---
id: pcodec
version: "1.0.3"
license: unclear
license_treatment: unclear
maintenance: active
---

# pcodec — Good compression for numerical sequences

License: unclear · Maintenance: active · Downloads: 120.4K/mo

## What it is and what it does

Pcodec is a Python binding to a Rust-based codec designed specifically for compressing numerical sequences. It takes numpy arrays, applies a specialized compression algorithm tuned for numerical data patterns, and produces a byte string that can be decompressed back to the original array. The package exposes a minimal API: `simple_compress()` and `simple_decompress()` functions that handle the core workflow, plus configuration options via `ChunkConfig`.

The package depends only on numpy and is distributed as precompiled wheels for modern Python versions (3.10–3.12) across common platforms. Maintenance is active, with recent releases and repository commits. However, the license status is undeclared in the package metadata, which is a significant gap for adoption in commercial or legally sensitive contexts.

Use it for:

- Compress large numpy arrays of floating-point or integer data for storage or transmission.
- Reduce memory footprint of time-series or sensor data in data pipelines.
- Archive numerical datasets with better compression than generic algorithms.
- Embed numerical compression in scientific computing workflows that already use numpy.
- Benchmark or compare specialized numerical codecs against standard compression libraries.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Pcodec compresses and decompresses numerical sequences using a specialized codec optimized for numerical data, exposing a simple Python API backed by Rust.

Yes, with conditions. Install if you need specialized compression for numerical sequences and can verify the license terms in the repository. The package is actively maintained, has no known vulnerabilities, and offers a clean API. However, the undeclared license is a blocker for any project with legal compliance requirements; resolve that first. Also confirm that Python 3.10–3.12 wheels cover your deployment targets.

## Install

pip install pcodec
uv add pcodec
poetry add pcodec

## Installing pcodec

Before you install: Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.10–3.12 across macOS, Linux (x86_64, ARM, ARMv7, i686), and Windows. Active maintenance with a release 13 days old and recent commits.

License in practice: License status is unclear—no SPDX identifier or raw license text is declared in package metadata. Verify the actual license terms in the repository before adopting in proprietary or copyleft-sensitive projects.

Quickstart:

import numpy as np
from pcodec import standalone, ChunkConfig

nums = np.random.normal(size=1000000)
compressed = standalone.simple_compress(nums, ChunkConfig())
recovered = standalone.simple_decompress(compressed)

Requires numpy; compiled extension wheels are provided for Python 3.10–3.12 only.

Verify before relying:

- Actual license terms and conditions—metadata declares neither SPDX nor raw license text.
- Python version support beyond 3.10–3.12 (wheels not listed for 3.9 or 3.13+).
- Compression ratio and speed benchmarks relative to standard codecs (zlib, lz4, etc.).
- Whether lossless recovery is guaranteed for all numerical types or only specific dtypes.

## Package facts

- License: not declared (unclear)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 120.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

numerical data compression, compress numpy arrays, codec for time series, lossless numerical compression, sequence compression library, fast array compression, numpy compression codec, compression, numerical-data, rust-binding

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