在 numpy 数组中添加具有特定值的列的最快方法？

Question

This is most likely a duplicate; apologies as I couldn't find the answer. I have a (3000, 3) array and I want to make a (3000, 4) array with the last column being a specific value. I can do a very simple np.insert(x, 1, val, axis=1) to insert the value before column 1, but I can't add it to the last column with np.insert(). I can do np.repeat(val, np.shape(x)[0]) or using np.hstack() but they require creating an array first of the same length as the original array (which varies) and a bit clunky (maybe this is the only way), and I'm guessing there is a better way if I am only concerned with one value I want to append.

Is there anything better than np.concatenate((x, np.repeat(val, np.shape(x)[0])[:, np.newaxis]), axis=1) ?

Answer 1

You can allocate a larger array, copy old values from x and fill the last column with a scalar with a little help of broadcasting

y=np.empty_like(x, shape=(3000,4))
y[:,:-1]=x
y[:,-1]=your_value

Answer 2

I would suggest using np.c_ and np.ones_like to cut out some verbosity, but the solution is similar to your suggestion

x=np.c_[x,val*np.ones_like(x[:,0])]

It doesn't require allocating memory for a new matrix.