You have two options:
If you no longer want the original shape, the easiest is just to assign a new shape to the array
a.shape = (a.size//ncols, ncols)
You can switch the a.size//ncols by -1 to compute the proper shape automatically. Make sure that a.shape[0]*a.shape[1]=a.size, else you'll run into some problem.
You can get a new array with the np.reshape function, that works mostly like the version presented above
new = np.reshape(a, (-1, ncols))
When it's possible, new will be just a view of the initial array a, meaning that the data are shared. In some cases, though, new array will be acopy instead. Note that np.reshape also accepts an optional keyword order that lets you switch from row-major C order to column-major Fortran order. np.reshape is the function version of the a.reshape method.
If you can't respect the requirement a.shape[0]*a.shape[1]=a.size, you're stuck with having to create a new array. You can use the np.resize function and mixing it with np.reshape, such as
>>> a =np.arange(9)
>>> np.resize(a, 10).reshape(5,2)