============================= Announcing python-blosc 1.7.0 ============================= What is new? ============ This is a maintenance release which takes care of several housekeepting tasks. Support for older versions of Python (2.6 and 3.3) has been removed from the codebase. A new version of C-Blosc (1.5.1) that now passes all unit and integration tests across all supported platforms has been included. Finally, a the vendored cpuinfo.py has been upgraded and the automatic tests on Windows via Appveyor have been upgraded to include a larger variety of Windows/Python combinations. A big thank you goes out to Daniel Stender from the Debian project for his continued efforts to package the Blosc stack -- including python-blosc -- for Debian. This also means it is likely that a recent version of python-blosc will be included in Buster. 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 ---- **Enjoy data!**