Storing Metadata with Attributes - Python and HDF5 - page 76

When retrieving elements, we get back the actual value, not an intermediate object like
a Dataset :
>>> dset . attrs [ 'title' ]
'Dataset from third round of experiments'
>>> dset . attrs [ 'sample_rate' ]
100000000.0
>>> dset . attrs [ 'run_id' ]
144
Like groups (and Python dictionaries), iterating over the .attrs object provides the
attribute names:
>>> [ x for x in dset . attrs ]
[u'title', u'sample_rate', u'run_id']
You'll notice that like object names, the names of attributes are always returned as “text”
strings; this means unicode on Python 2, which explains the u prefix.
Attributes don't have the same strict rules as groups for item deletion. You can freely
overwrite attributes just by reusing the name:
>>> dset . attrs [ 'another_id' ] = 42
>>> dset . attrs [ 'another_id' ] = 100
Trying to access missing attributes raises KeyError , although as with Group you don't
get the name of the missing attribute:
>>> del dset . attrs [ 'another_id' ]
>>> dset . attrs [ 'another_id' ]
KeyError: "can't open attribute (Attribute: Can't open object)"
There are also the usual methods like iterkeys , iteritems , values , and so on. They
all do what you expect:
>>> [( name , val ) for name , val in dset . attrs . iteritems ()]
[(u'title', 'Dataset from third round of experiments'),
(u'sample_rate', 100000000.0),
(u'run_id', 144)]
There generally aren't that many attributes attached to an object, so worrying about
items versus iteritems , etc., is less important from a performance perspective.
There is also a get method that (unlike the Group version) is a dictionary-style get :
>>> dset . attrs . get ( 'run_id' )
144
>>> print dset . attrs . get ( 'missing' )
None