Conditional indexing with Numpy ndarray

For a numpy based solution, you can use numpy.where and then get the row indexes from it and then use it for indexing you matrix. Example –

matrix[np.where((1 <= matrix[:,0]) & (matrix[:,0] <= 6)
       & (2 <= matrix[:,1]) & (matrix[:,1] <= 7))]

Demo –

In [169]: matrix
Out[169]:
array([[  1.,   2.,   3.,   4.,   5.],
       [  6.,   7.,   8.,   9.,  10.],
       [ 11.,  12.,  13.,  14.,  15.],
       [ 16.,  17.,  18.,  19.,  20.]])

In [170]: matrix[np.where((1 <= matrix[:,0]) & (matrix[:,0] <= 6)
   .....:        & (2 <= matrix[:,1]) & (matrix[:,1] <= 7))]
Out[170]:
array([[  1.,   2.,   3.,   4.,   5.],
       [  6.,   7.,   8.,   9.,  10.]])

---

Another method , as indicated in the comments would be to use boolean masks, Example –

mask = ((1 <= matrix[:,0]) & (matrix[:,0] <= 6)
           & (2 <= matrix[:,1]) & (matrix[:,1] <= 7))

matrix[mask,:]

Demo –

In [41]: matrix
Out[41]:
array([[  1.,   2.,   3.,   4.,   5.],
       [  6.,   7.,   8.,   9.,  10.],
       [ 11.,  12.,  13.,  14.,  15.],
       [ 16.,  17.,  18.,  19.,  20.]])

In [42]: mask = ((1 <= matrix[:,0]) & (matrix[:,0] <= 6)
   ....:            & (2 <= matrix[:,1]) & (matrix[:,1] <= 7))

In [43]:

In [43]: matrix[mask,:]
Out[43]:
array([[  1.,   2.,   3.,   4.,   5.],
       [  6.,   7.,   8.,   9.,  10.]])