PyTables 2.3.1 released

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.

Resources

About PyTables:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/

Acknowledgments

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.

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**Enjoy data!**

-- The PyTables Team