=========================== Announcing PyTables 3.3.0 ===========================
We are happy to announce PyTables 3.3.0.
What's new ==========
- Single codebase Python 2 and 3 support (PR #493). - Internal Blosc version updated to 1.11.1 (closes :issue:`541`) - Full BitShuffle support for new Blosc versions (>= 1.8). - It is now possible to remove all rows from a table. - It is now possible to read reference types by dereferencing them as numpy array of objects (closes :issue:`518` and :issue:`519`). Thanks to Ehsan Azar - Fixed Windows 32 and 64-bit builds.
In case you want to know more in detail what has changed in this version, please refer to: http://www.pytables.org/release_notes.html
You can install it via pip or download a source package with generated PDF and HTML docs from: https://github.com/PyTables/PyTables/releases/tag/v3.3.0
For an online version of the manual, visit: http://www.pytables.org/usersguide/index.html
What it is? ===========
PyTables is a library for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data with support for full 64-bit file addressing. PyTables runs on top of the HDF5 library and NumPy package for achieving maximum throughput and convenient use. PyTables includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second.
About PyTables: http://www.pytables.org
About the HDF5 library: http://hdfgroup.org/HDF5/
About NumPy: http://numpy.scipy.org/
Thanks to many users who provided feature improvements, patches, bug reports, support and suggestions. See the ``THANKS`` file in the distribution package for a (incomplete) list of contributors. Most specially, a lot of kudos go to the HDF5 and NumPy makers. Without them, PyTables simply would not exist.
Share your experience =====================
Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.
-- The PyTables Developers
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