Migrating from PyTables 1.x to 2.x¶
Francesc Alted i Abad
Ivan Vilata i Balaguer
Next are described a series of issues that you must have in mind when migrating from PyTables 1.x to PyTables 2.x series.
New type system¶
In PyTables 2.x all the data types for leaves are described through a couple of classes:
Atom: Describes homogeneous types of the atomic components in
Description: Describes (possibly nested) heterogeneous types in
So, in order to upgrade to the new type system, you must perform the next replacements:
*Array.atom.itemsize(the size of the item)
Furthermore, the PyTables types (previously called “string types”) have changed to better adapt to NumPy conventions. The next changes have been applied:
PyTables types are now written in lower case, so ‘Type’ becomes ‘type’. For example, ‘Int64’ becomes now ‘int64’.
‘CharType’ –> ‘string’
‘Complex32’, ‘Complex64’ –> ‘complex64’, ‘complex128’. Note that the numeric part of a ‘complex’ type refers now to the size in bits of the type and not to the precision, as before.
See Appendix I of the Users’ Manual on supported data types for more information on the new PyTables types.
Important changes in
EnumColconstructors has been replaced by the
baseargument, which can take a full-blown atom, although it accepts bare PyTables types as well. This is a mandatory argument now.
vlstringpseudo-atoms used in
VLArraynodes do no longer imply UTF-8 (nor any other) encoding, they only store and load raw strings of bytes. All encoding and decoding is left to the user. Be warned that reading old files may yield raw UTF-8 encoded strings, which may be converted back to Unicode in this way:
unistr = vlarray[index].decode('utf-8')
If you need to work with variable-length Unicode strings, you may want to use the new
vlunicodepseudo-atom, which fully supports Unicode strings with no encoding hassles.
Colare now abstract classes, so you can’t use them to create atoms or column definitions of an arbitrary type. If you know the particular type you need, use the proper subclass; otherwise, use the
Col.from_*()factory methods. See the section on declarative classes in the reference.
You are also advised to avoid using the inheritance of atoms to check for their kind or type; for that purpose, use their
New query system¶
In-kernel conditions, since they are based now in Numexpr, must be written as strings. For example, a condition that in 1.x was stated as:
result = [row['col2'] for row in table.where(table.cols.col1 == 1)]
now should read:
result = [row['col2'] for row in table.where('col1 == 1')]
That means that complex selections are possible now:
result = [ row['col2'] for row in table.where('(col1 == 1) & (col3**4 > 1)') ]
For the same reason, conditions for indexed columns must be written as strings as well.
New indexing system¶
The indexing system has been totally rewritten from scratch for PyTables 2.0 Pro Edition. The new indexing system has been included into PyTables with release 2.3. Due to this, your existing indexes created with PyTables 1.x will be useless, and although you will be able to continue using the actual data in files, you won’t be able to take advantage of any improvement in speed.
You will be offered the possibility to automatically re-create the indexes
in PyTables 1.x format to the new 2.0 format by using the
New meanings for atom shape and
*Array shape argument¶
With PyTables 1.x, the atom shape was used for different goals depending on
the context it was used. For example, in
createEArray(), the shape of the
atom was used to specify the dataset shape of the object on disk, while in
CArray the same atom shape was used to specify the chunk shape of the
dataset on disk. Moreover, for
VLArray objects, the very same atom shape
specified the type shape of the data type. As you see, all of these was
quite a mess.
Starting with PyTables 2.x, an
Atom only specifies properties of the data
type (à la
VLArray in 1.x). This lets the door open for specifying
multidimensional data types (that can be part of another layer of
multidimensional datasets) in a consistent way along all the
objects in PyTables.
As a consequence of this,
methods have received new parameters in order to make possible to specify the
shapes of the datasets as well as chunk sizes (in fact, it is possible now to
specify the latter for all the chunked leaves, see below). Please have this
in mind during the migration process.
Another consequence is that, now that the meaning of the atom shape is clearly defined, it has been chosen as the main object to describe homogeneous data types in PyTables. See the Users’ Manual for more info on this.
chunkshape of chunked leaves¶
It is possible now to specify the chunk shape for all the chunked leaves in
PyTables (all except
Array). With PyTables 1.x this value was
automatically calculated so as to achieve decent results in most of the
situations. However, the user may be interested in specifying its own chunk
shape based on her own needs (although this should be done only by advanced
Of course, if this parameter is not specified, a sensible default is calculated for the size of the leave (which is recommended).
A new attribute called
chunkshape has been added to all leaves. It is
read-only (you can’t change the size of chunks once you have created a leaf),
but it can be useful for inspection by advanced users.
New flavor specification¶
As of 2.x, flavors can only be set through the
flavor attribute of
leaves, and they are persistent, so changing a flavor requires that the file
Flavors can no longer be set through
File.create*() methods, nor the
flavor argument previously found in some
Table methods, nor through
Atom constructors or the
_v_flavor attribute of descriptions.
System attributes can be deleted now¶
The protection against removing system attributes (like
CLASS, to name only a few) has been completely removed. It
is now the responsibility of the user to make a proper use of this freedom.
With this, users can get rid of all proprietary PyTables attributes if they
want to (for example, for making a file to look more like an HDF5 native one).
Now, all the data coming from reads and internal buffers is always converted on-the-fly, if needed, to the native byteorder. This represents a big advantage in terms of speed when operating with objects coming from files that have been created in machines with a byte ordering different from native.
Besides, all leaf constructors have received a new
that allows specifying the byteorder of data on disk. In particular, a
_v_byteorder entry in a Table description is no longer honored and you
should use the aforementioned
Tunable internal buffer sizes¶
You can change the size of the internal buffers for I/O purposes of PyTables
by changing the value of the new public attribute
nrowsinbuf that is
present in all leaves. By default, this contains a sensible value so as to
achieve a good balance between speed and memory consumption. Be careful when
changing it, if you don’t want to get unwanted results (very slow I/O, huge
Changes to module names¶
If your application is directly accessing modules under the
package, you need to know that the names of all modules are now all in
lowercase. This allows one to tell apart the
tables.Array class from
tables.array module (which was also called
This includes subpackages like
On top of that, more-or-less independent modules have also been renamed and some of them grouped into subpackages. The most important are:
tables.netcdf3subpackage replaces the old
tables.nrasubpackage replaces the old
nestedrecords.pywith the implementation of the
tables.misc package includes utility modules which do not depend
Nonefor filter properties created with
complevel=0(i.e. disabled compression, which is the default).
‘non-relevant’ –> ‘irrelevant’ (applied to byteorders)
Table.coldescr, dictionary of the
Table.colshapeshas disappeared. You can get it this way:
colshapes = dict( (name, col.shape) for (name, col) in table.coldescr.iteritems() )
Table.colitemsizeshas disappeared. You can get it this way:
colitemsizes = dict( (name, col.itemsize) for (name, col) in table.coldescr.iteritems() )
—The PyTables Team