Question: I'm trying to convert the following MATLAB code to Python and am having trouble finding a solution that works in any reasonable amount of time.

```
M = diag(sum(a)) - a;
where = vertcat(in, out);
M(where,:) = 0;
M(where,where) = 1;

```

Here, a is a sparse matrix and where is a vector (as are in/out). The solution I have using Python is:

```
M = scipy.sparse.diags([degs], [0]) - A
where = numpy.hstack((inVs, outVs)).astype(int)
M = scipy.sparse.lil_matrix(M)
M[where, :] = 0  # This is the slowest line
M[where, where] = 1
M = scipy.sparse.csc_matrix(M)

```

But since A is 334863x334863, this takes like three minutes. If anyone has any suggestions on how to make this faster, please contribute them! For comparison, MATLAB does this same step imperceptibly fast.

Thanks!

Answer: 
The solution I use for similar task [attributes to @seberg](https://stackoverflow.com/a/12130287/1265154) and do not convert to `lil` format:

```
import scipy.sparse
import numpy
import time

def csr_row_set_nz_to_val(csr, row, value=0):
    """Set all nonzero elements (elements currently in the sparsity pattern)
    to the given value. Useful to set to 0 mostly.
    """
    if not isinstance(csr, scipy.sparse.csr_matrix):
        raise ValueError('Matrix given must be of CSR format.')
    csr.data[csr.indptr[row]:csr.indptr[row+1]] = value

def csr_rows_set_nz_to_val(csr, rows, value=0):
    for row in rows:
        csr_row_set_nz_to_val(csr, row)
    if value == 0:
        csr.eliminate_zeros()

```

wrap your evaluations with timing

```
def evaluate(size):
    degs = [1]*size
    inVs = list(xrange(1, size, size/25))
    outVs = list(xrange(5, size, size/25))
    where = numpy.hstack((inVs, outVs)).astype(int)
    start_time = time.time()
    A = scipy.sparse.csc_matrix((size, size))
    M = scipy.sparse.diags([degs], [0]) - A
    csr_rows_set_nz_to_val(M, where)
    return time.time()-start_time

```

and test its performance:

```
>>> print 'elapsed %.5f seconds' % evaluate(334863)
elapsed 0.53054 seconds

```
A slightly different take on alko/seberg's approach. I find for loops disturbing, so I spent the better part of this morning figuring a way to get rid of it. The following is not always faster than the other approach. It performs better the more rows there are to be zeroed and the sparser the matrix:

```
def csr_zero_rows(csr, rows_to_zero):
    rows, cols = csr.shape
    mask = np.ones((rows,), dtype=np.bool)
    mask[rows_to_zero] = False
    nnz_per_row = np.diff(csr.indptr)

    mask = np.repeat(mask, nnz_per_row)
    nnz_per_row[rows_to_zero] = 0
    csr.data = csr.data[mask]
    csr.indices = csr.indices[mask]
    csr.indptr[1:] = np.cumsum(nnz_per_row)

```

And to test drive both approaches:

```
rows, cols = 334863, 334863
a = sps.rand(rows, cols, density=0.00001, format='csr')
b = a.copy()
rows_to_zero = np.random.choice(np.arange(rows), size=10000, replace=False)

In [117]: a
Out[117]: 
<334863x334863 sparse matrix of type '<type 'numpy.float64'>'
    with 1121332 stored elements in Compressed Sparse Row format>

In [118]: %timeit -n1 -r1 csr_rows_set_nz_to_val(a, rows_to_zero)
1 loops, best of 1: 75.8 ms per loop

In [119]: %timeit -n1 -r1 csr_zero_rows(b, rows_to_zero)
1 loops, best of 1: 32.5 ms per loop

```

And of course:

```
np.allclose(a.data, b.data)
Out[122]: True

np.allclose(a.indices, b.indices)
Out[123]: True

np.allclose(a.indptr, b.indptr)
Out[124]: True

```