Groups, Links, and Iteration: The “H” in HDF5 - Python and HDF5 - page 56

By the way, you don't have to manually create nested groups one at a time. If you supply
a full path, HDF5 will create all the intermediate groups for you:
>>> out = f . create_group ( '/some/big/path' )
>>> out
<HDF5 group "/some/big/path" (0 members)>
The same goes for creating datasets; just supply the full path you want and HDF5 will
fill in the missing pieces.
Group Basics
If you remember nothing else from this chapter, remember this: groups work mostly like
dictionaries . There are a couple of holes in this abstraction, but on the whole it works
surprisingly well. Groups are iterable, and have a subset of the normal Python dictionary
API.
Let's add another few objects to our file for the examples that follow:
>>> f [ "Dataset1" ] = 1.0
>>> f [ "Dataset2" ] = 2.0
>>> f [ "Dataset3" ] = 3.0
>>> subgroup [ "Dataset4" ] = 4.0
Dictionary-Style Access
You got a hint of this dictionary-like behavior from the syntax group[name] = object .
Objects can be retrieved from a group by name:
>>> dset1 = f [ "Dataset1" ]
Unlike normal Python dictionaries, you can also use POSIX-style paths to directly access
objects in subgroups, without having to tediously open all the groups between here and
there:
>>> dset4 = f [ "SubGroup/Dataset4" ] # Right
>>> dset4 = f [ "SubGroup" ][ "Dataset4" ] # Works, but inefficient
Attempting to access an empty group raises KeyError , although one irritating thing
about h5py is that you don't get the name of the missing object in the exception:
>>> f [ 'BadName' ]
KeyError: "unable to open object (Symbol table: Can't open object)"
There's also the familar get method, which is handy if you don't want to raise an ex‐
ception:
>>> out = f . get ( "BadName" )
>>> print out
None