2
3

The nanoarrow.Array can accommodate arrays with any number of chunks, reflecting the reality that many array containers (e.g., pyarrow.ChunkedArray, polars.Series) support this.

chunked = na.Array.from_chunks([[1, 2, 3], [4, 5, 6]], na.int32())
chunked

nanoarrow.Array<int32>[6]
1
2
3
4
5
6

Whereas chunks of an Array are always fully materialized when the object is constructed, the chunks of an ArrayStream have not necessarily been resolved yet.

stream = na.ArrayStream(chunked)
stream

nanoarrow.ArrayStream<int32>

with stream:
    for chunk in stream:
        print(chunk)

nanoarrow.Array<int32>[3]
1
2
3
nanoarrow.Array<int32>[3]
4
5
6

The nanoarrow.ArrayStream also provides an interface to nanoarrow's Arrow IPC reader:

url = "https://github.com/apache/arrow-experiments/raw/main/data/arrow-commits/arrow-commits.arrows"
na.ArrayStream.from_url(url)

nanoarrow.ArrayStream<non-nullable struct<commit: string, time: timestamp('us', 'UTC'), files: int3...>

These objects implement the Arrow PyCapsule interface for both producing and consuming and are interchangeable with pyarrow objects in many cases:

import pyarrow as pa

pa.field(na.int32())

pyarrow.Field<: int32>

pa.chunked_array(chunked)

<pyarrow.lib.ChunkedArray object at 0x12a49a250>
[
  [
    1,
    2,
    3
  ],
  [
    4,
    5,
    6
  ]
]

pa.array(chunked.chunk(1))

<pyarrow.lib.Int32Array object at 0x11b552500>
[
  4,
  5,
  6
]

na.Array(pa.array([10, 11, 12]))

nanoarrow.Array<int64>[3]
10
11
12

na.Schema(pa.string())

<Schema> string

Low-level C library bindings

The nanoarrow Python package also provides lower level wrappers around Arrow C interface structures. You can create these using nanoarrow.c_schema(), nanoarrow.c_array(), and nanoarrow.c_array_stream().

Schemas

Use nanoarrow.c_schema() to convert an object to an ArrowSchema and wrap it as a Python object. This works for any object implementing the Arrow PyCapsule Interface (e.g., pyarrow.Schema, pyarrow.DataType, and pyarrow.Field).

na.c_schema(pa.decimal128(10, 3))

<nanoarrow.c_schema.CSchema decimal128(10, 3)>
- format: 'd:10,3'
- name: ''
- flags: 2
- metadata: NULL
- dictionary: NULL
- children[0]:

Using c_schema() is a good fit for testing and for ephemeral schema objects that are being passed from one library to another. To extract the fields of a schema in a more convenient form, use Schema():

schema = na.Schema(pa.decimal128(10, 3))
schema.precision, schema.scale

(10, 3)

The CSchema object cleans up after itself: when the object is deleted, the underlying ArrowSchema is released.

Arrays

You can use nanoarrow.c_array() to convert an array-like object to an ArrowArray, wrap it as a Python object, and attach a schema that can be used to interpret its contents. This works for any object implementing the Arrow PyCapsule Interface (e.g., pyarrow.Array, pyarrow.RecordBatch).

na.c_array(["one", "two", "three", None], na.string())

<nanoarrow.c_array.CArray string>
- length: 4
- offset: 0
- null_count: 1
- buffers: (4754305168, 4754307808, 4754310464)
- dictionary: NULL
- children[0]:

Using c_array() is a good fit for testing and for ephemeral array objects that are being passed from one library to another. For a higher level interface, use Array():

array = na.Array(["one", "two", "three", None], na.string())
array.to_pylist()

['one', 'two', 'three', None]

array.buffers

(nanoarrow.c_lib.CBufferView(bool[1 b] 11100000),
 nanoarrow.c_lib.CBufferView(int32[20 b] 0 3 6 11 11),
 nanoarrow.c_lib.CBufferView(string[11 b] b'onetwothree'))

Advanced users can create arrays directly from buffers using c_array_from_buffers():

na.c_array_from_buffers(
    na.string(),
    2,
    [None, na.c_buffer([0, 3, 6], na.int32()), b"abcdef"]
)

<nanoarrow.c_array.CArray string>
- length: 2
- offset: 0
- null_count: 0
- buffers: (0, 5002908320, 4999694624)
- dictionary: NULL
- children[0]:

Array streams

You can use nanoarrow.c_array_stream() to wrap an object representing a sequence of CArrays with a common CSchema to an ArrowArrayStream and wrap it as a Python object. This works for any object implementing the Arrow PyCapsule Interface (e.g., pyarrow.RecordBatchReader, pyarrow.ChunkedArray).

