Tom Kooij
2016-09-12 08:01:30 UTC
===========================
Announcing PyTables 3.3.0
===========================
We are happy to announce PyTables 3.3.0.
What's new
==========
- Single codebase Python 2 and 3 support (PR #493).
- Internal Blosc version updated to 1.11.1 (closes :issue:`541`)
- Full BitShuffle support for new Blosc versions (>= 1.8).
- It is now possible to remove all rows from a table.
- It is now possible to read reference types by dereferencing them as
numpy array of objects (closes :issue:`518` and :issue:`519`).
Thanks to Ehsan Azar
- Fixed Windows 32 and 64-bit builds.
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/tag/v3.3.0
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
.. Local Variables:
.. mode: rst
.. coding: utf-8
.. fill-column: 72
.. End:
Announcing PyTables 3.3.0
===========================
We are happy to announce PyTables 3.3.0.
What's new
==========
- Single codebase Python 2 and 3 support (PR #493).
- Internal Blosc version updated to 1.11.1 (closes :issue:`541`)
- Full BitShuffle support for new Blosc versions (>= 1.8).
- It is now possible to remove all rows from a table.
- It is now possible to read reference types by dereferencing them as
numpy array of objects (closes :issue:`518` and :issue:`519`).
Thanks to Ehsan Azar
- Fixed Windows 32 and 64-bit builds.
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/tag/v3.3.0
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
.. Local Variables:
.. mode: rst
.. coding: utf-8
.. fill-column: 72
.. End: