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# numpy.reshape

`numpy.``reshape`(a, newshape, order='C')

  Gives a new shape to an array without changing its data.

  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’}, optional Determines whether the array data should be viewed as in C (row-major) order or FORTRAN (column-major) order.
  Returns : | reshaped_array : ndarray This will be a new view object if possible; otherwise, it will be a copy.

  See also

  `ndarray.reshape`
    Equivalent method.

  Notes

  It is not always possible to change the shape of an array without copying the data. If you want an error to be raise if the data is copied, you should assign the new shape to the shape attribute of the array:

      >>> a = np.zeros((10, 2))
      # A transpose make the array non-contiguous
      >>> b = a.T
      # Taking a view makes it possible to modify the shape without modiying the
      # initial object.
      >>> c = b.view()
      >>> c.shape = (20)
      AttributeError: incompatible shape for a non-contiguous array

  Examples

      >>> a = np.array([[1,2,3], [4,5,6]])
      >>> np.reshape(a, 6)
      array([1, 2, 3, 4, 5, 6])
      >>> np.reshape(a, 6, order='F')
      array([1, 4, 2, 5, 3, 6])

      >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
      array([[1, 2],
             [3, 4],
             [5, 6]])

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