Is there a way to take...

>>> x = np.array([0, 8, 10, 15, 50]).reshape((-1, 1)); ncols = 5

...and turn it into...

array([[ 0,  1,  2,  3,  4],[ 8,  9, 10, 11, 12],[10, 11, 12, 13, 14],[15, 16, 17, 18, 19],[50, 51, 52, 53, 54]])

I was able to do it with np.apply_along_axis...

>>> def myFunc(a, ncols):return np.arange(a, (a+ncols))>>> np.apply_along_axis(myFunc, axis=1, arr=x)

and with for loops...

>>> X = np.zeros((x.size,ncols))
>>> for a,b in izip(xrange(x.size),x):X[a] = myFunc(b, ncols)

but they are too slow. Is there a faster way?

Thanks in advance.

The following will do it:

In [9]: x = np.array([0, 8, 10, 15, 50]).reshape((-1, 1))In [10]: ncols = 5In [11]: x + np.arange(ncols)
Out[11]: 
array([[ 0,  1,  2,  3,  4],[ 8,  9, 10, 11, 12],[10, 11, 12, 13, 14],[15, 16, 17, 18, 19],[50, 51, 52, 53, 54]])

It adds a row vector to a column vector and relies on broadcasting to do the rest.

This should be as fast as anything: producing a 1000x1000 matrix takes ~1.6ms:

In [17]: %timeit np.arange(1000).reshape((-1, 1)) + np.arange(1000)
1000 loops, best of 3: 1.61 ms per loop