<pre><code>In [339]: arr = np.array([[1, 1, 1, 1, 1, 1, 0, 0, 0],
...: [1, 0, 0, 0, 1, 0, 1, 0, 0],
...: [0, 0, 0, 0, 1, 1, 0, 1, 0],
...: [0, 1, 0, 0, 0, 0, 0, 1, 0],
...: [0, 0, 0, 0, 0, 0, 0, 0, 4],
...: [0, 0, 0, 0, 0, 0, 0, 0, 5],
...: [0, 0, 0, 0, 0, 0, 0, 0, 1]])
In [340]: sparse
Out[340]: <module 'scipy.sparse' from '/usr/local/lib/python3.6/dist-packages/scipy/sparse/__init__.py'>
In [341]: M =sparse.csr_matrix(arr)
In [342]: M
Out[342]:
<7x9 sparse matrix of type '<class 'numpy.int64'>'
with 17 stored elements in Compressed Sparse Row format>
In [343]: M.sum(axis=1)
Out[343]:
matrix([[6],
[3],
[3],
[2],
[4],
[5],
[1]])
In [344]: M.getnnz(axis=1)
Out[344]: array([6, 3, 3, 2, 1, 1, 1], dtype=int32)
In [345]: M.sum(axis=1).A1/M.getnnz(axis=1)
Out[345]: array([1., 1., 1., 1., 4., 5., 1.])
In [346]: M.mean(axis=1)
Out[346]:
matrix([[0.66666667],
[0.33333333],
[0.33333333],
[0.22222222],
[0.44444444],
[0.55555556],
[0.11111111]])
</code></pre>