How to compute scipy sparse matrix determinant without turning it to dense?
The "standard" way to solve this problem is with a cholesky decomposition, but if you're not up to using any new compiled code, then you're out of luck. The best sparse cholesky implementation is Tim Davis's CHOLMOD, which is licensed under the LGPL and thus not available in scipy proper (scipy is BSD).
I am trying to figure out the fastest method to find the determinant of sparse symmetric and real matrices in python. using scipy sparse module but really surprised that there is no determinant function. I am aware I could use LU factorization to compute determinant but don't see a easy way to do it because the return of scipy.sparse.linalg.splu is an object and instantiating a dense L and U matrix is not worth it - I may as well do sp.linalg.det(A.todense()) where A is my scipy sparse matrix. 
I am also a bit surprised why others have not faced the problem of efficient determinant computation within scipy. How would one use splu to compute determinant? 
I looked into pySparse and scikits.sparse.chlmod. The latter is not practical right now for me - needs package installations and also not sure sure how fast the code is before I go into all the trouble. 
Any solutions? Thanks in advance.