class_docstr: Visualization Module for Natural Language Processing
  
  Attributes:
      df (pd.DataFrame): Original dataframe to be graphed.
      target_col: Columns to be analyzed that exist in df (assuming type list) e.g. [hoge, fuga, ...]
      output_file_path: path to save the html file of the generated graph
      default_stopwords_file_path: The path to the file that defines the default stopword
class_docstr_tok: ["Visualization","Module","for","Natural","Language","Processing","Attributes",":","df","(","pd.DataFrame",")",":","Original","dataframe","to","be","graphed",".","target_col",":","Columns","to","be","analyzed","that","exist","in","df","(","assuming","type","list",")","e.g",".","[","hoge",",","fuga",",","...","]","output_file_path",":","path","to","save","the","html","file","of","the","generated","graph","default_stopwords_file_path",":","The","path","to","the","file","that","defines","the","default","stopword"]
docstr: Plots of sunburst chart
  
  Args:
      title (str): title of plot
      colorscale (bool): Size of the maker
      color_col (str): Color-coded column names.
      color_continuous_scale (str): cf.https://plotly.com/python/builtin-colorscales/
      width (int): width of the graph
      height (int): height of the graph
      save (bool): Whether or not to save the HTML file.
  
  Returns:
      px.sunburst: Figure of a sunburst graph
docstr_tok: ["Plots","of","sunburst","chart","Args",":","title","(","str",")",":","title","of","plot","colorscale","(","bool",")",":","Size","of","the","maker","color_col","(","str",")",":","Color-coded","column","names",".","color_continuous_scale","(","str",")",":","cf.https",":","\/\/plotly.com\/python\/builtin-colorscales\/","width","(","int",")",":","width","of","the","graph","height","(","int",")",":","height","of","the","graph","save","(","bool",")",":","Whether","or","not","to","save","the","HTML","file",".","Returns",":","px.sunburst",":","Figure","of","a","sunburst","graph"]
code: def sunburst(
      self,
      title,
      colorscale=False,
      color_col="betweeness_centrality",
      color_continuous_scale="Oryel",
      width=1100,
      height=1100,
      save=False,
  ) -> px.sunburst:
      """Plots of sunburst chart
  
      Args:
          title (str): title of plot
          colorscale (bool): Size of the maker
          color_col (str): Color-coded column names.
          color_continuous_scale (str): cf.https://plotly.com/python/builtin-colorscales/
          width (int): width of the graph
          height (int): height of the graph
          save (bool): Whether or not to save the HTML file.
  
      Returns:
          px.sunburst: Figure of a sunburst graph
  
      """
  
      # make copy of node dataframe
      _df = self.node_df.copy()
  
      # change community label to string (needed for plot)
      _df.loc[:, "community"] = _df["community"].map(lambda x: str(x))
  
      # conditionals for plot type
      if colorscale is False:
          fig = px.sunburst(
              _df,
              path=["community", "id"],
              values="adjacency_frequency",
              color="community",
              hover_name=None,
              hover_data=None,
          )
      else:
          fig = px.sunburst(
              _df,
              path=["community", "id"],
              values="adjacency_frequency",
              color=color_col,
              hover_data=None,
              color_continuous_scale=color_continuous_scale,
              color_continuous_midpoint=np.average(
                  _df[color_col], weights=_df[color_col]
              ),
          )
  
      fig.update_layout(
          title=str(title),
          width=width,
          height=height,
      )
  
      if save:
          self.save_plot(fig, title)
  
      del _df
      gc.collect()
      return fig
  
imports: import os
  import gc
  import itertools
  from io import BytesIO
  from PIL import Image
  from collections import defaultdict, Counter
  import datetime
  import pandas
  import numpy
  from tqdm import tqdm
  from sklearn import preprocessing
  import seaborn
  import plotly
  import plotly.graph_objs
  import plotly.express
  from wordcloud import WordCloud
  import networkx
  from networkx.algorithms import community
label_desc: Use image node_id 11 for calling the NLPlot obj's underlying member method code with example usage: obj.sunburst(title, colorscale, color_col, color_continuous_scale, width, height, save) and returns: fig