dpnp.reshape

dpnp.reshape(a, /, newshape, order='C', copy=None)[source]

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

For full documentation refer to numpy.reshape.

Parameters:

  • a ({dpnp.ndarray, usm_ndarray}) -- 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) -- 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.

  • copy ({None, bool}, optional) -- Boolean indicating whether or not to copy the input array. If True, the result array will always be a copy of input a. If False, the result array can never be a copy and a ValueError exception will be raised in case the copy is necessary. If None, the result array will reuse existing memory buffer of a if possible and copy otherwise. Default: None.

Returns:

out -- This will be a new view object if possible; otherwise, it will be a copy. Note there is no guarantee of the memory layout (C- or Fortran- contiguous) of the returned array.

Return type:

dpnp.ndarray

Limitations

Parameter order is supported only with values "C" and "F".

See also

dpnp.ndarray.reshape

Equivalent method.

Examples

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

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