Web scraping gets more common and popular every day. It is a tool that is useful in every industry. This is because by using APIs, you can easily scrape, collect and parse important unstructured raw data. As technology becomes more advanced and more innovative, and as more businesses enter the entrepreneurial space, it is especially useful for making new ideas a reality.
In this article, we’ll briefly talk about web scraping, when it should be used, and the pre-requisites and action items you need to know to get started web scraping with Python. Another great API to pair with web scraping is ip geolocation.
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What is web scraping used for?
We all know that web scraping is the process of extracting a large amount of unstructured data stored on a website. But why do we need it? The answer is very simple. Web scraping lets you automatically pull information off the web that would be impractical and inefficient to collect manually.
There is a multitude of reasons for web scraping. For example, you may want to design an application that uses information from other websites before you execute your custom logic on top of it.
This is especially beneficial when your source web products don’t have publically exposed APIs that allow you to access their information in a structured form. In cases like this, web scraping is the only way to pull raw information and structure or data process it yourself.
Why is Python the ideal choice for web scraping?
Python is one of the few programming languages that is very intuitive, easy to understand, and provides plenty of pre-built functions. Another advantage of web scraping with Python is that its pre-built functionality makes it quicker and easier to write scraping scripts. Python code is simple, easy to understand, and more importantly, easy to maintain and extend.
Because Python is one of the most popular and in-demand programming languages, there are also a lot of open-source libraries and wrappers that you can use to simplify and optimize the script you are trying to develop.
Web scraping with Python also lets you apply data analytics to your scraped data. Essentially, Python extends your application scope to cover a multitude of dimensions.
What are some of the powerful libraries for web scraping with Python?
Here are some of the most powerful and frequently used libraries for web scraping with Python.
What are some of the powerful libraries for web scraping with Python?
Now that you understand what web scraping is and why it is necessary, let’s take a look at the advantages and benefits of web scraping with Python. Here is a short practical example:
Say you want to scrape a product list of t-shirts from Flipkart. Here’s how to do it in Python.
Simply, import the urllib library, and read from the apilayer URL.
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from urllib.request import urlopen url = "flipkart.com/clothing-and-accessories/topwear/tshirt/men-tshirt/pr?sid=clo,ash,ank,edy&otracker=categorytree&otracker=nmenu_sub_Men_0_T-Shirts" page = urlopen(url) html_bytes = page.read() html = html_bytes.decode("utf-8") |
Now, if you print the HTML returned by the urlopen function, you’ll see something that starts with the following.
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<!doctype html><html lang="en"><head><link href="https://rukminim1.flixcart.com" rel="preconnect"/><link rel="stylesheet" href="//static-assets-web.flixcart.com/www/linchpin/fk-cp-zion/css/app.chunk.7570a9.css"/><meta http-equiv="Content-type" content="text/html; charset=utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=Edge"/><meta property="fb:page_id" content="102988293558"/><meta property="fb:admins" content="658873552,624500995,100000233612389"/><meta name="robots" content="noodp"/><link rel="shortcut icon" href="https://static-assets-web.flixcart.com/www/promos/new/20150528-140547-favicon-retina.ico"/><link type="application/opensearchdescription+xml" rel="search" href="/osdd.xml?v=2"/><meta property="og:type" content="website"/><meta name="og_site_name ... |
Once you have the scraped data, you can use libraries such as Beautiful Soup, and Pandas to process it and save it in a structured format.
As you can see, web scraping is a powerful technique for providing data to applications dependant on third-party resources for updates. Web scraping with Python simplifies the extraction process. This is because Python’s intuitive syntax and pre-built libraries make the scraping process smoother and faster.
apilayer is an initiative that truly understands and believes in simplifying the developer experience. It uses forward-thinking development processes within its own APIs as well as providing design and development guidelines for custom services that use it as a third-party service.
Within the apilayer family, scrapestack is a real-time and scalable web scraping REST API that makes the scraping process even smoother with just one API call and you are only left with the post-processing of scraped data.
Head over to apilayer and get inspired by the APIs for their clean design and architecture.
