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.
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])	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, compressed_axes])	Return a 2-D array in the specified format with ones on the diagonal and zeros elsewhere.
full(shape, fill_value[, dtype, format, …])	Return a SparseArray of given shape and type, filled with fill_value.
full_like(a, fill_value[, dtype, 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.
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, compressed_axes])	Return a SparseArray of given shape and type, filled with ones.
ones_like(a[, dtype, format, compressed_axes])	Return a SparseArray of ones with the same shape and type as a.
outer(a, b[, out])	Return outer product of two sparse arrays.
random(shape[, density, 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])	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, compressed_axes])	Return a SparseArray of given shape and type, filled with zeros.
zeros_like(a[, dtype, format, compressed_axes])	Return a SparseArray of zeros with the same shape and type as a.