[Python Cookbook] Numpy Array Manipulation

1. Reshape: 

The np.reshape() method will give a new shape to an array without changing its data. Note that the new shape should be compatible with the original shape. Here is how it works.

np.reshape(a, newshape, order='C')

 

Parameters

----------

a : array_like

    Array to be reshaped.

newshape : int or tuple of ints

    The new shape should be compatible with the original shape. If

    an integer, then the result will be a 1-D array of that length.

    One shape dimension can be -1. In this case, the value is

    inferred from the length of the array and remaining dimensions.

order : {'C', 'F', 'A'}, optional

    Read the elements of `a` using this index order, and place the

    elements into the reshaped array using this index order.  'C'

    means to read / write the elements using C-like index order,

    with the last axis index changing fastest, back to the first

    axis index changing slowest. 'F' means to read / write the

    elements using Fortran-like index order, with the first index

    changing fastest, and the last index changing slowest. Note that

    the 'C' and 'F' options take no account of the memory layout of

    the underlying array, and only refer to the order of indexing.

    'A' means to read / write the elements in Fortran-like index

    order if `a` is Fortran *contiguous* in memory, C-like order

    otherwise.