old_contents: from pandas import DataFrame, Series, concat
  
  ################################################################################
  #                                                                          VOCAB
  ################################################################################
  class Vocab(object):
      """
      Vocabulary object, that stores frequency counts of words from some corpus,
      along with methods for trimming down the vocabulary size based on max
      number of items, and min frequency count.
  
      Allows you to convert from token strings to word indices, and vice versa.
      """
      ############################################################################
      # ==========================================================================
      #                                                                   __INIT__
      # ==========================================================================
      def __init__(self):
          self.vocab = DataFrame()
          self.w2i = Series() # word to index Series
          self.i2w = Series() # index to word Series
          self.size = 0       # vocab size
  
      def from_tokenized_lists(self, toklist):
          print("Extracting the vocab from a tokenized list")
          self.vocab = dict()
          for sentence in toklist:
              for word in sentence:
                  # If the word exists in wordcount, increment the value by 1. Otherwise
                  # create a new key, initialised to 0, and increment by 1.
                  self.vocab[word] = self.vocab.get(word, 0) + 1
  
          self.vocab = Series(self.vocab)
          self.vocab.sort_values(ascending=False, inplace=True)
          self.vocab = concat([Series({u"UNKNOWN":0}), self.vocab], ignore_index=False)
          self.w2i = Series(range(self.vocab.size), index=self.vocab.index)
          self.i2w = self.vocab.index
          self.size = self.vocab.size
          print("---Done!")
  
new_contents: from pandas import DataFrame, Series, concat
  
  ################################################################################
  #                                                                          VOCAB
  ################################################################################
  class Vocab(object):
      """
      Vocabulary object, that stores frequency counts of words from some corpus,
      along with methods for trimming down the vocabulary size based on max
      number of items, and min frequency count.
  
      Allows you to convert from token strings to word indices, and vice versa.
      """
      ############################################################################
      # ==========================================================================
      #                                                                   __INIT__
      # ==========================================================================
      def __init__(self):
          self.vocab = DataFrame()
          self.w2i = Series() # word to index Series
          self.i2w = Series() # index to word Series
          self.size = 0       # vocab size
  
      # ==========================================================================
      #                                                       FROM_TOKENIZED_LISTS
      # ==========================================================================
      def from_tokenized_lists(self, toklist):
          """
          Takes a list of lists of word tokens, and generates the vocabulary based
          on those words.
  
          :param toklist: (list of lists of strings)
          """
          print("Extracting the vocab from a tokenized list")
          self.vocab = dict()
          for sentence in toklist:
              for word in sentence:
                  # If the word exists in wordcount, increment the value by 1. Otherwise
                  # create a new key, initialised to 0, and increment by 1.
                  self.vocab[word] = self.vocab.get(word, 0) + 1
  
          self.vocab = Series(self.vocab)
          self.vocab.sort_values(ascending=False, inplace=True)
          self.vocab = concat([Series({u"UNKNOWN":0}), self.vocab], ignore_index=False)
          self.w2i = Series(range(self.vocab.size), index=self.vocab.index)
          self.i2w = self.vocab.index
          self.size = self.vocab.size
          print("---Done!")
  
subject: Add docstring to from_tokenized_lists() method in Vocab class