docstr: Divide the data by the difference between the max and min
  
  Parameters
  ----------
  data2d : pandas.DataFrame
      Data to normalize
  axis : int
      Which axis to normalize across. If 0, normalize across rows, if 1,
      normalize across columns.
  
  Returns
  -------
  standardized : pandas.DataFrame
      Noramlized data with a mean of 0 and variance of 1 across the
      specified axis.
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code: def standard_scale(data2d, axis=1):
      """Divide the data by the difference between the max and min
  
      Parameters
      ----------
      data2d : pandas.DataFrame
          Data to normalize
      axis : int
          Which axis to normalize across. If 0, normalize across rows, if 1,
          normalize across columns.
  
      Returns
      -------
      standardized : pandas.DataFrame
          Noramlized data with a mean of 0 and variance of 1 across the
          specified axis.
  
      """
      # Normalize these values to range from 0 to 1
      if axis == 1:
          standardized = data2d
      else:
          standardized = data2d.T
  
      subtract = standardized.min()
      standardized = (standardized - subtract) / (
          standardized.max() - standardized.min()
      )
  
      if axis == 1:
          return standardized
      else:
          return standardized.T
  
imports: import warnings
  import matplotlib
  from matplotlib.collections import LineCollection
  import matplotlib.pyplot
  from matplotlib import gridspec
  import numpy
  import pandas
  from .None import cm
  from .axisgrid import Grid
  from ._compat import get_colormap
  from .utils import despine, axis_ticklabels_overlap, relative_luminance, to_utf8, _draw_figure
label_desc: Use image node_id 5 for calling the ClusterGrid obj's underlying member method code with example usage: obj.standard_scale(data2d, axis) and returns: standardized, standardized