docstr: Parameters
  ----------
  term_doc_matrix : TermDocMatrix
          The term doc matrix to use for the scatter chart.
  minimum_term_frequency : int, optional
          Minimum times an ngram has to be seen to be included. Default is 3.
  minimum_not_category_term_frequency : int, optional
    If an n-gram does not occur in the category, minimum times it
     must been seen to be included. Default is 0.
  jitter : float, optional
          Maximum amount of noise to be added to points, 0.2 is a lot. Default is None to disable jitter.
  seed : float, optional
          Random seed. Default 0
  pmi_threshold_coefficient : int
          Filter out bigrams with a PMI of < 2 * pmi_threshold_coefficient. Default is 3
  max_terms : int, optional
          Maximum number of terms to include in visualization
  filter_unigrams : bool, optional
          If True, remove unigrams that are part of bigrams. Default is False.
  term_ranker : TermRanker, optional
          TermRanker class for determining term frequency ranks.
  use_non_text_features : bool, default = False
          Use non-BoW features (e.g., Empath) instead of text features
  term_significance : TermSignificance instance or None
          Way of getting significance scores.  If None, p values will not be added.
  terms_to_include : set or None
          Only annotate these terms in chart
  score_transform : function
          Transforms original scores into value between 0 and 1. Default is percentile_min
  dont_filter : bool, default is False
          Don't do any filtering of dataframe
  add_extra_features : bool, default is False
          Used in pairplot to add the extra doc structure
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code: def __init__(
      self,
      minimum_term_frequency=3,
      minimum_not_category_term_frequency=0,
      jitter=None,
      seed=0,
      pmi_threshold_coefficient=3,
      max_terms=None,
      filter_unigrams=False,
      term_ranker=AbsoluteFrequencyRanker,
      use_non_text_features=False,
      term_significance=None,
      terms_to_include=None,
      score_transform=percentile_min,
      dont_filter=False,
      add_extra_features=False,
  ):
      """
  
      Parameters
      ----------
      term_doc_matrix : TermDocMatrix
              The term doc matrix to use for the scatter chart.
      minimum_term_frequency : int, optional
              Minimum times an ngram has to be seen to be included. Default is 3.
      minimum_not_category_term_frequency : int, optional
        If an n-gram does not occur in the category, minimum times it
         must been seen to be included. Default is 0.
      jitter : float, optional
              Maximum amount of noise to be added to points, 0.2 is a lot. Default is None to disable jitter.
      seed : float, optional
              Random seed. Default 0
      pmi_threshold_coefficient : int
              Filter out bigrams with a PMI of < 2 * pmi_threshold_coefficient. Default is 3
      max_terms : int, optional
              Maximum number of terms to include in visualization
      filter_unigrams : bool, optional
              If True, remove unigrams that are part of bigrams. Default is False.
      term_ranker : TermRanker, optional
              TermRanker class for determining term frequency ranks.
      use_non_text_features : bool, default = False
              Use non-BoW features (e.g., Empath) instead of text features
      term_significance : TermSignificance instance or None
              Way of getting significance scores.  If None, p values will not be added.
      terms_to_include : set or None
              Only annotate these terms in chart
      score_transform : function
              Transforms original scores into value between 0 and 1. Default is percentile_min
      dont_filter : bool, default is False
              Don't do any filtering of dataframe
      add_extra_features : bool, default is False
              Used in pairplot to add the extra doc structure
      """
      self.jitter = jitter
      self.minimum_term_frequency = minimum_term_frequency
      self.minimum_not_category_term_frequency = (
          minimum_not_category_term_frequency
      )
      self.seed = seed
      self.pmi_threshold_coefficient = pmi_threshold_coefficient
      self.filter_unigrams = filter_unigrams
      self.term_ranker = term_ranker
      self.max_terms = max_terms
      self.use_non_text_features = use_non_text_features
      self.term_significance = term_significance
      self.terms_to_include = terms_to_include
      self.score_transform = score_transform
      self.dont_filter = dont_filter
      self.add_extra_features = add_extra_features
      np.random.seed(seed)
  
imports: import numpy
  from scattertext.Scalers import percentile_min
  from scattertext.termranking import AbsoluteFrequencyRanker
label_desc: Use image node_id 1 to create a new ScatterChartData object from inherited base classes: object with example: obj = ScatterChartData(minimum_term_frequency, minimum_not_category_term_frequency, jitter, seed, pmi_threshold_coefficient, max_terms, filter_unigrams, term_ranker, use_non_text_features, term_significance, terms_to_include, score_transform, dont_filter, add_extra_features)