Throughout each iteration, one will be added to i, giving us a new URL each time. Soup = BeautifulSoup(res.data, 'html.parser')Ĭontents = soup.find_all(class_= 'product_pod')īecause there are 50 pages, our range will need to be from 1 to 51 in order to capture all of them. #All of the page URLs follow the same format with the exception of one number followed by 'page-' Then, by wrapping our calculations in the round method, we can round the number to the hundredths place. By defining this function, we are able to call it later when the time comes. Return f'$ 'Īs of August 1st, the conversion rate from British Pounds to US dollars is 1.21255. #Convert British Pounds to USD (as of 20190801) Next, we define our function as get_book_data and pass in the argument filename that we will choose for our CSV. Finally, since we want to write our information to a CSV via a dictionary, it only makes sense to use the CSV module. One of the easiest ways to remove and replace the symbol is through Regular Expressions. A prime example of this is that all of the prices on the page have a pound symbol in front of the numbers. Throughout most of your Web Scraping, there will be a time that Regex typically comes in handy. For more information, check out the docs. It's pretty versatile and perfect for what we need. #These will be our Master Lists and must remain outside of any loops #The file name will be whatever you decide when running the function Let's begin!įirst, let's import our modules and define our function. Since we'll be putting everything into a function, be mindful of your indentations. Now that we have our outline, we can get to work. Convert both master lists into a single dictionary.Collect every book price from the page, convert to USD, and append to the prices master list.Collect every book title from the page, and append it to one of the master lists.Using Urllib3 and Beautiful Soup, set up the environment to parse the first page.Import the required modules and create two master lists (titles and prices).The URL for this page changes one number each time, so a simple for loop should do the trick. Therefore, our script will have to iterate 50 times, while altering the base URL each time.
If we scroll to the bottom of the page, we notice that there are 50 pages worth of books. We notice that the prices are in British Pounds, so we'll want to convert them into US Dollars. For now, just know that it means to space out the amount of time between your individual scrapes.)īasically, we want a list of every book title and price from this website. However, on other websites, this may be a good idea, since they will most likely block you if you're not "polite." (I'll talk more on the concept of being polite in later posts. Before we begin, please understand that we won't be rotating our IP Addresses or User Agents. It's one of those websites that is literally made for practicing WebScraping.
The website that we will be working with is called. Today, using Python, Beautiful Soup, and Urllib3, we will do a little WebScraping and even scratch the surface of data extraction to an excel document.
#Webscraper package python code
Work that would take hours to complete can be accomplished with just over 50 lines of code and run in under a minute. Fortunately, for those users, there are programmers with the ability to develop scripts that will do the sorting, organizing, and extracting of this data for them. The problem, however, is because of the abundance of information we as the users become overwhelmed. Through the internet, we have an unlimited amount of information and data at our disposal.
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