SciPy sparse matrix not modified when passed into function-CodePudding

I have noticed an apparent inconsistency in how SciPy sparse matrices and numpy arrays are modified when passed into functions. In particular, I was wondering if someone could explain why the a sparse matrix below is not globally modified by func, but the b array is:

from scipy import sparse
import numpy as np

def func(m):
    m  = m

a = sparse.identity(2)
b = np.array([1, 2])

print(a.todense()) # [[1,0],[0,1]]
func(a)
print(a.todense()) # Still [[1,0],[0,1]]. Why???

print(b) # [1, 2]
func(b)
print(b) # Now [2, 4]

CodePudding user response：

In [11]: arr = np.array([[1,0],[2,3]])
In [12]: id(arr)
Out[12]: 1915221691344

In [13]: M = sparse.csr_matrix(arr)
In [14]: id(M)
Out[14]: 1915221319840

In [15]: arr  = arr

In [16]: id(arr)
Out[16]: 1915221691344

= operates in-place for array.

In [17]: M  = M    
In [18]: id(M)
Out[18]: 1915221323200

For the sparse matrix it creates a new sparse matrix object. It doesn't modify the matrix in-place.

For this operation, the data attribute could be modified in place:

In [20]: M.data
Out[20]: array([2, 4, 6], dtype=int32)

In [21]: M.data  = M.data

In [22]: M.A
Out[22]: 
array([[ 4,  0],
       [ 8, 12]], dtype=int32)

But in general, adding something to a sparse matrix can modify its sparsity. The sparse developers, in their wisdom, decided it wasn't possible, or just not cost effective (programming or run time?) to do this without creating a new matrix.

While a sparse matrix is patterned on the np.matrix subclass, it is not a subclass of ndarray, and is not obligated to behave in exactly the same way.

In [30]: type(M).__mro__
Out[30]: 
(scipy.sparse.csr.csr_matrix,
 scipy.sparse.compressed._cs_matrix,
 scipy.sparse.data._data_matrix,
 scipy.sparse.base.spmatrix,
 scipy.sparse.data._minmax_mixin,
 scipy.sparse._index.IndexMixin,
 object)