===========================
Announcing PyTables 3.5.1
===========================
We are happy to announce PyTables 3.5.1.
What's new
==========
This is a minor version of PyTables. The main feature added is that
the padding in original HDF5 files is respected when copied via PyTables.
Also, when the `description` is a NumPy struct array with padding, this
is honored now. The previous behaviour (i.e. getting rid of paddings) can
be replicated by passing the new `allow_padding` parameter when opening
a file.
Also, support for AVX2 has been added on Windows.
In case you want to know more in detail what has changed in this
You can install it via pip or download a source package with generated
PDF and HTML docs 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.
Resources
=========
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