How do I make my web scraping script more robust?

Question

I launched a code to scrape the Santander website.

Scraping seems to work, except that I get false results. And when I run the code twice in a row, the results change.

How could I make the scraping more robust, the problem is that when I run the code and check the results one by one, it seems to work well.

def hw_santander_scrape(Amount, Duration):
  from selenium import webdriver
  from selenium.webdriver.support.ui import WebDriverWait
  from selenium.webdriver.support import expected_conditions as EC
  chrome_options = webdriver.ChromeOptions()
  chrome_options.add_argument('--headless')
  chrome_options.add_argument('--no-sandbox')
  chrome_options.add_argument('--disable-dev-shm-usage')
  chrome_options.add_argument('--start-maximized')
  chrome_options.add_argument('window-size=10000x5000')
  webdriver = webdriver.Chrome('chromedriver', chrome_options = chrome_options)

  #
  import time
  maintenant = DT.now()
  period = str(maintenant.day) + '_' + str(maintenant.month) + '_' + str(maintenant.year)
  print('Start Scraping')

  ################################################ Santander###############################################

  Santander = pd.DataFrame({
      'Project': "reforma vivienda",
      'Period': period,
      'Monthly repayment': [0],
      'TIN': [0],
      'TAE': [0],
      'Total repayment': [0],
      'Initial amount': [0],
      'Duration': [0]
  })

  project = pd.DataFrame({
      'Project': "reforma vivienda",
      'Period': period,
      'Monthly repayment': [0],
      'TIN': [0],
      'TAE': [0],
      'Total repayment': [0],
      'Initial amount': [0],
      'Duration': [0]
  })
  url = 'https://simuladores.bancosantander.es/SantanderES/loansimulatorweb.aspx?por=webpublica&prv=publico&m=300&cta=1&ls=0#/t0'
  webdriver.get(url)

  Max_amount = 90.000
  Min_amount = 3.000
  for i in range(len(Amount)):
    Simulated_amount = Amount[i]
    if Simulated_amount > Max_amount:
      pass
    elif Simulated_amount < Min_amount:
      pass
    else :
      amount = WebDriverWait(webdriver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, "#amount")))
      amount.clear()
      amount.send_keys("{:.3f}".format(Simulated_amount))
      WebDriverWait(webdriver, 30).until(lambda webdriver: webdriver.execute_script('return jQuery.active') == 0)
      for j in range(len(Duration)):
        Simulated_duration = Duration[j]
        Simulated_duration = round(int(Simulated_duration))
        Max_duration = 96
        Min_duration = 12
        if Simulated_duration > Max_duration:
          pass
        elif Simulated_duration < Min_duration:
          pass
        else :
          term = WebDriverWait(webdriver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, "#term")))
        term.clear()
        term.send_keys("{}".format(Simulated_duration))
        term.send_keys(Keys.TAB)
        webdriver.save_screenshot('screenshot_santander.png')
        project.loc[j, 'Project'] = "reforma vivienda"
        project.loc[j, 'Initial amount'] = float("{:.3f}".format(Amount[i]).replace('.', ''))
        project.loc[j, 'Duration'] = Simulated_duration
        project.loc[j, 'Period'] = str(maintenant.day) + '/' + str(maintenant.month) + '/' + str(maintenant.year)
        project.loc[j, 'Monthly repayment'] = webdriver.find_element_by_css_selector('.r1 span').text.replace(' €', '').replace(',', '.')
        project.loc[j, 'TIN'] = float(webdriver.find_element_by_css_selector('.r3 span').text[6: 10].replace(',', '.'))
        project.loc[j, 'TAE'] = float(webdriver.find_element_by_css_selector('.r3 span').text[13: 17].replace(',', '.'))
        project.loc[j, 'Total repayment'] = float(webdriver.find_element_by_css_selector('.r7 span').text.replace(' €', '').replace('.', '').replace(',', '.'))
      Santander = Santander.append(project)
  Santander = Santander.loc[Santander.TIN != 0,: ]
  Santander.to_csv('Santander_{}.csv'.format(period), index = False)
print('End Scraping')

For run the code:

Amount = [13.000, 14.000, 15.000, 30.000, 45.000, 60.000]
Duration = [12, 15, 24, 36, 48, 60, 72, 84, 96]
hw_santander_scrape(Amount, Duration)

Answer 1

That data come from a XHR. So just use requests to post your values and parse the response with json.loads

Use your browser network tab to see what the request looks like.

Answer 2

This is my time to shine!

Information:

I'm currently working on a financial data aggregator that was facing this exact same problem.

It collects data from about a dozen websites and organizes it into a JSON object that is then used by a Flask site to display the data.

This data is scraped from websites that have several sub-directories with similar content which have different selectors.

As you can imagine, with a framework like selenium this becomes very complex so the only solution is to dumb-down it down.

Solution:

Simplicity is key, so I removed every dependency except for the BeautifulSoup and requests library.

Then I created three classes and a function for each filter ^[1]

from bs4 import BeautifulSoup

class GET:
  def text(soup, selector, index = 0):
    selected = soup.select(selector)
    if len(selected) > index:
      return selected[index].text.strip()

class Parse:
  def common(soup, selector):
    return GET.text(soup, selector, index = 5)

class Routes:
  def main(self):
    data = {}
    if self.is_dir_1:
      data["name"] = GET.text(self.soup, "div")
      data["title-data"] = Parse.common(self.soup, "p > div:nth-child(1)")
    elif self.is_dir_2:
      data["name"] = GET.text(self.soup, "p", index = 2)
      data["title-data"] = Parse.common(self.soup, "p > div:nth-child(5)")
    return data

def filter_name(url: str, response: str, filter_type: str):
  if hasattr(Routes, filter_type):
    return getattr(Routes, filter_type)(to_object({
      "is_dir_1": bool("/sub_dir_1/" in url),
      "is_dir_2": bool("/sub_dir_1/" in url),
      "soup": BeautifulSoup(html, "lxml")
    }))
  return {}

Using the requests library I made the request that got the data, then I passed the URL, response text and filter_type to the filter_name function.

Then in the filter_name function I used the filter_type argument to pass the "soup" to the target route function and select each element and get it's data there.

Then in the target route function, I used an if condition to determine the sub directory and assigned the text to a data object.

After all this is complete I returned the data object.

This method is very simple and has kept my code DRY, it even allows for optional key: value pairs.

Here is the code for the to_object helper class:

class to_object(object):
  def __init__(self, dictionary):
    self.__dict__ = dictionary

This converts dictionaries to objects so instead of having to always write:

self["soup"]

You would write:

self.soup

Fixing errors:

You really need to standardize the type of indentation you use because your script raises the following error:

Traceback (most recent call last):
  File "", line 84
    Amount =   [13.000, 14.000, 15.000, 30.000, 45.000, 60.000]
    ^
IndentationError: unindent does not match any outer indentation level

Notes:

1. filters are scripts that scrape different sites, my project requires that I scrape several sites to get the required data.
2. try to tidy your code more, tidy code is simpler to read and simpler to write

I hope this helps, good luck.