PyTables (Hierarchical Datasets) 2.0 released
Ivan Vilata i Balaguer
ivan at selidor.net
Fri Jul 13 13:37:58 CEST 2007
Announcing PyTables 2.0
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.
After more than one year of continuous development and about five months
of alpha, beta and release candidates, we are very happy to announce
that the PyTables 2.0 (final) is here. We are pretty confident that 2.0
is ready to be used in production scenarios, bringing higher
performance, better portability (specially in 64-bit environments) and
more stability than the 1.x series.
You can download a source package of the version 2.0 with
generated PDF and HTML docs and binaries for Windows from
For an on-line version of the manual, visit:
In case you want to know more in detail what has changed in this
version, have a look at ``RELEASE_NOTES.txt``. Find the HTML version
for this document at:
If you are a user of PyTables 1.x, probably it is worth for you to look
at ``MIGRATING_TO_2.x.txt`` file where you will find directions on how
to migrate your existing PyTables 1.x apps to the 2.0 version. You can
find an HTML version of this document at
Keep reading for an overview of the most prominent improvements in
PyTables 2.0 series.
New features of PyTables 2.0
- A complete refactoring of many, many modules in PyTables. With this,
the different parts of the code are much better integrated and code
redundancy is kept under a minimum. A lot of new optimizations have
been included as well, making working with it a smoother experience
than ever before.
- NumPy is finally at the core! That means that PyTables no longer
needs numarray in order to operate, although it continues to be
supported (as well as Numeric). This also means that you should be
able to run PyTables in scenarios combining Python 2.5 and 64-bit
platforms (these are a source of problems with numarray/Numeric
because they don't support this combination as of this writing).
- Most of the operations in PyTables have experimented noticeable
speed-ups (sometimes up to 2x, like in regular Python table
selections). This is a consequence of both using NumPy internally and
a considerable effort in terms of refactorization and optimization of
the new code.
- Combined conditions are finally supported for in-kernel selections.
So, now it is possible to perform complex selections like::
result = [ row['var3'] for row in
table.where('(var2 < 20) | (var1 == "sas")') ]
complex_cond = '((%s <= col5) & (col2 <= %s)) ' \
'| (sqrt(col1 + 3.1*col2 + col3*col4) > 3)'
result = [ row['var3'] for row in
table.where(complex_cond % (inf, sup)) ]
and run them at full C-speed (or perhaps more, due to the cache-tuned
computing kernel of Numexpr, which has been integrated into PyTables).
- Now, it is possible to get fields of the ``Row`` iterator by
specifying their position, or even ranges of positions (extended
slicing is supported). For example, you can do::
result = [ row for row in table # fetch field #4
if row < 20 ]
result = [ row[:] for row in table # fetch all fields
if row['var2'] < 20 ]
result = [ row[1::2] for row in # fetch odd fields
table.iterrows(2, 3000, 3) ]
in addition to the classical::
result = [row['var3'] for row in table.where('var2 < 20')]
- ``Row`` has received a new method called ``fetch_all_fields()`` in
order to easily retrieve all the fields of a row in situations like::
[row.fetch_all_fields() for row in table.where('column1 < 0.3')]
The difference between ``row[:]`` and ``row.fetch_all_fields()`` is
that the former will return all the fields as a tuple, while the
latter will return the fields in a NumPy void type and should be
faster. Choose whatever fits better to your needs.
- Now, all data that is read from disk is converted, if necessary, to
the native byteorder of the hosting machine (before, this only
happened with ``Table`` objects). This should help to accelerate
applications that have to do computations with data generated in
platforms with a byteorder different than the user machine.
- The modification of values in ``*Array`` objects (through __setitem__)
now doesn't make a copy of the value in the case that the shape of the
value passed is the same as the slice to be overwritten. This results
in considerable memory savings when you are modifying disk objects
with big array values.
- All leaf constructors (except for ``Array``) have received a new
``chunkshape`` argument that lets the user explicitly select the
chunksizes for the underlying HDF5 datasets (only for advanced users).
- All leaf constructors have received a new parameter called
``byteorder`` that lets the user specify the byteorder of their data
*on disk*. This effectively allows to create datasets in other
byteorders than the native platform.
- Native HDF5 datasets with ``H5T_ARRAY`` datatypes are fully supported
for reading now.
- The test suites for the different packages are installed now, so you
don't need a copy of the PyTables sources to run the tests. Besides,
you can run the test suite from the Python console by using::
Go to the PyTables web site for more details:
About the HDF5 library:
To know more about the company behind the development of PyTables, see:
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. Many
thanks also to SourceForge who have helped to make and distribute this
package! And last, but not least thanks a lot 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
-- The PyTables Team
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