pa_batch = pa.record_batch({"col1": [1, 2, 3]})
reader = pa.RecordBatchReader.from_batches(pa_batch.schema, [pa_batch])
array_stream = na.c_array_stream(reader)
array_stream

<nanoarrow.c_array_stream.CArrayStream>
- get_schema(): struct<col1: int64>

You can pull the next array from the stream using .get_next() or use it like an iterator. The .get_next() method will raise StopIteration when there are no more arrays in the stream.

for array in array_stream:
    print(array)

<nanoarrow.c_array.CArray struct<col1: int64>>
- length: 3
- offset: 0
- null_count: 0
- buffers: (0,)
- dictionary: NULL
- children[1]:
  'col1': <nanoarrow.c_array.CArray int64>
    - length: 3
    - offset: 0
    - null_count: 0
    - buffers: (0, 2642948588352)
    - dictionary: NULL
    - children[0]:

Use ArrayStream() for a higher level interface:

reader = pa.RecordBatchReader.from_batches(pa_batch.schema, [pa_batch])
na.ArrayStream(reader).read_all()

nanoarrow.Array<non-nullable struct<col1: int64>>[3]
{'col1': 1}
{'col1': 2}
{'col1': 3}

Development

Python bindings for nanoarrow are managed with setuptools. This means you can build the project using:

git clone https://github.com/apache/arrow-nanoarrow.git
cd arrow-nanoarrow/python
# Build dependencies:
# pip install meson meson-python cython
pip install -e . --no-build-isolation

Tests use pytest:

# Install dependencies
pip install -e ".[test]"

# Run tests
pytest -vvx

CMake is currently required to ensure that the vendored copy of nanoarrow in the Python package stays in sync with the nanoarrow sources in the working tree.

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SHA256	`5f466ff39d74a5e9f08bc8f8cac252f3a2380b9c10b71e092a365af6e758a89a`
MD5	`f098caf074daee04ebd0b0d166b5015d`
BLAKE2b-256	`f952bdb18fa566d0084e41ac3c6e9a56e17ba68feeb9554b2d7595a54d9813b1`

Algorithm	Hash digest
SHA256	`56078876f9ec3594c95d9f75f07ef025fe84a63331fcd1f5f8b36b8a1ce3a210`
MD5	`51769a3b63577381a9a7d574aa6aa02b`
BLAKE2b-256	`5680ac94fe89949487ec18b86b17713b4c915a9fd7c8a4c68453d1b022e51804`

Algorithm	Hash digest
SHA256	`e6c3949a9ee7b5e995ea52feb526d84a395b9dfa8eb611fd18d479ec687d6693`
MD5	`1acd2a4001e77f68c829c38ffaa4f4a8`
BLAKE2b-256	`cb02367fe9d35c512d2cdf72ff4473f6aa310dc8a2b5eb0ccd7edccbceb6e461`

Algorithm	Hash digest
SHA256	`4eff07a0be9572a29f5ca624b51759dac2399f819f440fac94c7557eac07d18c`
MD5	`797aa9ed84a09bce9e3203ec7b62e712`
BLAKE2b-256	`95f84aacb1ad0a9b61c1fc134e2e53a69f878270ad55080302cbd0015cd55a1c`

Algorithm	Hash digest
SHA256	`d6337ab980800f06abe8356f1d774458046d654cf646019618388160de84e234`
MD5	`7cba960bb180ab324617742747d602c9`
BLAKE2b-256	`26f50ae8362c503a257393a30760e7ea17dcc3a5625859716d329f852f45c52b`

Algorithm	Hash digest
SHA256	`d661becacd37a1745e97596fe59a7723a9e330383fd80180f9f7fce31ba609c6`
MD5	`7b83cb79d41fcbd4c8185ff06419756d`
BLAKE2b-256	`63a08aae6d5dd6291b7d93962c44d85d56294209152a6de3bf69ac43785d605e`

Algorithm	Hash digest
SHA256	`03af782b942883b64205ea0ce61a74559dfe4980c24df7c9dfdcef8234550a74`
MD5	`a672a86ddc57dead69db1df9f480c87b`
BLAKE2b-256	`f8d4d95f6f3d8a7cffe6c4505adec89b41a3ff0ebb3b8cd9182c25677872de87`

Algorithm	Hash digest
SHA256	`fe171ae767a21043972670439f1dadc82cc1d8a7294e0692446421751626fcc8`
MD5	`6256920979ba5b9a4f850e0f51cc4689`
BLAKE2b-256	`bb9a5b0ef86b7874e6395bf34a878b42d3c8085ce6b9b224f6e5c42bba10eb0c`

Algorithm	Hash digest
SHA256	`3f1d484cdd88cfe908cb00c1d8b61420d1188a65a07c0272eba9bd7451a9fd1b`
MD5	`6e123ea769aba104b99200c42816c675`
BLAKE2b-256	`840e36128553bda9120457ac35182bc2cf692e3fe0de0c13d17d63333835be33`

