Distance computations (scipy.spatial.distance)

Function reference

Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.

pdist(X[, metric])	Pairwise distances between observations in n-dimensional space.
cdist(XA, XB[, metric])	Compute distance between each pair of the two collections of inputs.
squareform(X[, force, checks])	Convert a vector-form distance vector to a square-form distance matrix, and vice-versa.
directed_hausdorff(u, v[, seed])	Compute the directed Hausdorff distance between two N-D arrays.

Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix.

is_valid_dm(D[, tol, throw, name, warning])	Return True if input array is a valid distance matrix.
is_valid_y(y[, warning, throw, name])	Return True if the input array is a valid condensed distance matrix.
num_obs_dm(d)	Return the number of original observations that correspond to a square, redundant distance matrix.
num_obs_y(Y)	Return the number of original observations that correspond to a condensed distance matrix.

Distance functions between two numeric vectors u and v. Computing distances over a large collection of vectors is inefficient for these functions. Use pdist for this purpose.

braycurtis(u, v[, w])	Compute the Bray-Curtis distance between two 1-D arrays.
canberra(u, v[, w])	Compute the Canberra distance between two 1-D arrays.
chebyshev(u, v[, w])	Compute the Chebyshev distance.
cityblock(u, v[, w])	Compute the City Block (Manhattan) distance.
correlation(u, v[, w, centered])	Compute the correlation distance between two 1-D arrays.
cosine(u, v[, w])	Compute the Cosine distance between 1-D arrays.
euclidean(u, v[, w])	Computes the Euclidean distance between two 1-D arrays.
jensenshannon(p, q[, base])	Compute the Jensen-Shannon distance (metric) between two 1-D probability arrays.
mahalanobis(u, v, VI)	Compute the Mahalanobis distance between two 1-D arrays.
minkowski(u, v[, p, w])	Compute the Minkowski distance between two 1-D arrays.
seuclidean(u, v, V)	Return the standardized Euclidean distance between two 1-D arrays.
sqeuclidean(u, v[, w])	Compute the squared Euclidean distance between two 1-D arrays.
wminkowski(u, v, p, w)	Compute the weighted Minkowski distance between two 1-D arrays.

Distance functions between two boolean vectors (representing sets) u and v. As in the case of numerical vectors, pdist is more efficient for computing the distances between all pairs.

dice(u, v[, w])	Compute the Dice dissimilarity between two boolean 1-D arrays.
hamming(u, v[, w])	Compute the Hamming distance between two 1-D arrays.
jaccard(u, v[, w])	Compute the Jaccard-Needham dissimilarity between two boolean 1-D arrays.
kulsinski(u, v[, w])	Compute the Kulsinski dissimilarity between two boolean 1-D arrays.
rogerstanimoto(u, v[, w])	Compute the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays.
russellrao(u, v[, w])	Compute the Russell-Rao dissimilarity between two boolean 1-D arrays.
sokalmichener(u, v[, w])	Compute the Sokal-Michener dissimilarity between two boolean 1-D arrays.
sokalsneath(u, v[, w])	Compute the Sokal-Sneath dissimilarity between two boolean 1-D arrays.
yule(u, v[, w])	Compute the Yule dissimilarity between two boolean 1-D arrays.

hamming also operates over discrete numerical vectors.