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

def clean_cornell_movies(filename='cornell_movie_dialogs_corpus.zip', subdir='cornell movie-dialogs corpus'):
    """ 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
    """
    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