Algorithm	Hash digest
SHA256	`94af9f150fbb4f657b35d8333bc68bcc7127b3f26cd4195d2ffbea92ecef83f0`
MD5	`fff4e0c4ec680440f7739c36b0d4b4fe`
BLAKE2b-256	`9a30ee0355979c07b60457dba2ee0883c5d0b96f1db83a22b6c0e9d563a7fbd2`

Algorithm	Hash digest
SHA256	`deaf08f372f145282e432639cb0507f05ce67bc56cb7208211ff1a2502432249`
MD5	`6f7b3362245b915cd25aaf941f900a83`
BLAKE2b-256	`ef489cc42d75b96a127f8bdf9710f1445bf593d889ec33f44fd09e3edc1bebe8`

nanoarrow 0.9.0

nanoarrow for Python

Installation

Data types, arrays, and array streams

Low-level C library bindings

Schemas

Arrays

Array streams

Development

Project links

Key dates

Owner

Credits

License

Requires

Provides Extra

Download files

Source Distribution

Built Distributions

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Algorithm	Hash digest
SHA256	`450d22d9c6f909627c0b2bcc6a50bc8aeddda2eb0e780427185cfdd8b6c887d8`
MD5	`9db08110258eda2b899acbacc4a25b91`
BLAKE2b-256	`4b11f54b96774c852e2ea0584b5db8d7562af9ecf1c35f50f7eb0bf7cbc98d74`

Algorithm	Hash digest
SHA256	`374eb1e3c600252049c5294c82530b237bbb9c6e28e67e5bfa213d03595e152c`
MD5	`95d7e8760a30a85296779a69897d429f`
BLAKE2b-256	`76d16dd67a5589dda71e673b0ad4afb2133fed23e204c88359ce0231a604c678`

Algorithm	Hash digest
SHA256	`81f3e7d11d3da2b3ee18ba095d85af4b5d5db8984474ef78b6989f8bc192e036`
MD5	`1a1eb05b9f2240617572ebb210104385`
BLAKE2b-256	`f755e7ac81be0aad41f643a1b59f6a1224b014269281caa468e73d8ed65d346f`

Algorithm	Hash digest
SHA256	`eac37cb506193e728b93f6563e344213e840ff388b29384b8ba46eb30ab7f9d3`
MD5	`d311816e3457f254b24a7358fbf8ccfa`
BLAKE2b-256	`d3fe832e16bafc29d357e4235cc6308d9864d37424c3c7675ac13214e0ef738b`

Algorithm	Hash digest
SHA256	`dc0a4b6e1138d7ab83b08ec7b238f54e5a900649ef8e880dd9530dc7435777ba`
MD5	`5dd3ce92b70acba15649d4a12c4a2a81`
BLAKE2b-256	`fe49219c37e918d46e0ef011365d290499f6acecd56102b503ff2bc7714d0c3b`

Algorithm	Hash digest
SHA256	`68dbc4e6352b2da9db161292121b7622285d0fe07ec5d7b69eb09ad2dc0b04ab`
MD5	`332139be82fc457a55e94882ec02f222`
BLAKE2b-256	`ace48a4db49b233bcc7c150d003d6a5541e8d6c6f2c54102f8392b10304d388c`

Algorithm	Hash digest
SHA256	`f8a6e4b45b7453c1729816d75df02188f88dff9f0957ea6ed771affa35c0349d`
MD5	`4a1ad18b845d44bcd5fd71dd21d43396`
BLAKE2b-256	`4526a9fda752056ec5b96753a56cd13c45e5cb438e78740bf5a69661e9548530`

Algorithm	Hash digest
SHA256	`926ff5bd72cc8c3b478e462c2fdd7b9797f2067be25c9fd5cf655ed152a06409`
MD5	`fc44d06ae0e4a4767e769ed15b936d8d`
BLAKE2b-256	`974154ce0bf22bea0b6e24b8bb36b426475e45ca18f6e2dd57b56ae738f01936`

Algorithm	Hash digest
SHA256	`9968abd0e68fc12381abbd01d9181562e0acc22bc231401ed9b85498cb34caf9`
MD5	`d248cce5639f3b9b8ddd3a63e1a2e0ed`
BLAKE2b-256	`9380b63b2482d1410ce2ac75b4dff4a661c80aa969a19163482c8c773bf37d4a`

Algorithm	Hash digest
SHA256	`dc55d746719380048cfc56740e69d7442cfba83a0b46bebf4218e896ebb97d0e`
MD5	`2913137ef79bdd178211b28adf057ea4`
BLAKE2b-256	`8e74d392aba2402e72549e7439e7ee9fbccd65c17b67b479a21ef3aca7dd87ea`