class_docstr: A smoothed conditional mean
  
  {usage}
  
  Parameters
  ----------
  {common_parameters}
  legend_fill_ratio : float, default=0.5
      How much (vertically) of the legend box should be filled by
      the color that indicates the confidence intervals. Should be
      in the range [0, 1].
class_docstr_tok: ["A","smoothed","conditional","mean","{","usage","}","Parameters","--","--","--","--","--","{","common_parameters","}","legend_fill_ratio",":","float",",","default=0.5","How","much","(","vertically",")","of","the","legend","box","should","be","filled","by","the","color","that","indicates","the","confidence","intervals",".","Should","be","in","the","range","[","0",",","1","]","."]
code: def use_defaults(
      self, data: pd.DataFrame, aes_modifiers: dict[str, Any]
  ) -> pd.DataFrame:
      has_ribbon = "ymin" in data and "ymax" in data
      data = super().use_defaults(data, aes_modifiers)
  
      # When there is no ribbon, the default values for 'ymin'
      # and 'ymax' are None (not numeric). So we remove them
      # prevent any computations that may use them without checking.
      if not has_ribbon:
          del data["ymin"]
          del data["ymax"]
      return data
  
file_path: plotnine/plotnine/geoms/geom_smooth.py
imports: from __future__ import annotations
  import typing
  from .._utils import to_rgba
  from ..doctools import document
  from .geom import geom
  from .geom_line import geom_line, geom_path
  from .geom_ribbon import geom_ribbon
label_desc: Use image node_id 1 for calling the geom_smooth obj's underlying member method code with example usage: obj.use_defaults(data, aes_modifiers) and returns: data