API

Description

Classes

 COO(coords[, data, shape, has_duplicates, ...]) A sparse multidimensional array. DOK(shape[, data, dtype, fill_value]) A class for building sparse multidimensional arrays. GCXS(arg[, shape, compressed_axes, prune, ...]) A sparse multidimensional array. SparseArray(shape[, fill_value]) An abstract base class for all the sparse array classes.

Functions

 argwhere(a) Find the indices of array elements that are non-zero, grouped by element. as_coo(x[, shape, fill_value, idx_dtype]) Converts any given format to COO. concatenate(arrays[, axis, compressed_axes]) Concatenate the input arrays along the given dimension. clip(a[, a_min, a_max, out]) Clip (limit) the values in the array. diagonal(a[, offset, axis1, axis2]) Extract diagonal from a COO array. diagonalize(a[, axis]) Diagonalize a COO array. dot(a, b) Perform the equivalent of numpy.dot on two arrays. elemwise(func, *args, **kwargs) Apply a function to any number of arguments. eye(N[, M, k, dtype, format]) Return a 2-D array in the specified format with ones on the diagonal and zeros elsewhere. full(shape, fill_value[, dtype, format, order]) Return a SparseArray of given shape and type, filled with fill_value. full_like(a, fill_value[, dtype, shape, format]) Return a full array with the same shape and type as a given array. isposinf(x[, out]) Test element-wise for positive infinity, return result as sparse bool array. isneginf(x[, out]) Test element-wise for negative infinity, return result as sparse bool array. kron(a, b) Kronecker product of 2 sparse arrays. load_npz(filename) Load a sparse matrix in numpy's .npz format from disk. matmul(a, b) Perform the equivalent of numpy.matmul on two arrays. moveaxis(a, source, destination) Move axes of an array to new positions. nanmax(x[, axis, keepdims, dtype, out]) Maximize along the given axes, skipping NaN values. nanmean(x[, axis, keepdims, dtype, out]) Performs a NaN skipping mean operation along the given axes. nanmin(x[, axis, keepdims, dtype, out]) Minimize along the given axes, skipping NaN values. nanprod(x[, axis, keepdims, dtype, out]) Performs a product operation along the given axes, skipping NaN values. nanreduce(x, method[, identity, axis, keepdims]) Performs an NaN skipping reduction on this array. nansum(x[, axis, keepdims, dtype, out]) Performs a NaN skipping sum operation along the given axes. ones(shape[, dtype, format]) Return a SparseArray of given shape and type, filled with ones. ones_like(a[, dtype, shape, format]) Return a SparseArray of ones with the same shape and type as a. outer(a, b[, out]) Return outer product of two sparse arrays. pad(array, pad_width[, mode]) Performs the equivalent of numpy.pad for SparseArray. random(shape[, density, nnz, random_state, ...]) Generate a random sparse multidimensional array result_type(*arrays_and_dtypes) Returns the type that results from applying the NumPy type promotion rules to the arguments. roll(a, shift[, axis]) Shifts elements of an array along specified axis. save_npz(filename, matrix[, compressed]) Save a sparse matrix to disk in numpy's .npz format. stack(arrays[, axis, compressed_axes]) Stack the input arrays along the given dimension. tensordot(a, b[, axes, return_type]) Perform the equivalent of numpy.tensordot. tril(x[, k]) Returns an array with all elements above the k-th diagonal set to zero. triu(x[, k]) Returns an array with all elements below the k-th diagonal set to zero. where(condition[, x, y]) Select values from either x or y depending on condition. zeros(shape[, dtype, format]) Return a SparseArray of given shape and type, filled with zeros. zeros_like(a[, dtype, shape, format]) Return a SparseArray of zeros with the same shape and type as a.