Function reshape

numpy/_core/fromnumeric.py:208–299

Returns a reshaped ndarray without changing data. Parameters ---------- a : array_like Array to be reshaped. shape : 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 th

(a, /, shape, order='C', *, copy=None)

@array_function_dispatch(_reshape_dispatcher)
def reshape(a, /, shape, order='C', *, copy=None):
    """
    Returns a reshaped ndarray without changing data.

    Parameters
    ----------
    a : array_like
        Array to be reshaped.
    shape : 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.
    copy : bool, optional
        If ``True``, then the array data is copied. If ``None``, a copy will
        only be made if it's required by ``order``. For ``False`` it raises
        a ``ValueError`` if a copy cannot be avoided. Default: ``None``.

    Returns
    -------
    reshaped_array : ndarray
        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.

    See Also
    --------
    ndarray.reshape : Equivalent method.

    Notes
    -----
    It is not always possible to change the shape of an array without copying
    the data.

    The ``order`` keyword gives the index ordering both for *fetching*
    the values from ``a``, and then *placing* the values into the output
    array. For example, let's say you have an array:

    >>> a = np.arange(6).reshape((3, 2))
    >>> a
    array([[0, 1],
           [2, 3],
           [4, 5]])

    You can think of reshaping as first raveling the array (using the given