Can we assume they are the same shape?
In [202]: a=sparse.csr_matrix([[0,1],[1,0]])
In [203]: b=sparse.csr_matrix([[0,1],[1,1]])
In [204]: (a!=b).nnz==0
Out[204]: False
This checks the sparsity of the inequality array.
It will give you an efficiency warning if you try a==b (at least the 1st time you use it). That’s because it has to test all those zeros. It can’t take much advantage of the sparsity.
You need a relatively recent version to use logical operators like this. Were you trying to use x.__eq__(y) in some if expression, or did you get error from just that expression?
In general you probably want to check several parameters first. Same shape, same nnz, same dtype. You need to be careful with floats.
For dense arrays np.allclose is a good way of testing equality. And if the sparse arrays aren’t too large, that might be good as well
np.allclose(a.A, b.A)
allclose uses all(less_equal(abs(x-y), atol + rtol * abs(y))). You can use a-b, but I suspect that this too will give an efficiecy warning.