biom.table.Table.merge¶

Table.merge(other, sample='union', observation='union', sample_metadata_f=<function prefer_self at 0x7fd9bb5e1b80>, observation_metadata_f=<function prefer_self at 0x7fd9bb5e1b80>)¶

Merge two tables together

The axes, samples and observations, can be controlled independently. Both can work on either “union” or “intersection”.

sample_metadata_f and observation_metadata_f define how to merge metadata between tables. The default is to just keep the metadata associated to self if self has metadata otherwise take metadata from other. These functions are given both metadata dicts and must return a single metadata dict

Parameters:

other : biom.Table or Iterable of Table The other table to merge with this one. If an iterable, the tables are expected to not have metadata. sample : {‘union’, ‘intersection’}, optional observation : {‘union’, ‘intersection’}, optional sample_metadata_f : function, optional Defaults to biom.util.prefer_self. Defines how to handle sample metadata during merge. obesrvation_metadata_f : function, optional Defaults to biom.util.prefer_self. Defines how to handle observation metdata during merge.

Returns:

biom.Table The merged table

Notes

• If sample_metadata_f and observation_metadata_f are None,

  then a fast merge is applied.

• There is an implicit type conversion to float.
• The return type is always that of self

Examples

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

Create a 2x2 table and a 3x2 table:

>>> d_a = np.asarray([[2, 0], [6, 1]])
>>> t_a = Table(d_a, ['O1', 'O2'], ['S1', 'S2'])
>>> d_b = np.asarray([[4, 5], [0, 3], [10, 10]])
>>> t_b = Table(d_b, ['O1', 'O2', 'O3'], ['S1', 'S2'])

Merging the table results in the overlapping samples/observations (see O1 and S2) to be summed and the non-overlapping ones to be added to the resulting table (see S3).

>>> merged_table = t_a.merge(t_b)
>>> print(merged_table)  
# Constructed from biom file
#OTU ID S1      S2
O1      6.0     5.0
O2      6.0     4.0
O3      10.0    10.0