Logic for the 1-d interpolation.  The result should be 1-d, inputs
    xvalues and yvalues will each be 1-d arrays of the same length.

    Bounds_error is currently hardcoded to False since non-scipy ones don't
    take it as an argument.

def interpolate_1d(xvalues, yvalues, method='linear', limit=None,
                   limit_direction='forward', limit_area=None, fill_value=None,
                   bounds_error=False, order=None, **kwargs):
    """
    Logic for the 1-d interpolation.  The result should be 1-d, inputs
    xvalues and yvalues will each be 1-d arrays of the same length.

    Bounds_error is currently hardcoded to False since non-scipy ones don't
    take it as an argument.
    """
    # Treat the original, non-scipy methods first.

    invalid = isna(yvalues)
    valid = ~invalid

    if not valid.any():
        # have to call np.asarray(xvalues) since xvalues could be an Index
        # which can't be mutated
        result = np.empty_like(np.asarray(xvalues), dtype=np.float64)
        result.fill(np.nan)
        return result

    if valid.all():
        return yvalues

    if method == 'time':
        if not getattr(xvalues, 'is_all_dates', None):
            # if not issubclass(xvalues.dtype.type, np.datetime64):
            raise ValueError('time-weighted interpolation only works '
                             'on Series or DataFrames with a '
                             'DatetimeIndex')
        method = 'values'

    valid_limit_directions = ['forward', 'backward', 'both']
    limit_direction = limit_direction.lower()
    if limit_direction not in valid_limit_directions:
        msg = ('Invalid limit_direction: expecting one of {valid!r}, '
               'got {invalid!r}.')
        raise ValueError(msg.format(valid=valid_limit_directions,
                                    invalid=limit_direction))

    if limit_area is not None:
        valid_limit_areas = ['inside', 'outside']
        limit_area = limit_area.lower()
        if limit_area not in valid_limit_areas:
            raise ValueError('Invalid limit_area: expecting one of {}, got '
                             '{}.'.format(valid_limit_areas, limit_area))

    # default limit is unlimited GH #16282
    if limit is None:
        # limit = len(xvalues)
        pass
    elif not is_integer(limit):
        raise ValueError('Limit must be an integer')
    elif limit < 1:
        raise ValueError('Limit must be greater than 0')

    from pandas import Series
    ys = Series(yvalues)

    # These are sets of index pointers to invalid values... i.e. {0, 1, etc...
    all_nans = set(np.flatnonzero(invalid))
    start_nans = set(range(ys.first_valid_index()))
    end_nans = set(range(1 + ys.last_valid_index(), len(valid)))
    mid_nans = all_nans - start_nans - end_nans

    # Like the sets above, preserve_nans contains indices of invalid values,
    # but in this case, it is the final set of indices that need to be
    # preserved as NaN after the interpolation.

    # For example if limit_direction='forward' then preserve_nans will
    # contain indices of NaNs at the beginning of the series, and NaNs that
    # are more than'limit' away from the prior non-NaN.

    # set preserve_nans based on direction using _interp_limit
    if limit_direction == 'forward':
        preserve_nans = start_nans | set(_interp_limit(invalid, limit, 0))
    elif limit_direction == 'backward':
        preserve_nans = end_nans | set(_interp_limit(invalid, 0, limit))
    else:
        # both directions... just use _interp_limit
        preserve_nans = set(_interp_limit(invalid, limit, limit))

    # if limit_area is set, add either mid or outside indices
    # to preserve_nans GH #16284
    if limit_area == 'inside':
        # preserve NaNs on the outside
        preserve_nans |= start_nans | end_nans
    elif limit_area == 'outside':
        # preserve NaNs on the inside
        preserve_nans |= mid_nans

    # sort preserve_nans and covert to list
    preserve_nans = sorted(preserve_nans)

    xvalues = getattr(xvalues, 'values', xvalues)
    yvalues = getattr(yvalues, 'values', yvalues)
    result = yvalues.copy()

    if method in ['linear', 'time', 'index', 'values']:
        if method in ('values', 'index'):
            inds = np.asarray(xvalues)
            # hack for DatetimeIndex, #1646
            if needs_i8_conversion(inds.dtype.type):
                inds = inds.view(np.int64)
            if inds.dtype == np.object_:
                inds = lib.maybe_convert_objects(inds)
        else:
            inds = xvalues
        result[invalid] = np.interp(inds[invalid], inds[valid], yvalues[valid])
        result[preserve_nans] = np.nan
        return result

    sp_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic',
                  'barycentric', 'krogh', 'spline', 'polynomial',
                  'from_derivatives', 'piecewise_polynomial', 'pchip', 'akima']

    if method in sp_methods:
        inds = np.asarray(xvalues)
        # hack for DatetimeIndex, #1646
        if issubclass(inds.dtype.type, np.datetime64):
            inds = inds.view(np.int64)
        result[invalid] = _interpolate_scipy_wrapper(inds[valid],
                                                     yvalues[valid],
                                                     inds[invalid],
                                                     method=method,
                                                     fill_value=fill_value,
                                                     bounds_error=bounds_error,
                                                     order=order, **kwargs)
        result[preserve_nans] = np.nan
        return result