class_docstr: Container for data heatmap calculation and visualization.
  
  This class (object) is returned from the ``data_heatmap`` function. The
  attributes documented below can be accessed or extracted.
  
  
  Attributes:
      input_data: The input data.
      colnames: Names of numeric columns.
      std_data: The transposed, standardized data after scaling.
      missing: If True, the heatmap shows missing values as indicators
          instead of standardized values.
      missing_data: The missing value indicator data.
class_docstr_tok: ["Container","for","data","heatmap","calculation","and","visualization",".","This","class","(","object",")","is","returned","from","the","``","data_heatmap","``","function",".","The","attributes","documented","below","can","be","accessed","or","extracted",".","Attributes",":","input_data",":","The","input","data",".","colnames",":","Names","of","numeric","columns",".","std_data",":","The","transposed",",","standardized","data","after","scaling",".","missing",":","If","True",",","the","heatmap","shows","missing","values","as","indicators","instead","of","standardized","values",".","missing_data",":","The","missing","value","indicator","data","."]
code: def __repr__(self):
      mode = "missing" if self.missing else "standardized"
      return f"Heatmap Widget showing {mode} values."
  
imports: from typing import List
  import pandas
  import numpy
  from plotly.offline import init_notebook_mode, iplot
  from matplotlib.figure import Figure
  import seaborn
  import plotly.graph_objs
  from sklearn.preprocessing import StandardScaler
  from data_describe._widget import BaseWidget
  from data_describe.config._config import get_option
  from data_describe.compat import _is_dataframe, _requires, _in_notebook
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 3 for calling the HeatmapWidget obj's underlying member method code with example usage: obj.__repr__() and returns: str, mode, str