Concatenate sparse matrices in Python using SciPy/Numpy
numpy, python, scipy, sparse-matrix
Solution
You can use the `scipy.sparse.hstack` to concatenate sparse matrices with the same number of rows (horizontal concatenation):
from scipy.sparse import hstack
hstack((X, X2))
Similarly, you can use `scipy.sparse.vstack` to concatenate sparse matrices with the same number of columns (vertical concatenation).
Using `numpy.hstack` or `numpy.vstack` will create an array with two sparse matrix objects.
Problem
What would be the most efficient way to concatenate sparse matrices in Python using SciPy/Numpy? Here I used the following: ``` >>> np.hstack((X, X2)) array([ <49998x70000 sparse matrix of type '<class 'numpy.float64'>' with 1135520 stored elements in Compressed Sparse Row format>, <49998x70000 sparse matrix of type '<class 'numpy.int64'>' with 1135520 stored elements in Compressed Sparse Row format>], dtype=object) ``` I would like to use both predictors in a regression, but the current format is obviously not what I'm looking for. Would it be possible to get the following: ``` <49998x1400000 sparse matrix of type '<class 'numpy.float64'>' with 2271040 stored elements in Compressed Sparse Row format> ``` It is too large to be converted to a deep format.