# Constructing COO arrays¶

## From coordinates and data¶

This is the preferred way of constructing COO arrays. The constructor for COO (see COO.__init__) can create these objects from two main variables: coords and data.

coords contains the indices where the data is nonzero, and data contains the data corresponding to those indices. For example, the following code will generate a $$5 \times 5$$ identity matrix:

coords = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
data = [1, 1, 1, 1, 1]
s = COO(coords, data)


In general coords should be a (ndim, nnz) shaped array. Each row of coords contains one dimension of the desired sparse array, and each column contains the index corresponding to that nonzero element. data contains the nonzero elements of the array corresponding to the indices in coords. Its shape should be (nnz,)

You can, and should, pass in numpy.ndarray objects for coords and data.

In this case, the shape of the resulting array was determined from the maximum index in each dimension. If the array extends beyond the maximum index in coords, you should supply a shape explicitly. For example, if we did the following without the shape keyword argument, it would result in a $$4 \times 5$$ matrix, but maybe we wanted one that was actually $$5 \times 5$$.

coords = [[0, 3, 2, 1], [4, 1, 2, 0]]
data = [1, 4, 2, 1]
s = COO(coords, data, shape=(5, 5))


## From scipy.sparse.spmatrix objects¶

To construct COO array from scipy.sparse.spmatrix objects, you can use the COO.from_scipy_sparse method. As an example, if x is a scipy.sparse.spmatrix, you can do the following to get an equivalent COO array:

s = COO.from_scipy_sparse(x)


## From numpy.ndarray objects¶

To construct COO arrays from numpy.ndarray objects, you can use the COO.from_numpy method. As an example, if x is a numpy.ndarray, you can do the following to get an equivalent COO array:

s = COO.from_numpy(x)


## Generating random COO objects¶

The sparse.random method can be used to create random COO arrays. For example, the following will generate a $$10 \times 10$$ matrix with $$10$$ nonzero entries, each in the interval $$[0, 1)$$.

s = sparse.random((10, 10), density=0.1)