Congrats Francesc!
On Tue, Jul 22, 2014 at 9:53 AM, Francesc Alted <fal...@gmail.com> wrote: > ====================== > Announcing bcolz 0.7.0 > ====================== > > What's new > ========== > > In this release, support for Python 3 has been added, Pandas and > HDF5/PyTables conversion, support for different compressors via latest > release of Blosc, and a new `iterblocks()` iterator. > > Also, intensive benchmarking has lead to an important tuning of buffer > sizes parameters so that compression and evaluation goes faster than > ever. Together, bcolz and the Blosc compressor, are finally fullfilling > the promise of accelerating memory I/O, at least for some real > scenarios: > > > http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb#Plots > > ``bcolz`` is a renaming of the ``carray`` project. The new goals for > the project are to create simple, yet flexible compressed containers, > that can live either on-disk or in-memory, and with some > high-performance iterators (like `iter()`, `where()`) for querying them. > > For more detailed info, see the release notes in: > https://github.com/Blosc/bcolz/wiki/Release-Notes > > > What it is > ========== > > bcolz provides columnar and compressed data containers. Column storage > allows for efficiently querying tables with a large number of columns. > It also allows for cheap addition and removal of column. In addition, > bcolz objects are compressed by default for reducing memory/disk I/O > needs. The compression process is carried out internally by Blosc, a > high-performance compressor that is optimized for binary data. > > bcolz can use numexpr internally so as to accelerate many vector and > query operations (although it can use pure NumPy for doing so too). > numexpr optimizes the memory usage and use several cores for doing the > computations, so it is blazing fast. Moreover, the carray/ctable > containers can be disk-based, and it is possible to use them for > seamlessly performing out-of-memory computations. > > bcolz has minimal dependencies (NumPy), comes with an exhaustive test > suite and fully supports both 32-bit and 64-bit platforms. Also, it is > typically tested on both UNIX and Windows operating systems. > > > Installing > ========== > > bcolz is in the PyPI repository, so installing it is easy: > > $ pip install -U bcolz > > > Resources > ========= > > Visit the main bcolz site repository at: > http://github.com/Blosc/bcolz > > Manual: > http://bcolz.blosc.org > > Home of Blosc compressor: > http://blosc.org > > User's mail list: > bc...@googlegroups.com > http://groups.google.com/group/bcolz > > License is the new BSD: > https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt > > > ---- > > **Enjoy data!** > > -- Francesc Alted > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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