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
  ----------
  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 features is None, feature
      names are selected as the columns of the DataFrame.
  
  figsize : tuple, optional
          (width, height) for specifying the size of the plot.
  
  orient : 'h' or 'v', default='h'
      Specifies a horizontal or vertical bar chart.
  
  color: string
      Specify color for barchart
  
  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).
  
  Attributes
  ----------
  ranks_ : ndarray
      An array of rank scores with shape (n,), where n is the
      number of features.
  
  Examples
  ---------
  .. plot:: ../examples/feature_ranking_1D.py
  
  Notes
  -----
  .. versionadded:: 0.8.4
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","--","--","--","--","--","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","features","is","None",",","feature","names","are","selected","as","the","columns","of","the","DataFrame",".","figsize",":","tuple",",","optional","(","width",",","height",")","for","specifying","the","size","of","the","plot",".","orient",":","'","h","'","or","'","v","'",",","default=","'","h'","Specifies","a","horizontal","or","vertical","bar","chart",".","color",":","string","Specify","color","for","barchart","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",")",".","Attributes","--","--","--","--","--","ranks_",":","ndarray","An","array","of","rank","scores","with","shape","(","n",",",")",",","where","n","is","the","number","of","features",".","Examples","--","--","--","--","-","..","plot",":",":","..","\/examples\/feature_ranking_1D.py","Notes","--","--","-","..","versionadded",":",":","0.8.4"]
code: def __init__(
      self,
      algorithm="shapiro",
      features=None,
      figsize=(7, 7),
      orient="h",
      color="g",
      ax=None,
  ):
      super().__init__(
          algorithm=algorithm, features=features, figsize=figsize, ax=ax
      )
      self.color = color
      self.orientation_ = orient
  
imports: import numpy
  import pandas
  from scipy.stats import shapiro
  from scipy.stats import spearmanr
  from scipy.stats import kendalltau
  from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
  from ploomber_core.exceptions import modify_exceptions
  import matplotlib.pyplot
label_desc: Use image node_id 1 to create a new Rank1D object from inherited base classes: RankD with example: obj = Rank1D(algorithm, features, figsize, orient, color, ax)