class_docstr: Rank1D computes a score for each feature in the data set with a specific
  metric or algorithm (e.g. Shapiro-Wilk) then returns the features ranked
  as a bar plot.
  
  Parameters
  ----------
  ax : matplotlib Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  algorithm : one of {'shapiro', }, default: 'shapiro'
      The ranking algorithm to use, default is 'Shapiro-Wilk.
  
  features : list
      A list of feature names to use.
      If a DataFrame is passed to fit and features is None, feature
      names are selected as the columns of the DataFrame.
  
  orient : 'h' or 'v', default='h'
      Specifies a horizontal or vertical bar chart.
  
  show_feature_names : boolean, default: True
      If True, the feature names are used to label the x and y ticks in the
      plot.
  
  color: string
      Specify color for barchart
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  ranks_ : ndarray
      An array of rank scores with shape (n,), where n is the
      number of features. It is computed during `fit`.
  
  Examples
  --------
  >>> visualizer = Rank1D()
  >>> visualizer.fit(X, y)
  >>> visualizer.transform(X)
  >>> visualizer.show()
class_docstr_tok: ["Rank1D","computes","a","score","for","each","feature","in","the","data","set","with","a","specific","metric","or","algorithm","(","e.g",".","Shapiro-Wilk",")","then","returns","the","features","ranked","as","a","bar","plot",".","Parameters","--","--","--","--","--","ax",":","matplotlib","Axes",",","default",":","None","The","axis","to","plot","the","figure","on",".","If","None","is","passed","in","the","current","axes","will","be","used","(","or","generated","if","required",")",".","algorithm",":","one","of","{","'shapiro","'",",","}",",","default",":","'shapiro'","The","ranking","algorithm","to","use",",","default","is","'Shapiro-Wilk",".","features",":","list","A","list","of","feature","names","to","use",".","If","a","DataFrame","is","passed","to","fit","and","features","is","None",",","feature","names","are","selected","as","the","columns","of","the","DataFrame",".","orient",":","'","h","'","or","'","v","'",",","default=","'","h'","Specifies","a","horizontal","or","vertical","bar","chart",".","show_feature_names",":","boolean",",","default",":","True","If","True",",","the","feature","names","are","used","to","label","the","x","and","y","ticks","in","the","plot",".","color",":","string","Specify","color","for","barchart","kwargs",":","dict","Keyword","arguments","that","are","passed","to","the","base","class","and","may","influence","the","visualization","as","defined","in","other","Visualizers",".","Attributes","--","--","--","--","--","ranks_",":","ndarray","An","array","of","rank","scores","with","shape","(","n",",",")",",","where","n","is","the","number","of","features",".","It","is","computed","during","`","fit","`",".","Examples","--","--","--","--",">",">",">","visualizer","=","Rank1D","(",")",">",">",">","visualizer.fit","(","X",",","y",")",">",">",">","visualizer.transform","(","X",")",">",">",">","visualizer.show","(",")"]
docstr: Draws the bar plot of the ranking array of features.
docstr_tok: ["Draws","the","bar","plot","of","the","ranking","array","of","features","."]
code: def draw(self, **kwargs):
      """
      Draws the bar plot of the ranking array of features.
      """
      if self.orientation_ == "h":
          # Make the plot
          self.ax.barh(
              np.arange(len(self.ranks_)), self.ranks_, color=self.color
          )
  
          # Add ticks and tick labels
          self.ax.set_yticks(np.arange(len(self.ranks_)))
          if self.show_feature_names_:
              self.ax.set_yticklabels(self.features_)
          else:
              self.ax.set_yticklabels([])
  
          # Order the features from top to bottom on the y axis
          self.ax.invert_yaxis()
  
          # Turn off y grid lines
          self.ax.yaxis.grid(False)
  
      elif self.orientation_ == "v":
          # Make the plot
          self.ax.bar(
              np.arange(len(self.ranks_)), self.ranks_, color=self.color
          )
  
          # Add ticks and tick labels
          self.ax.set_xticks(np.arange(len(self.ranks_)))
          if self.show_feature_names_:
              self.ax.set_xticklabels(self.features_, rotation=90)
          else:
              self.ax.set_xticklabels([])
  
          # Turn off x grid lines
          self.ax.xaxis.grid(False)
  
      else:
          raise YellowbrickValueError("Orientation must be 'h' or 'v'")
  
imports: import warnings
  import numpy
  import matplotlib
  from scipy.stats import shapiro
  from scipy.stats import spearmanr
  from scipy.stats import kendalltau
  from yellowbrick.utils import is_dataframe
  from yellowbrick.features.base import MultiFeatureVisualizer
  from yellowbrick.exceptions import YellowbrickValueError, YellowbrickWarning
label_desc: Use image node_id 2 for calling the Rank1D obj's underlying member method code with example usage: obj.draw() without return types