skbio.table.Table.iter_data#

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

Yields axis values

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: Axis to iterate over.

Returns:

generator: Yields list of values for each value in axis

Raises:

UnknownAxisError: If axis other than ‘sample’ or ‘observation’ passed

Examples

>>> import numpy as np
>>> from biom.table import Table
>>> data = np.arange(30).reshape(3,10) # 3 X 10 OTU X Sample table
>>> obs_ids = ['o1', 'o2', 'o3']
>>> sam_ids = ['s%i' %i for i in range(1,11)]
>>> bt = Table(data, observation_ids=obs_ids, sample_ids=sam_ids)

Lets find the sample with the largest sum

>>> sample_gen = bt.iter_data(axis='sample')
>>> max_sample_count = max([sample.sum() for sample in sample_gen])
>>> print(max_sample_count)
57.0