skbio.table.Table.iter#

Table.iter(dense=True, axis='sample')[source]#

Yields (value, id, metadata)

Parameters:

densebool, optional: Defaults to True. If False, yield compressed sparse row or compressed sparse columns if axis is ‘observation’ or ‘sample’, respectively.
axis{‘sample’, ‘observation’}, optional: The axis to iterate over.

Returns:

Examples

>>> import numpy as np
>>> from biom.table import Table

Create a 2x3 BIOM table:

>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'Z3'])

Iter over samples and keep those that start with an Z:

>>> [(values, id, metadata)
...     for values, id, metadata in table.iter() if id[0]=='Z']
[(array([  1.,  42.]), 'Z3', None)]

Iter over observations and add the 2nd column of the values

>>> col = [values[1] for values, id, metadata in table.iter()]
>>> sum(col)
46.0