What if you need to get a lot of information from a certain site in a short time? In this situation, website scraping is the best solution. Web scraping can be used to search for prices and product details, compile market research, check competitors’ products and services, mine job postings and reviews, collect contact information, analyze competitor strategies, monitor news stories and more. When done professionally and ethically it is an invaluable tool that can save businesses time while other forms of data collection could become costly in both time and money. Through the use of Python’s scraping libraries such as BeautifulSoup, Selenium, and Requests, it is easy to build complex and customized programs for website scraping. This allows for quick gathering of structured or unstructured data from multiple sources in order to satisfy varied analytics requirements. In the link below I will show you how you can use Python

Many online stores focus on the assortment and prices of major online retailers, such as Amazon, Ebay, Aliexpress. Collecting this data manually is a tediously long and often pointless task. Because all the prices and assortments can change several times during the data collection process. That’s why usually all this data is just parsed. I was asked by a client to write a parser of products and their prices from Aliexpress.ru. Aliexpress is an online shopping experience that offers products from some of the world’s top brands and suppliers at competitive prices. It was founded in 2010, and has become one of China’s largest businesses. In addition to being a great place for consumers, Aliexpress also has a business side that allows wholesalers to browse through a range of products from more than 70 countries worldwide. With over 100 million active buyers and 8 million sellers, it’s not surprising that

Extracting information from websites is one of the most important skills in modern data science. Because it is the Internet today that is the key source of information for various studies. Parsing websites can be a tricky business, especially for those with limited technical knowledge. Trying to extract data from HTML and other web formats is no easy feat – you’ve got to figure out the structure of the site first and understand what bits need to be scooped up before you can actually do any parsing. And then there are things like javascript which can add additional layers of complexity. If you’re not careful, it’s easy to miss out on important information or accidentally parse in duplicate records. An additional difficulty in extracting information from websites is that all sites differ in structure, as well as in code and markup. And the bigger and older the website, the more