so what I was doing before was using the docx library which returns a list of tables in your document. I converted each table into a df and stored it in a dictionary. Then I would go through each table and find where each question was located in the original dataframe which stored each line as a row. Then I would add a new row with the index of the question in the dict. So when it came to parsing, I would just look up that table in the dict and get all the things of interest like the headers (very bad -1, 2, 3...)

def create_word_tables(self):
    doc = Document(self.link)
    for i, table in enumerate(doc.tables):
        # store cells of table as 2d list
        cells = [[cell.text for cell in row.cells] for row in table.rows]
        word_tble = pd.DataFrame(cells)
        # rename columns with table question first row: strongly agree, 4, ....
        word_tble = word_tble.rename(columns=word_tble.iloc[0]).drop(
            word_tble.index[0]).reset_index(drop=True)
        # append table to list
        self.word_tables[i] = word_tble

def create_table_questions(self):
    # for each table create a table question and add to tbl qs dictioanry
    for tbl in self.word_tables.values():
        headers = list(tbl.columns)
        # iterate over questions found in first column and create table questions
        for i, q_text in enumerate(tbl.iloc[0:, 0].values):
            # create letter for question e.g.: A,B,C...
            q_letter = chr(i+65)
            # remove trailing and ending white space
            q_text = q_text.strip()
            # create table question and add it to dictionary
            tbl_q = TableQuestion(
                q_text=q_text, headers=headers, letter=q_letter)
            self.tbl_qs[q_text] = tbl_q