Assign 1d numpy ndarray into columns of a 2d array

fearless_fool :

Assume dst is an ndarray with shape (5, N), and ramp is an ndarray with shape (5,). (In this case, N = 2):

>>> dst = np.zeros((5, 2))
>>> dst
array([[0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.],
       [0., 0.]])
>>> ramp = np.linspace(1.0, 2.0, 5)
>>> ramp
array([1.  , 1.25, 1.5 , 1.75, 2.  ])

Now I'd like to copy ramp into the columns of dst, resulting in this:

>>> dst
array([[1., 1.],
       [1.25., 1.25.],
       [1.5., 1.5.],
       [1.75, 1.75],
       [2.0, 2.0]])

I didn't expect this to work, and it doesn't:

>>> dst[:] = ramp
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: could not broadcast input array from shape (5) into shape (5,2)

This works, but I'm certain there's a more "numpyesque" way to accomplish this:

>>> dst[:] = ramp.repeat(dst.shape[1]).reshape(dst.shape)
>>> dst
array([[1.  , 1.  ],
       [1.25, 1.25],
       [1.5 , 1.5 ],
       [1.75, 1.75],
       [2.  , 2.  ]]) 

Any ideas?

note

Unlike "Cloning" row or column vectors, I want to assign ramp into dst (or even a subset of dst). In addition, the solution given there uses a python array as the source, not an ndarray, and thus requires calls to .transpose, etc.

Andy L. :

Method 1: Use broadcasting:

As OP mentioned in the comment. Broadcasting works on assigment too

dst[:] = ramp[:,None]

---

Method 2: Use column_stack

N = dst.shape[1]
dst[:] = np.column_stack([ramp.tolist()]*N)

Out[479]:
array([[1.  , 1.  ],
       [1.25, 1.25],
       [1.5 , 1.5 ],
       [1.75, 1.75],
       [2.  , 2.  ]])

---

Method 3: use np.tile

N = dst.shape[1]
dst[:] = np.tile(ramp[:,None], (1,N))