Get Google Search Screen Information with Python Scraping

import requests
import bs4
from time import sleep
import pandas aspd
from google.colab import files
import re

# enter the keyword one wants to search for
listKeyword=['Dog', 'Cat']

# change the number to match the number of items one wants to acquire
searchNum=str(2)

response=requests.get('https://www.google.co.jp/search?num='+searchNum+'&q='+'.join(listKeyword))
response.raise_for_status()

# parse the HTML obtained from
soup=bs4.BeautifulSoup(response.content, "html.parser")

file_prefix=""
for word in listKeyword:
  if(file_prefix==""):
    file_prefix+=str(word)
  else:
    file_prefix+="-"+str(word)

fileName=file_prefix+'_Top'+searchNum+'.csv'

# Set the header for the csv file
df = pd.DataFrame (columns = ['URL', 'Title', 'Description', 'metakey'])

sleepCounter = 0

# retrieve the URL of the top site of the search results
for a in group.select('div#searchh3.ra'):
  sleepCounter+=1
  url=re.sub(r'/url\?q=|&sa.*',',', a.get('href')))

  try:
    # load the retrieved URL
    search=requests.get(url)
    searchSoup=bs4.BeautifulSoup(search.content, "html.parser")

    # Capturing Title
    titleList=[ ]
    for a in searchSoup.select('title'):
      titleList.append(a.text)     
    title='"

    for index, item in enumerate (titleList):
      if index == 0:
        title=item
      else:
        title=title+', '+item

    # Retrieving Descriptions
    descriptionList = [ ]
    for a in searchSoup.select('meta[name="description"]'):
      descriptionList.append(a.get('content')))
    description='No data'

    for index, item in enumerate (descriptionList):
      if index == 0:
        description=item
      else:
        description=description+', '+item

    # Obtaining Keywords
    keywordList = [ ]
    for a in searchSoup.select('meta[name="keywords"]'):
      keywordList.append(a.get('content')))     
    keywords = 'No data'

    for index, item in enumerate (keywordList):
      if index == 0:
        keywords=item
      else:
        keywords=keyword+', '+item

  except:# exception handling:what to do when a site cannot be loaded
    print('Failed to read website.')

  # Add retrieved URLs, titles, descriptions, and keywords
  outputRow = [url, title, description, keywords ]
  s=pd.Series(outputRow, index=['URL', 'Title', 'Description', 'metakey']) 
  df=df.append(s,ignore_index=True)

  # Allow 10 seconds to wait for every 10 searches to meet the limit on the number of requests per second
  if sleepCounter>10:
    sleep(10)
    sleepCounter = 0

# output to csv
df.to_csv(fileName, index=False)

# Download csv
files.download(fileName)