msgpack-numpy
Numpy data serialization using msgpack
Decision gist · record as of 2026-08-14
Yes, if you need to serialize NumPy arrays with msgpack and your project tolerates dormant maintenance. The package is stable, has no known vulnerabilities, and low install friction. However, verify compatibility with your Python version (last release was 2022-06-09) and confirm that read-only deserialized arrays and the object-dtype pickle fallback fit your workflow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy and msgpack as runtime dependencies; deserialized arrays are read-only and must be copied if modification is needed.
- Low install friction with a pure-Python wheel distribution.
- Maintenance is dormant—last release was 2022-06-09 with no commits since 2024-07-19—so expect no active bug fixes or feature updates.
License · maintenance · safety
BSD (permissive) — BSD license is permissive, allowing commercial and private use with minimal restrictions.
last release 2022-06-09 (1527 days) · last repo commit 2024-07-19 · 215 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,129,000 downloads/mo, #4,321 on PyPI
Alternatives
Verify before relying
import msgpack
import msgpack_numpy as m
m.patch()
# Now all msgpack calls handle numpy arrays
x_enc = msgpack.packb(x)
x_rec = msgpack.unpackb(x_enc)- Whether deserialized arrays' read-only constraint materially affects performance in typical workflows.
- Current compatibility with Python versions beyond 3.8, given the last release predates recent Python versions.
- Whether pickle fallback for object-dtype arrays introduces security concerns in untrusted data scenarios.
What it is and what it does
msgpack-numpy bridges NumPy and msgpack by adding custom encoders and decoders that let you serialize NumPy arrays and Python complex numbers into msgpack's compact binary format while preserving their data types. It works in two modes: monkey-patching msgpack globally to make all serialization NumPy-aware, or passing its encoder and decoder functions manually to msgpack routines.
The package trades some storage overhead for type fidelity—serialized data includes type information so deserialized arrays come back as the correct dtype. For object-dtype arrays, it falls back to pickle, which adds overhead and negates some of msgpack's efficiency gains. Deserialized arrays are read-only and must be copied if you need to modify them.
Use it for
- Serialize NumPy arrays for storage or network transmission while preserving dtype information across systems.
- Exchange numerical data between Python processes or services using msgpack's efficient binary protocol.
- Monkey-patch msgpack in a library or application to make all downstream serialization NumPy-aware without code changes.
- Manually encode/decode specific NumPy arrays in a custom serialization pipeline where msgpack is already in use.
- Handle Python complex numbers in msgpack serialization when NumPy arrays are not the primary payload.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to serialize NumPy arrays with msgpack and your project tolerates dormant maintenance.
The package is stable, has no known vulnerabilities, and low install friction. However, verify compatibility with your Python version (last release was 2022-06-09) and confirm that read-only deserialized arrays and the object-dtype pickle fallback fit your workflow.
Install
msgpack-numpy on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Maintenance is dormant—last release was 2022-06-09 with no commits since 2024-07-19—so expect no active bug fixes or feature updates.
Requires numpy and msgpack as runtime dependencies; deserialized arrays are read-only and must be copied if modification is needed.
License in practice
BSD license is permissive, allowing commercial and private use with minimal restrictions.
Quickstart
import msgpack
import msgpack_numpy as m
m.patch()
# Now all msgpack calls handle numpy arrays
x_enc = msgpack.packb(x)
x_rec = msgpack.unpackb(x_enc)Verify before relying
- Whether deserialized arrays' read-only constraint materially affects performance in typical workflows.
- Current compatibility with Python versions beyond 3.8, given the last release predates recent Python versions.
- Whether pickle fallback for object-dtype arrays introduces security concerns in untrusted data scenarios.
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpymsgpack |
| Maintenance | Dormant 1,527 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,129,000 / month, #4,321 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: msgpack_numpy-0.4.8-py2.py3-none-any.whl
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See also msgpack-numpy-opentensor · msgpack · u-msgpack-python · msgpack-python · json-numpy · numpyencoder · ormsgpack · modelcif · jsonpickle · stanio