Convert a 1D array to a 2D array in numpy

Solution 1:

You want to reshape the array.

B = np.reshape(A, (-1, 2))

where -1 infers the size of the new dimension from the size of the input array.

Solution 2:

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)

Solution 3:

Try something like:

B = np.reshape(A,(-1,ncols))

You'll need to make sure that you can divide the number of elements in your array by ncols though. You can also play with the order in which the numbers are pulled into B using the order keyword.