skbio.diversity.beta_diversity(metric, counts, ids=None, validate=True, pairwise_func=None, **kwargs)[source]#

Compute distances between all pairs of samples.

metricstr, callable

The pairwise distance function to apply. See the scipy pdist docs and the scikit-bio functions linked under See Also for available metrics. Passing metrics as a strings is preferable as this often results in an optimized version of the metric being used.

counts2D array_like of ints or floats, 2D pandas DataFrame, Table

Matrix containing count/abundance data where each row contains counts of taxa in a given sample.

idsiterable of strs, optional

Identifiers for each sample in counts. By default, samples will be assigned integer identifiers in the order that they were provided (where the type of the identifiers will be str).

validatebool, optional

If False, validation of the input won’t be performed. This step can be slow, so if validation is run elsewhere it can be disabled here. However, invalid input data can lead to invalid results or error messages that are hard to interpret, so this step should not be bypassed if you’re not certain that your input data are valid. See skbio.diversity for the description of what validation entails so you can determine if you can safely disable validation.

pairwise_funccallable, optional

The function to use for computing pairwise distances. This function must take counts and metric and return a square, hollow, 2-D numpy.ndarray of dissimilarities (floats). Examples of functions that can be provided are scipy.spatial.distance.pdist and sklearn.metrics.pairwise_distances. By default, scipy.spatial.distance.pdist will be used.

kwargskwargs, optional

Metric-specific parameters.


Distances between all pairs of samples (i.e., rows). The number of rows and columns will be equal to the number of rows in counts.

ValueError, MissingNodeError, DuplicateNodeError

If validation fails. Exact error will depend on what was invalid.


If invalid method-specific parameters are provided.