Function structured_to_unstructured

numpy/lib/recfunctions.py:940–1067

Converts an n-D structured array into an (n+1)-D unstructured array. The new array will have a new last dimension equal in size to the number of field-elements of the input array. If not supplied, the output datatype is determined from the numpy type promotion rules applied to all

(arr, dtype=None, copy=False, casting='unsafe')

@array_function_dispatch(_structured_to_unstructured_dispatcher)
def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
    """
    Converts an n-D structured array into an (n+1)-D unstructured array.

    The new array will have a new last dimension equal in size to the
    number of field-elements of the input array. If not supplied, the output
    datatype is determined from the numpy type promotion rules applied to all
    the field datatypes.

    Nested fields, as well as each element of any subarray fields, all count
    as a single field-elements.

    Parameters
    ----------
    arr : ndarray
       Structured array or dtype to convert. Cannot contain object datatype.
    dtype : dtype, optional
       The dtype of the output unstructured array.
    copy : bool, optional
        If true, always return a copy. If false, a view is returned if
        possible, such as when the `dtype` and strides of the fields are
        suitable and the array subtype is one of `numpy.ndarray`,
        `numpy.recarray` or `numpy.memmap`.

        .. versionchanged:: 1.25.0
            A view can now be returned if the fields are separated by a
            uniform stride.

    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
        See casting argument of `numpy.ndarray.astype`. Controls what kind of
        data casting may occur.

    Returns
    -------
    unstructured : ndarray
       Unstructured array with one more dimension.

    Examples
    --------
    >>> import numpy as np

    >>> from numpy.lib import recfunctions as rfn
    >>> a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
    >>> a
    array([(0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.]),
           (0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.])],
          dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))])
    >>> rfn.structured_to_unstructured(a)
    array([[0., 0., 0., 0., 0.],
           [0., 0., 0., 0., 0.],
           [0., 0., 0., 0., 0.],
           [0., 0., 0., 0., 0.]])

    >>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
    ...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
    >>> np.mean(rfn.structured_to_unstructured(b[['x', 'z']]), axis=-1)
    array([ 3. ,  5.5,  9. , 11. ])