Extend B to 2D and then divide -

A/B[:,None].astype(float)

Sample run -

In [9]: A
Out[9]: 
array([[ 2,  4],
       [ 6,  8],
       [10, 12]])

In [10]: B
Out[10]: array([1, 2, 4])

In [11]: A/B[:,None].astype(float)
Out[11]: 
array([[ 2. ,  4. ],
       [ 3. ,  4. ],
       [ 2.5,  3. ]])

Or use from __future__ import division that takes care of division to result in a floating pt array -

In [14]: from __future__ import division

In [15]: A/B[:,None]
Out[15]: 
array([[ 2. ,  4. ],
       [ 3. ,  4. ],
       [ 2.5,  3. ]])

Performance boost with multiplication by reciprocal -

In [32]: A = np.random.rand(300,200)

In [33]: B = np.random.rand(300)

In [34]: from __future__ import division

In [35]: %timeit A/B[:,None]
1000 loops, best of 3: 336 µs per loop

In [36]: %timeit A*(1.0/B[:,None])
10000 loops, best of 3: 101 µs per loop

More info on this could be found here. Also, one needs to be careful using this method though, if the values of B are extremely close to 0.

Do you really need to be able to handle both 1D and 2D inputs with the same function? If you know the input is going to be 1D, use

X[:, i] = x

If you know the input is going to be 2D, use

X[:, start:end] = x

If you don't know the input dimensions, I recommend switching between one line or the other with an if, though there might be some indexing trick I'm not aware of that would handle both identically.

Your x has shape (N,) rather than shape (N, 1) (or (1, N)) because numpy isn't built for just matrix math. ndarrays are n-dimensional; they support efficient, consistent vectorized operations for any non-negative number of dimensions (including 0). While this may occasionally make matrix operations a bit less concise (especially in the case of dot for matrix multiplication), it produces more generally applicable code for when your data is naturally 1-dimensional or 3-, 4-, or n-dimensional.