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Table.filter(ids_to_keep, axis='sample', invert=False, inplace=True)

Filter a table based on a function or iterable.


ids_to_keep : iterable, or function(values, id, metadata) -> bool

If a function, it will be called with the values of the sample/observation, its id (a string) and the dictionary of metadata of each sample/observation, and must return a boolean. If it’s an iterable, it must be a list of ids to keep.

axis : {‘sample’, ‘observation’}, optional

It controls whether to filter samples or observations and defaults to “sample”.

invert : bool, optional

Defaults to False. If set to True, discard samples or observations where ids_to_keep returns True

inplace : bool, optional

Defaults to True. Whether to return a new table or modify itself.



Returns itself if inplace, else returns a new filtered table.



If provided an unrecognized axis.


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

Create a 2x3 BIOM table, with observation metadata and sample metadata:

>>> data = np.asarray([[0, 0, 1], [1, 3, 42]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],
...               [{'full_genome_available': True},
...                {'full_genome_available': False}],
...               [{'sample_type': 'a'}, {'sample_type': 'a'},
...                {'sample_type': 'b'}])

Define a function to keep only samples with sample_type == ‘a’. This will drop sample S3, which has sample_type ‘b’:

>>> filter_fn = lambda val, id_, md: md['sample_type'] == 'a'

Get a filtered version of the table, leaving the original table untouched:

>>> new_table = table.filter(filter_fn, inplace=False)
>>> print table.ids()
['S1' 'S2' 'S3']
>>> print new_table.ids()
['S1' 'S2']

Using the same filtering function, discard all samples with sample_type ‘a’. This will keep only sample S3, which has sample_type ‘b’:

>>> new_table = table.filter(filter_fn, inplace=False, invert=True)
>>> print table.ids()
['S1' 'S2' 'S3']
>>> print new_table.ids()

Filter the table in-place using the same function (drop all samples where sample_type is not ‘a’):

>>> table.filter(filter_fn)
2 x 2 <class 'biom.table.Table'> with 2 nonzero entries (50% dense)
>>> print table.ids()
['S1' 'S2']

Filter out all observations in the table that do not have full_genome_available == True. This will filter out observation O2:

>>> filter_fn = lambda val, id_, md: md['full_genome_available']
>>> table.filter(filter_fn, axis='observation')
1 x 2 <class 'biom.table.Table'> with 0 nonzero entries (0% dense)
>>> print table.ids(axis='observation')

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