# scipy.spatial.distance¶

## 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, p, w, V, VI]) Computes the pairwise distances between m original observations in n-dimensional space. cdist(XA, XB[, metric, p, V, VI, w]) Computes distance between each pair of the two collections of inputs. squareform(X[, force, checks]) Converts a vector-form distance vector to a square-form distance matrix, and vice-versa.

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]) Returns True if the variable D passed is a valid distance matrix. is_valid_y(y[, warning, throw, name]) Returns True if the variable y passed is a valid condensed num_obs_dm(d) Returns the number of original observations that correspond to a num_obs_y(Y) Returns the number of original observations that correspond to a

Distance functions between two 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) Computes the Bray-Curtis distance between two n-vectors u and canberra(u, v) Computes the Canberra distance between two n-vectors u and v, chebyshev(u, v) Computes the Chebyshev distance between two n-vectors u and v, cityblock(u, v) Computes the Manhattan distance between two n-vectors u and v, correlation(u, v) Computes the correlation distance between two n-vectors u and v, which is defined as .. cosine(u, v) Computes the Cosine distance between two n-vectors u and v, which dice(u, v) Computes the Dice dissimilarity between two boolean n-vectors euclidean(u, v) Computes the Euclidean distance between two n-vectors u and v, hamming(u, v) Computes the Hamming distance between two n-vectors u and jaccard(u, v) Computes the Jaccard-Needham dissimilarity between two boolean kulsinski(u, v) Computes the Kulsinski dissimilarity between two boolean n-vectors mahalanobis(u, v, VI) Computes the Mahalanobis distance between two n-vectors u and v, matching(u, v) Computes the Matching dissimilarity between two boolean n-vectors minkowski(u, v, p) Computes the Minkowski distance between two vectors u and v, rogerstanimoto(u, v) Computes the Rogers-Tanimoto dissimilarity between two boolean russellrao(u, v) Computes the Russell-Rao dissimilarity between two boolean n-vectors seuclidean(u, v, V) Returns the standardized Euclidean distance between two n-vectors sokalmichener(u, v) Computes the Sokal-Michener dissimilarity between two boolean vectors sokalsneath(u, v) Computes the Sokal-Sneath dissimilarity between two boolean vectors sqeuclidean(u, v) Computes the squared Euclidean distance between two n-vectors u and v, yule(u, v) Computes the Yule dissimilarity between two boolean n-vectors u and v,

Functions

 braycurtis(u, v) Computes the Bray-Curtis distance between two n-vectors u and canberra(u, v) Computes the Canberra distance between two n-vectors u and v, cdist(XA, XB[, metric, p, V, VI, w]) Computes distance between each pair of the two collections of inputs. chebyshev(u, v) Computes the Chebyshev distance between two n-vectors u and v, cityblock(u, v) Computes the Manhattan distance between two n-vectors u and v, correlation(u, v) Computes the correlation distance between two n-vectors u and v, which is defined as .. cosine(u, v) Computes the Cosine distance between two n-vectors u and v, which dice(u, v) Computes the Dice dissimilarity between two boolean n-vectors euclidean(u, v) Computes the Euclidean distance between two n-vectors u and v, hamming(u, v) Computes the Hamming distance between two n-vectors u and is_valid_dm(D[, tol, throw, name, warning]) Returns True if the variable D passed is a valid distance matrix. is_valid_y(y[, warning, throw, name]) Returns True if the variable y passed is a valid condensed jaccard(u, v) Computes the Jaccard-Needham dissimilarity between two boolean kulsinski(u, v) Computes the Kulsinski dissimilarity between two boolean n-vectors mahalanobis(u, v, VI) Computes the Mahalanobis distance between two n-vectors u and v, matching(u, v) Computes the Matching dissimilarity between two boolean n-vectors minkowski(u, v, p) Computes the Minkowski distance between two vectors u and v, norm(x[, ord]) Matrix or vector norm. num_obs_dm(d) Returns the number of original observations that correspond to a num_obs_y(Y) Returns the number of original observations that correspond to a pdist(X[, metric, p, w, V, VI]) Computes the pairwise distances between m original observations in n-dimensional space. rogerstanimoto(u, v) Computes the Rogers-Tanimoto dissimilarity between two boolean russellrao(u, v) Computes the Russell-Rao dissimilarity between two boolean n-vectors seuclidean(u, v, V) Returns the standardized Euclidean distance between two n-vectors sokalmichener(u, v) Computes the Sokal-Michener dissimilarity between two boolean vectors sokalsneath(u, v) Computes the Sokal-Sneath dissimilarity between two boolean vectors sqeuclidean(u, v) Computes the squared Euclidean distance between two n-vectors u and v, squareform(X[, force, checks]) Converts a vector-form distance vector to a square-form distance matrix, and vice-versa. wminkowski(u, v, p, w) Computes the weighted Minkowski distance between two vectors u yule(u, v) Computes the Yule dissimilarity between two boolean n-vectors u and v,

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