class_docstr: A frequency distribution tells us the frequency of each vocabulary
  item in the text. In general, it could count any kind of observable
  event. It is a distribution because it tells us how the total
  number of word tokens in the text are distributed across the
  vocabulary items.
  
  
  Parameters
  ----------
  features : list, default: None
      The list of feature names from the vectorizer, ordered by index. E.g.
      a lexicon that specifies the unique vocabulary of the corpus. This
      can be typically fetched using the ``get_feature_names()`` method of
      the transformer in Scikit-Learn.
  
  ax : matplotlib axes, default: None
      The axes to plot the figure on.
  
  n: integer, default: 50
      Top N tokens to be plotted.
  
  orient : 'h' or 'v', default: 'h'
      Specifies a horizontal or vertical bar chart.
  
  color : string
      Specify color for bars
  
  kwargs : dict
      Pass any additional keyword arguments to the super class.
  
  These parameters can be influenced later on in the visualization
  process, but can and should be set as early as possible.
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docstr: Called from the fit method, this method gets all the
  words from the corpus and their corresponding frequency
  counts.
  
  Parameters
  ----------
  
  X : ndarray or masked ndarray
      Pass in the matrix of vectorized documents, can be masked in
      order to sum the word frequencies for only a subset of documents.
  
  Returns
  -------
  
  counts : array
      A vector containing the counts of all words in X (columns)
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code: def count(self, X):
      """
      Called from the fit method, this method gets all the
      words from the corpus and their corresponding frequency
      counts.
  
      Parameters
      ----------
  
      X : ndarray or masked ndarray
          Pass in the matrix of vectorized documents, can be masked in
          order to sum the word frequencies for only a subset of documents.
  
      Returns
      -------
  
      counts : array
          A vector containing the counts of all words in X (columns)
  
      """
      # Sum on axis 0 (by columns), each column is a word
      # Convert the matrix to an array
      # Squeeze to remove the 1 dimension objects (like ravel)
      return np.squeeze(np.asarray(X.sum(axis=0)))
  
file_path: yellowbrick/yellowbrick/text/freqdist.py
imports: import numpy
  from operator import itemgetter
  from yellowbrick.text.base import TextVisualizer
  from yellowbrick.exceptions import YellowbrickValueError
label_desc: Use image node_id 2 for calling the FrequencyVisualizer obj's underlying member method code with example usage: obj.count(X) and returns: np