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

Question

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.

Does this answer your question? "Cloning" row or column vectors — AMC
– AMC, Commented Mar 9, 2020 at 23:37
The suggested post (stackoverflow.com/questions/1550130/…) ends up being much more complex than the answer given below. And it doesn't address copying into a vector. I never would have discovered this "nump-ish" technique otherwise. — fearless_fool
– fearless_fool, Commented Mar 10, 2020 at 0:00

fearless_fool · Accepted Answer · 2020-03-10 00:11:01Z

1

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))

edited Mar 10, 2020 at 0:11

fearless_fool

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answered Mar 9, 2020 at 23:15

Andy L.

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2 Comments

fearless_fool Over a year ago

Ah! Thank you for the ramp[:,None] hint. This is even easier (since broadcasting works in this case): dst[:] = ramp[:,None]. (If you agree, feel free to update your answer!)

Andy L. Over a year ago

hah! totally forgot about broadcasting works on assignment too. I added it to the answer :)

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