class_docstr: From http://www.cs.columbia.edu/~julia/papers/dict_of_affect/DictionaryofAffect.
  
  Developed by Cynthia Whissell at Laurentian University.
  
  Header:
  new dictionary with imagery (c) C Whissell
  
  
  comment w=word, ee=pleasantness, aa=activation, ii=imagery
  
  comment scores for pleasantness range from 1 (unpleasant) to 3 (pleasant)
          scores for activation range from 1 (passive) to 3 (active)
          scores for imagery range from 1 (difficult to form a meantal
            picture of this word) to 3 (easy to form a mental picture)
  
  comment pleasantness mean=1.84, sd=.44
          activation mean=1.85, sd=.39
          imagery (does word give you a clear mental picture) mean=1.94, sd=.63
  
  comment these values have been tested on 348,000 words of natural language
      the dictionary has a 90% matching rate for this corpus
      mean ee is 1.85, with an sd of .36
      mean aa is 1.67, with an sd of .36
      mean ii is 1.52, with an sd of .63
docstr: Returns
  -------
  pd.DataFrame
  
  I.e.,
  >>> print(WhissellsLexicon.get_data())
               activation  imagery  pleasantness
  word
  a                1.3846      1.0        2.0000
  abandon          2.3750      2.4        1.0000
  abandoned        2.1000      3.0        1.1429
  abandonment      2.0000      1.4        1.0000
  abated           1.3333      1.2        1.6667
docstr_tok: ["Returns","--","--","--","-","pd.DataFrame","I.e.",",",">",">",">","print","(","WhissellsLexicon.get_data","(",")",")","activation","imagery","pleasantness","word","a","1.3846","1.0","2.0000","abandon","2.3750","2.4","1.0000","abandoned","2.1000","3.0","1.1429","abandonment","2.0000","1.4","1.0000","abated","1.3333","1.2","1.6667"]
code: def get_data():
      """
      Returns
      -------
      pd.DataFrame
  
      I.e.,
      >>> print(WhissellsLexicon.get_data())
                   activation  imagery  pleasantness
      word
      a                1.3846      1.0        2.0000
      abandon          2.3750      2.4        1.0000
      abandoned        2.1000      3.0        1.1429
      abandonment      2.0000      1.4        1.0000
      abated           1.3333      1.2        1.6667
      """
      data_stream = pkgutil.get_data(
          "scattertext", "data/whissells_df.csv"
      )
      return (
          pd.read_csv(io.StringIO(data_stream.decode("utf8")))
          .set_index("word")
          .astype(np.float64)
      )
  
imports: import io
  import pkgutil
  import pandas
  import numpy
label_desc: Use image node_id 1 for calling the WhissellsLexicon obj's underlying member method code with example usage: obj.get_data() and returns: pd