now that we have the listthe final step it to iterate through this list of urls and extract each blog post' text the following function showcases how beautifulsoup simplifies the task as compared to our previous method of using regular expressions the method of identifying the required tag remains the samethough we utilize the power of this library to get text that is free from all html tags def get_post_content(content)"""this function extracts blog post content from response object argscontent (request content)string content returned from requests get returnsstrblog' content in plain text ""plain_text "text_soup beautifulsoup(content,"lxml"para_list text_soup find_all("div"{'class':'cms-richtext'}for in para_list[ ]plain_text + gettext(return plain_text the following output is the content from one of the posts pay attention to the cleaner text in this case as compared to our previous approach by mike halseyit was perfectly ordinary friday when the wannacry ransomware struck in may the malware spread around the world to more than countries in just matter of few hoursaffecting the national health service in the uktelecoms provider telefonica in spainand many other organisations and businesses in the usacanadachinajapanrussiaand right across europethe middle-eastand asia the malware was reported to have been stolen in an attack on the us national security agency (nsa)though the nsa denied thisand exploited vulnerabilities in the microsoft windows operating system microsoft had been aware of the vulnerabilities since early in the yearand had patched them back in march through these two methodswe crawled and extracted information related to blog posts from our web site of interest you are encouraged to experiment with other utilities from beautifulsoup along with other web sites for better understanding of coursedo read the robots txt and honor the rules set by the webmaster sql databases date back to the and represent large volume of data stored in relational form data available in the form of tables in databasesor to be more specificrelational databasescomprise of another format of structured data that we encounter when working on different use cases over the yearsthere have been various flavors of databases availablemost of them conforming to the sql standard the python ecosystem handles data from databases in two major ways the first and the most common way used while working on data science and related use cases is to access data using sql queries directly to access data using sql queriespowerful libraries like sqlalchemy and pyodbc provide convenient interfaces to connectextractand manipulate data from variety of relational databases like ms sql servermysql