===========================
 Announcing PyTables 3.6.1
===========================

We are happy to announce PyTables 3.6.1.


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
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/v3.6.1

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

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