How to efficiently remove columns from a sparse matrix that only contain zeros?

Question

What is the best way to efficiently remove columns from a sparse matrix that only contain zeros. I have a matrix which I have created and filled with data: ``` matrix = sp.sparse.lil_matrix((100, 100)) ``` I now wish to remove ~ the last 20 columns which only contain zero data. How can I do this?

Accepted Answer

If this were just a numpy array, `X`, then you could say `X!=0` which would give you a boolean array of the same shape as `X`, and then you could index `X` with the boolean array, i.e. `non_zero_entries = X[X!=0]`

But this is a sparse matrix which does not support boolean indexing and also will not give you what you want if you try `X!=0` -- it just returns a single boolean value that seems to only return true if they are the exact same matrix (in memory).

What you want is the `nonzero` method from numpy.

```
import numpy as np
from scipy import sparse

X = sparse.lil_matrix((100,100)) # some sparse matrix
X[1,17] = 1
X[17,17] = 1
indices = np.nonzero(X) # a tuple of two arrays: 0th is row indices, 1st is cols
X.tocsc()[indices] # this just gives you the array of all non-zero entries
```

If you want only the full columns where there are non-zero entries, then just take the 1st from indices. Except you need to account for the repeated indices (if there are more than one entries in a column):

```
columns_non_unique = indices[1]
unique_columns = sorted(set(columns_non_unique))
X.tocsc()[:,unique_columns]
```

How to efficiently remove columns from a sparse matrix that only contain zeros?

Solution

Problem