==============================
Announcing python-blosc 1.11.1
==============================

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

This is a maintenance release.  Besides coming with the latest C-Blosc
sources (1.21.3), there are new arm64 wheels for MacOS.

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 (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 contain 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 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:

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


Twitter
=======

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


----

  **Enjoy data!**


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