Объект Dataframe не может быть вызван

Question

Когда я запустил его, он продолжает сообщать мне, что объект dataframe не может быть вызван.

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'] <= 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() 

Answer 1

Проблема заключается в декораторе свойств на crawl_data. This answer объясняет, как работает декоратор свойств, но в основном, dataframe.crawl_data - это dataframe, возвращаемый функцией, а не функция. Так dataframe.crawl_data() в первой строке clean_data пытается вызвать dataframe, а не функцию.

Вот пример:

>>> class Test(object): 
...  @property 
...  def example(self): 
...    return 1 
... 
>>> t = Test() 
>>> t.example 
1 
>>> t.example() 
Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
TypeError: 'int' object is not callable 

Этот вопрос действительно мог бы сделать с StackTrace. Это привело бы нас к тому, что мы сможем справиться с проблемным вызовом.