=========================== Announcing PyTables 2.3.1 ===========================
We are happy to announce PyTables 2.3.1. This is a bugfix release. Upgrading is recommended for users that are running PyTables in production environments.
What's new ==========
This release includes a small number of changes. It only fixes a couple of bugs that are considered serious even if they should not impact a large number of users:
- :issue:`113` caused installation of PyTables 2.3 to fail on hosts with multiple python versions installed. - :issue:`111` prevented to read scalar datasets of UnImplemented types.
In case you want to know more in detail what has changed in this version, have a look at: http://pytables.github.com/release_notes.html
You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/@VERSION@
For an on-line version of the manual, visit: http://pytables.github.com/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 1 tenth of a second.
About the HDF5 library:
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 (and numarray!) 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 Team