[Numpy-discussion] ANN: PyTables 2.4.0 released
Fri Jul 20 15:40:43 CDT 2012
Announcing PyTables 2.4.0
We are happy to announce PyTables 2.4.0.
This is an incremental release which includes many changes to prepare
for future Python 3 support.
This release includes support for the float16 data type and read-only
support for variable length string attributes.
The handling of HDF5 errors has been improved. The user will no longer
see HDF5 error stacks dumped to the console. All HDF5 error messages
are trapped and attached to a proper Python exception.
Now PyTables only supports HDF5 v1.8.4+. All the code has been updated
to the new HDF5 API. Supporting only HDF5 1.8 series is beneficial for
Documentation has been improved.
As always, a large amount of bugs have been addressed and squashed as well.
In case you want to know more in detail what has changed in this
version, please refer to:
You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
For an online version of the manual, visit:
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 (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
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