Dataframe Object is not callable
<p>When I run it, it keeps telling me the dataframe object is not callable. </p>

<pre><code>class OptionDataWebGleaner():

    def __init__(self):

        ticker = pd.read_csv('Yahoo_ticker_List.csv')['AUB.AX'].values
        stock = raw_input('Please give the ticker of your selected option?\n')

        if stock in ticker:

            self.stock = stock
        else:

            raise TypeError('Your option is not available here.')

        date_norm = raw_input('Please give your maturity date in the format of mm/dd/yyyy\n')

        maturity_date = datetime.strptime(date_norm, '%m/%d/%Y').date()

        self.maturity_date = maturity_date
        self.today = date.today()

        dates = ['1481846400', '1484870400', '1487289600']
        maturity_dates = [date(2016, 12, 16), date(2017, 1, 20), date(2017, 2, 17)]
        date_dict = {}

        for v in zip(dates, maturity_dates):
            date_dict[v[1]] = v[0]

        try:
            self.d = date_dict[self.maturity_date]
        except:
            print('Your maturuity date is not available')

        option = raw_input('Please give the type of your option, either call or put\n')

        self.option_type = option + 's'

    @property
    def crawl_data(self):  # self #option_type: calls or puts. str

        stock = self.stock
        option_type = self.option_type
        maturity_date = self.maturity_date
        d = self.d

        chromedriver = "/Users/Miya/Downloads/chromedriver.exe"
        os.environ["webdriver.chrome.driver"] = chromedriver
        driver = webdriver.Chrome(chromedriver)
        today = self.today

        ## Get the url
        url = 'http://finance.yahoo.com/quote/' + stock + '/options?date=' + d
        ## Crawl data
        driver.get(url)
        html_source = driver.page_source
        ## Beautifulsoup
        soup = BeautifulSoup(html_source, 'html.parser')


        if soup.find('table', option_type) is not None:

            stock_price = [float(i.text) for i in soup.findAll('span', 'Fz(36px)')]
            title = [i.text for i in soup.find('table', option_type).find_all('th')]
            text = [i.text for i in soup.find('table', option_type).find_all('td')]
            rows = [row for row in soup.find('table', option_type).find_all("tr")]

            l_table = len(rows) - 1
            ## call/put data
            dictionary = {}
            dictionary['maturity_date'] = [maturity_date] * l_table
            dictionary['date'] = [today] * l_table
            dictionary['stock_price'] = stock_price * l_table

            for j in range(10):
                key = title[j]
                dictionary[key] = []
                for i in range(l_table):
                    dictionary[key].append(text[10 * i + j])

            ## write into dataframe

            dataframe = pd.DataFrame(dictionary)


        return dataframe

    def clean_data(self):

        dataframe = self.crawl_data()

        print('Remove unexpected symbols...')

        columns_to_set = ['Last Price', 'Open Interest', 'Strike', 'Volume', 'Implied Volatility']
        for i in columns_to_set:
            series = dataframe[i]
            series_new = []
            for j in series:
                j = str(j)
                j_new = ''.join(ch for ch in j if (ch != '%') and (ch != ','))
                series_new.append(j_new)
            dataframe[i] = series_new
        print('Change the data type...')

        ## change the dtype
        columns_to_change = ['Last Price', 'Open Interest', 'Strike', 'Volume', 'stock_price', 'Implied Volatility']

        for i in columns_to_change:
            dataframe_cleaned[i] = dataframe[i].astype(float)

        print("Remove missing values...")

        dataframe_cleaned = dataframe_cleaned.dropna()

        # print("Clean Outliers...")

        # dataframe = dataframe.loc[dataframe['Implied Volatility'] &lt;= 2]

        return dataframe_cleaned

    def save_file(self):

        save_file = raw_input("Do you want to save the file into csv? Type Y for yes, N or no\n ")
        d = self.d
        stock = self.stock
        df_option = self.clean_data()

        if save_file == 'Y':
            csv_name = stock + d + '.csv'
            df_option.to_csv(csv_name)
            print("File Saved!")

    def viz(self):

        dataframe = self.clean_data()
        stock = self.stock
        time_to_maturity = []
        dataframe = dataframe.sort_values(by='Strike')
        ## grab dataframe, then relevant data
        for i, j in zip(dataframe.maturity_date, dataframe.date):
            time_to_maturity.append((i - j).days / 365)

        strike_price = dataframe['Strike']

        # generate pseudo-implied volatility by using strike price and time-to-maturity as parameters

        implied_vol = dataframe['Implied Volatility'].values

        strike_price, time_to_maturity = np.meshgrid(strike_price, time_to_maturity)

        fig = plot.figure(figsize=(10, 5))  ## a plot object
        ax = Axes3D(fig)  # create a 3D object/handle

        ##plot surface: array row/column stride(step size:2)
        ##plot surface: array row/column stride(step size:2)

        surf = ax.plot_surface(strike_price, time_to_maturity, implied_vol, rstride=2, cstride=2, cmap=cm.coolwarm,
                               linewidth=0.5, antialiased=False)

        # set x,y,a labels
        ax.set_xlabel('Strike Price')
        ax.set_ylabel('time to maturity')
        ax.set_zlabel('implied volatility%')
        plot.suptitle(stock)
        plot.show()

    def summary(self):

        dataframe = self.clean_data

        print(dataframe.describe())


OptionDataWebGleaner().viz()
</code></pre>