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# skbio.diversity.beta_diversity

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

  Compute distances between all pairs of samples

  State: Experimental as of 0.4.0.

  Parameters:

    - metric (str, 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.

    - counts (2D array_like of ints or floats or 2D pandas DataFrame) – Matrix containing count/abundance data where each row contains counts of OTUs in a given sample.

    - ids (iterable 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`).

    - validate (bool, 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_func (callable, 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.

    - kwargs (kwargs, optional) – Metric-specific parameters.

  Returns:

    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`.

  Return type:

    skbio.DistanceMatrix

  Raises:

    - ValueError, MissingNodeError, DuplicateNodeError – If validation fails. Exact error will depend on what was invalid.

    - TypeError – If invalid method-specific parameters are provided.

  See also

  `skbio.diversity`, `skbio.diversity.beta`, `skbio.diversity.get_beta_diversity_metrics`, `skbio.diversity.alpha_diversity`, `scipy.spatial.distance.pdist`, `sklearn.metrics.pairwise_distances`