Unexpected Result From Numpy Matrix Insert, How Does This Work?

My goal was to insert a column to the right on a numpy matrix. However, I found that the code I was using is putting in two columns rather than just one. # This one results in a 4x

Solution 1:

While new use of np.matrix is discouraged, we get the same result with np.array:

In [41]: np.insert(np.array([[1],[2]]),1, np.array([[10],[20]]), 0)                                          
Out[41]: 
array([[ 1],
       [10],
       [20],
       [ 2]])
In [42]: np.insert(np.array([[1],[2]]),1, np.array([[10],[20]]), 1)                                          
Out[42]: 
array([[ 1, 10, 20],
       [ 2, 10, 20]])

In [44]: np.insert(np.array([[1],[2]]),1, np.array([10,20]), 1)                                              
Out[44]: 
array([[ 1, 10],
       [ 2, 20]])

Insert as [1]:

In [46]: np.insert(np.array([[1],[2]]),[1], np.array([[10],[20]]), 1)                                        
Out[46]: 
array([[ 1, 10],
       [ 2, 20]])
In [47]: np.insert(np.array([[1],[2]]),[1], np.array([10,20]), 1)                                            
Out[47]: 
array([[ 1, 10, 20],
       [ 2, 10, 20]])

np.insert is a complex function written in Python. So we need to look at that code, and see how values are being mapped on the target space.

The docs elaborate on the difference between insert at 1 and [1]. But off hand I don't see an explanation of how the shape of values matters.

Difference between sequence and scalars:

>>> np.insert(a, [1], [[1],[2],[3]], axis=1)
array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])
>>> np.array_equal(np.insert(a, 1, [1, 2, 3], axis=1),
...                np.insert(a, [1], [[1],[2],[3]], axis=1))
True

When adding an array at the end of another, I'd use concatenate (or one of its stack variants) rather than insert. None of these operate in-place.

In [48]: np.concatenate([np.array([[1],[2]]), np.array([[10],[20]])], axis=1)                                
Out[48]: 
array([[ 1, 10],
       [ 2, 20]])