human
What is a the Python program you requested given the following:
Complete the below
def _concat_compat(to_concat, axis=0):

    ''''provide concatenation of an array of arrays each of which is a single
    \'normalized\' dtypes (in that for example, if it\'s object, then it is a
    non-datetimelike and provide a combined dtype for the resulting array that
    preserves the overall dtype if possible)
    Parameters
    to_concat : array of arrays
    axis : axis to provide concatenation
    Returns
    a single array, preserving the combined dtypes'
    '''
assistant
#Here is the Python program you requested.
    def is_nonempty(x):
        try:
            return (x.shape[axis] > 0)
        except Exception:
            return True
    nonempty = [x for x in to_concat if is_nonempty(x)]
    typs = get_dtype_kinds(to_concat)
    _contains_datetime = any((typ.startswith('datetime') for typ in typs))
    _contains_period = any((typ.startswith('period') for typ in typs))
    if ('category' in typs):
        return _concat_categorical(to_concat, axis=axis)
    elif (_contains_datetime or ('timedelta' in typs) or _contains_period):
        return _concat_datetime(to_concat, axis=axis, typs=typs)
    elif ('sparse' in typs):
        return _concat_sparse(to_concat, axis=axis, typs=typs)
    if (not nonempty):
        typs = get_dtype_kinds(to_concat)
        if (len(typs) != 1):
            if ((not len((typs - set(['i', 'u', 'f'])))) or (not len((typs - set(['bool', 'i', 'u']))))):
                pass
            else:
                to_concat = [x.astype('object') for x in to_concat]
    return np.concatenate(to_concat, axis=axis)