# xarray.DataArray.interp

`DataArray.``interp`(coords: Mapping[Hashable, Any] = None, method: str = 'linear', assume_sorted: bool = False, kwargs: Mapping[str, Any] = None, **coords_kwargs) → xarray.core.dataarray.DataArray

  Multidimensional interpolation of variables.

  coords : dict, optional
    Mapping from dimension names to the new coordinates. new coordinate can be an scalar, array-like or DataArray. If DataArrays are passed as new coordates, their dimensions are used for the broadcasting.
  method: {‘linear’, ‘nearest’} for multidimensional array,
    {‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’} for 1-dimensional array.
  assume_sorted: boolean, optional
    If False, values of x can be in any order and they are sorted first. If True, x has to be an array of monotonically increasing values.
  kwargs: dictionary
    Additional keyword passed to scipy’s interpolator.
  `**coords_kwargs` : {dim: coordinate, …}, optional
    The keyword arguments form of `coords`. One of coords or coords_kwargs must be provided.

  Returns: | interpolated – New dataarray on the new coordinates.
  Return type: | xr.DataArray

  Notes

  scipy is required.

  See also

  `scipy.interpolate.interp1d()`, `scipy.interpolate.interpn()`

  Examples

      >>> da = xr.DataArray([1, 3], [('x', np.arange(2))])
      >>> da.interp(x=0.5)
      <xarray.DataArray ()>
      array(2.0)
      Coordinates:
          x        float64 0.5