code: def _scale_dataframe_data(inst, data, picks, scalings):
      ch_types = inst.get_channel_types()
      ch_types_used = list()
      scalings = _handle_default("scalings", scalings)
      for tt in scalings.keys():
          if tt in ch_types:
              ch_types_used.append(tt)
      for tt in ch_types_used:
          scaling = scalings[tt]
          idx = [ii for ii in range(len(picks)) if ch_types[ii] == tt]
          if len(idx):
              data[:, idx] *= scaling
      return data
  
file_path: mne-python/mne/utils/dataframe.py
imports: from inspect import signature
  import numpy
  from ..defaults import _handle_default
  from ._logging import logger, verbose
label_desc: Use image node_id 2 for calling a global function with example usage: _scale_dataframe_data(inst, data, picks, scalings) and returns: data