code: def clean_cornell_movies(filename='cornell_movie_dialogs_corpus.zip', subdir='cornell movie-dialogs corpus'):
      
      fullpath_zipfile = find_filepath(filename)
      dirname = os.path.basename(filename)
      subdir = 'cornell movie-dialogs corpus'
      if fullpath_zipfile.lower().endswith('.zip'):
          retval = unzip(fullpath_zipfile)
          dirname = dirname[:-4]
      fullpath_movie_lines = os.path.join(BIGDATA_PATH, dirname, subdir, 'movie_lines.txt')
      dialog = pd.read_csv(
          fullpath_movie_lines, sep=r'\+\+\+\$\+\+\+', engine='python', header=None, index_col=0)
      dialog.columns = 'user movie person utterance'.split()
      dialog.index.name = 'line'
      dialog.index = [int(s.strip()[1:]) for s in dialog.index.values]
      dialog.sort_index(inplace=True)
      for col in dialog.columns:
          dialog[col] = dialog[col].str.strip()
      return dialog
docs: Load a dataframe of ~100k raw (uncollated) movie lines from the cornell movies dialog corpus
      
      >>> local_filepath = download_file(BIG_URLS['cornell_movie_dialogs_corpus'][0])
      >>> df = clean_cornell_movies(filename='cornell_movie_dialogs_corpus.zip')
      >>> df.describe(include='all')
                user   movie  person utterance
      count   304713  304713  304713    304446
      unique    9035     617    5356    265783
      top      u4525    m289    JACK     What?
      freq       537    1530    3032      1684