How to drop one or more columns of CSR matrix

Question

I want to drop some columns from a given CSR matrix. I can use slicing to exclude those columns, however, this approach seems inefficient. Is there any better way that I can use for a larger matrix?

Answer 1

If you want to drop one or more columns, you can use boolean masking approach. In this approach, you will create a boolean array of size equal to the number of columns in the CSR matrix with values set to True. Then set the value to False for the columns you want to drop and apply the mask to drop columns. This approach is fast, memory-efficient, and preserves CSR format, ideal for big sparse matrices.

Here is an example:

import numpy as np

from scipy.sparse import csr_matrix

X = np.array([[0, 5, 0, 0, 2], [3, 0, 0, 7, 0], [0, 0, 4, 0, 0], [0, 1, 0, 0, 6], [8, 0, 0, 0, 4]])

# Convert to CSR format

X_csr = csr_matrix(X)

# boolean mask

sel_cols = np.ones(X_csr.shape[1], dtype=bool)

# columns that need to be dropped

cols_to_drop = [1,2]

sel_cols[cols_to_drop] = False

# select remaining columns

X_csr = X_csr[:, sel_cols]

The code will produce a CSR matrix by removing columns 1 and 2.