==============================
Announcing python-blosc 1.11.2
==============================

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

This is a maintenance release.  Besides coming with the latest C-Blosc
sources (1.21.6), there are new wheels for Python 3.12, as well as
some fixes in the build process.

In addition to extensions, we are distributing library
binaries in the wheels too.  This way, people willing to use the C-Blosc
library can make use of these wheels to install the necessary development
files.  For details, see:
https://github.com/Blosc/c-blosc/blob/main/COMPILING_WITH_WHEELS.rst

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

https://github.com/Blosc/python-blosc/blob/main/RELEASE_NOTES.rst

More docs and examples are available in the documentation site:

https://www.blosc.org/python-blosc/python-blosc.html

**Important note**: There is a new Python-Blosc2 wrapper
on top of the next generation C-Blosc2 library.  Check it out at:
https://github.com/Blosc/python-blosc2

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

Blosc (https://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 contain data with relatively low entropy, like
sparse data, time series, grids with regular-spaced values, etc.

python-blosc (https://www.blosc.org/python-blosc/python-blosc.html) 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 command 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:

https://github.com/Blosc/python-blosc


Twitter
=======

Please follow @Blosc2 to get informed about the latest developments.


----

  **Enjoy data!**


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