Web Scraping with Python

Although the internet has a massive amount of data, not all are formatted in a way that we seek. Combining dispersed info into a more readable format requires scraping. If you are interested in profiting from a data scraping service, you may consider your possible options before you make a purchase. If you have some background, you may write code using Python and scrape data with it. There is a reason for this. Coding with Python is more effortless than other programming languages, and there are multiple libraries available to use with Python. There is a high chance that websites offering data scraping services are using Python. 

Defining Web Scraping

Websites are full of content—texts, videos, animations, pictures, music, and so on. There are cases when we need specific info. That may be the IP addresses of VPS, email address of the potential clients, an Instagram influencer with a particular number of followers. No matter what, collecting that data, mostly automatically, is the simplest definition of web scraping. Since the process is automated, it is done using some software.  Of course, you can do web scraping manually by copy and pasting for a small batch of information. However, collecting an immense amount of data requires an automated solution. This can be a software or even a browser extension. Highly skillful developers mostly create apps according to the customers’ needs, and therefore web scraping services and tools may vary. Such developed software can deliver you information as you wish and in a structured form. It would be helpful to visualize one specific case. Let’s assume that you are interested in building a website. For this, you need a hosting server. Different providers offer different prices for hosting services. If you can create, or maybe order, a web scraper service, then you will have what you need under your hand. You will get a file that has the name of the service that offers hosting, its URL, and prices for different plans. Some services may also provide domain names for free that are already included in the plans.

Python in Web Scraping

It is no secret that Python is one of the most preferred and practical programming languages. This is also the case with web scraping. If you are a savvy internet user, you can find many tutorials that show how to scrape data from web pages using Python. Since “Supply creates its own demand,” the more the users, the more additional tools are available for such purposes. In our case, as Python is popular among data scraping services. It has more libraries compared to other programming languages, like R. Libraries like “requests,” “Beautiful Soup 4”, “lmxl,” “Selenium,” “Scrapy,” and some others are available for the developers to use. Scraping with Python requires some research. In most cases, you have to load and analyze the page intended for scraping. This is mostly done using the Inspect function of the browsers. If you are familiar with tags, you can quickly get the info you need and organize them in file formats like XLS, JSON, TXT, or CSV. These file types are the most popular file types for such collected and categorized data.

Pros and Cons of using Python for Web Scraping

As mentioned above, Python has a rich choice of libraries. Not only that, but coding on Python is relatively simple compared to other programming languages. Here are some of the advantages of using Python for web scraping:

  • Most libraries don’t require too much CPU and RAM;
  • Possibility to develop custom solutions;
  • Built-in libraries for some cases;
  • Codes are shorter compared to other programming languages;
  • LXML is one of the best libraries for data parsing.

Naturally, there are some disadvantages of using Python as well.

  • For some reason, if you need a Selenium library, it is very slow, requires higher CPU and RAM usage;
  • Python is not ideal for simple jobs;
  • Some libraries or frameworks are harder than expected to install;
  • Beginners should avoid the LXML library.
Fabrizio VanMarcino