skbio.table.Table.rankdata#

Table.rankdata(axis='sample', inplace=True, method='average')[source]#

Convert values to rank abundances from smallest to largest

Parameters:

axis{‘sample’, ‘observation’}, optional: The axis to use for ranking.
inplacebool, optional: Defaults to True. If True, performs the ranking in place. Otherwise, returns a new table with ranking applied.
methodstr, optional: The method for handling ties in counts. This can be any valid string that can be passed to scipy.stats.rankdata.

Returns:

biom.Table: The rank-abundance-transformed table.

Raises:

ValueError: If unknown method is provided.

See also

scipy.stats.rankdata

Examples

>>> import numpy as np
>>> from biom import Table
>>> data = np.array([[ 99,  12,   8], [  0,  42,   7],
...                  [112,  42,   6], [  5,  75,   5]])
>>> t = Table(data, sample_ids=['s1', 's2', 's3'],
...           observation_ids=['o1', 'o2', 'o3', 'o4'])

Convert observation counts to their ranked abundance, from smallest to largest.

>>> print(t.rankdata())  
# Constructed from biom file
#OTU ID s1      s2      s3
o1      2.0     1.0     4.0
o2      0.0     2.5     3.0
o3      3.0     2.5     2.0
o4      1.0     4.0     1.0