Web scraping in USA has become an essential tool for businesses for various reasons, including lead generation, marketing and advertising, competitor analytics, market research, etc. There are many third-party web scraping tools available in the market. If you could not find any tools aligned with your requirements, you can partner with web scraping service providers like Relu consultancy to design your exclusive tool.
Or there are ways in which you can try scraping simply with the basic coding knowledge. Python, being an object-oriented language, is the most easiest way to get started with web scraping. Python’s classes, objects, and libraries make it significantly easy to scrape data.
In this article, you will learn how to extract data in large amounts using Python with a detailed demonstration.
Is web scraping using Python legal?
Web scraping as a process is not illegal. But it depends on what data is being extracted from websites. There will be no issue if you extract open-source data or data available for crawlers to visit through. Every website has its rules and regulations that allow and regulate web scraping, which you can find in “robots.txt.” If you abide by the guidelines given by the website, there won’t be any legal trouble.
Why Python for Web Scraping?
Web scraping is extracting a large amount of data from websites. An automated tool that will crawl through the website, collect the required data and organise them in a structured database. There are many ways to scrape data from websites, such as online scraper tools, predesigned scraper software, APIs or writing your own code.
Web scraping using Python is the most famous way of scraping data. Here is the list of Python’s features that make it the best coding language for scraping –
- Ease of Use – Python, is very easy to code. There is no need for semi-colons or braces, which makes it very simple, quick, less messy and easy to use.
- Large collections of libraries – one of the main features of libraries that make Python suitable for web scraping is that Python has a huge collection of libraries such as Matplotlib, Pandas, Numpy etc.
- Easily Understandable syntax – Since Python doesn’t have the semi-colon or braces, reading it is similar to reading a statement in English and is easily understandable. The indentation used in Python differentiates between the different blocks in the code.
- Simple coding– One of the main advantages of using the web scraping technique is to save time in collecting data manually. But if you have to spend time on coding, it will be of no good. But Python allows you to extract data using short and simple codes. So, even while developing code, you save time.
- Dynamically types – Unlike other code languages, In Python, you don’t have to define data types for variables. Instead, use it directly whenever necessary.
How to scrape data from a website?
When you run the web scraping code, a request is made to the URL you specified, or a crawler will be activated in the bot to find the data among the websites. The server transmits the information in response to the request, enabling you to see the HTML or XML page. After parsing the HTML or XML page, the code extracts the data.
So the basic steps involved in scraping web data are –
- Input the URL that you want to scrape or allow a bot to crawl through the websites to find the data
- Inspect and read the page
- Discover the data you want to extract
- Write the code
- Run and extract the data
- Stored the data in a defined structured format.
Here is an example of scraping data using Python.
The first step in web scraping is sending HTTP queries, such as POST or GET, to a website’s server and waiting for the server to respond with the required data.
With simple, few lines of code, the Requests library streamlines the process of sending such requests, improving readability and debugging without sacrificing efficacy. Using the pip command, the library can be installed directly from the terminal:
Requests library provides easy methods for sending HTTP GET and POST requests.
If a form needs to be submitted, it can be done using the post() method. The form data can be as a –
Requests library also makes it very easy to use proxies that require authentication.
In a similar way, data can be scraped using various libraries of Python. Some of the other commonly used libraries are
- Selenium – it is a web testing library used to automate browser activities.
- Beautifulsoup – is used for parsing the HTML and XML documents that help extract the data.
- Pandas – are used for data manipulation and analysis that helps extract and store data sets in the desired format.
Though this is a very simple and basic example of using Python for web scraping, the coding can be enhanced according to serious needs. Python is one of the simplest languages to learn because it is object-oriented. Classes and objects in Python are simpler to use than in any other language. Furthermore, numerous libraries available in Python make web scraping simple and easy.
Relu consultancy provides the best web scraping services in USA. We have a vibrant team of data engineers and scientists, who will understand your needs and build the best and most accurate data scraping solution that can accelerate your business growth in a brief period.