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Announcing PyTables 3.4
=========================
We are happy to announce PyTables 3.4.1. PyTables 3.4.0 was immediately
followed by a bugfix release 3.4.1. This announces both 3.4.0 and 3.4.1
What’s new
==========
The most important feature of PyTables 3.4 is support for HDF5 v1.10.x, while
maintaining compatiblity with HDF5 1.8.x. Also, the internal Blosc version
was updated to 1.11.3 which fixes a critical bug on big-endian machines.
The release includes many small bugfixes.
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/v3.4.1
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.
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 makers.
Without them, PyTables simply would not exist.
Share your experience
=====================
Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.
Enjoy data!
– The PyTables Developers