ANN: python-blosc 1.9.0 released

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Francesc Alted

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Mar 29, 2020, 12:47:48 PM3/29/20
to Blosc, python-...@python.org
=============================
Announcing python-blosc 1.9.0
=============================

What is new?
============

In this release we got rid of the support for Python 2.7 and 3.5.
Also, we fixed the copy of the leftovers of a chunk when its size is not a
multiple of the typesize.  Although this is a very unusual situation,
it can certainly happen (e.g. https://github.com/Blosc/python-blosc/issues/220).
Finally, sources for C-Blosc v1.18.1 have been included.

For more info, you can have a look at the release notes in:


More docs and examples are available in the documentation site:



What is it?
===========

Blosc (http://www.blosc.org) is a high performance compressor optimized
for binary data.  It has been designed to transmit data to the processor
cache faster than the traditional, non-compressed, direct memory fetch
approach via a memcpy() OS call.  Blosc works well for compressing
numerical arrays that contains data with relatively low entropy, like
sparse data, time series, grids with regular-spaced values, etc.

python-blosc (http://python-blosc.blosc.org/) is the Python wrapper for
the Blosc compression library, with added functions (`compress_ptr()`
and `pack_array()`) for efficiently compressing NumPy arrays, minimizing
the number of memory copies during the process.  python-blosc can be
used to compress in-memory data buffers for transmission to other
machines, persistence or just as a compressed cache.

There is also a handy tool built on top of python-blosc called Bloscpack
(https://github.com/Blosc/bloscpack). It features a commmand line
interface that allows you to compress large binary datafiles on-disk.
It also comes with a Python API that has built-in support for
serializing and deserializing Numpy arrays both on-disk and in-memory at
speeds that are competitive with regular Pickle/cPickle machinery.


Sources repository
==================

The sources and documentation are managed through github services at:




----

  **Enjoy data!**

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