============================= 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:

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

More docs and examples are available in the documentation site:

http://python-blosc.blosc.org

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:

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

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**Enjoy data!**