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
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.
The sources and documentation are managed through github services at:
https://github.com/Blosc/python-blosc
Please follow @Blosc2 to get informed about the latest developments.
Enjoy data!