cloud cloud cloud cloud cloud
Python web scraping tools

Data is one of the driving forces in our world. Every aspect of our day-to-day life revolves around data. Without data, it is impossible to reach the technological growth we have today. Data is crucial for any organization, irrespective of sector. The most prominent organization has its data banks and data lakes. They will take the data and analyze it to get better insight. Sometimes, it is necessary to gather the data from outside, collecting it online. This situation is where the web scraping shines better. Many data science communities encourage ethical web scraping to pick different forms of data for various analyses. We will see about web scraping and the best python web scraping tools in the upcoming sections.

What Is Web Scraping?

In simple words, web scraping, also known as screen scraping, is extracting a large amount of data from various sources online. It is an automated process without human interactions. Most people are often misled about the actual process involved in web scraping. The web scraping process is extracting data from a targeted source and organizing the data. Whenever you perform screen scraping, the data is in an unstructured format, which means no labeled data. The web data extraction process also includes managing those unstructured data into structured data using a dataframe.

How Does Web Scraping Work?

There are various ways to carry out the web scraping process, such as creating an automated script from scratch or using an API tool for scraping websites, such as Twitter, Facebook, and Reddit. Some websites have dedicated APIs that allow scraping a limited amount of data, and some do not. In those scenarios, it is best to perform the web scraping process to extract the data from those websites.

Web scraping consists of two parts, a scraper, and a crawler. A scraper is a machine learning algorithm that helps identify the required data by following the links. A crawler is a tool used to extract data from the target. Users can modify both a scraper and a crawler.

Technically the process of web scraping starts by feeding the seed URL. These URLs act as the gateway to the data. The scraper follows these URLs until it gets to where it can access the HTML part of the websites. As mentioned, the crawler is a tool that goes through the HTML data and XML documents, scrapes the data, and outputs the result in a user-defined format, usually in an Excel spreadsheet or CSV (Comma-separated file) format. The other configuration is the JSON file. This JSON file is beneficial for automating the whole process instead of one-time scraping.

Different Types of Web Scrapers:

Based on the requirements, web scrapers can be differentiated into four types, namely:

  • Self-scripted web scraper.
  • Pre-scripted web scraper.
  • Browser extension.
  • Cloud-based web scraper.

Self-scripted web scraper – This type is based on creating your web scraper using any programming language you choose. The most popular one is python. For this approach, it is necessary to have advanced programming knowledge.

Pre-scripted web scraper –This type uses an already scripted web scraper. This can be downloaded online to start the web scraping process. Pre-build web scraper does allow you to tweak the option based on your requirements. Little to no programming knowledge is required.

Browser extension – Some web scraping APIs are available in the form of a browser extension (add-on). You just have to enable it with the default browser and mention the database location for saving the extracted data like an Excel spreadsheet or CSV file.

Cloud-based web scraper – There are very few cloud-based web scrapers. These web scrapers are run based on a cloud server maintained by the company from whom you purchased the web scraper. The main advantage is the computational resource. Web scraping is a demanding resource, so your computer can focus on other essential tasks with a cloud-based web scraper.

Best 6 Python Web Scraping Tools:

Python has widely considered the best beginners programming language due to its high user readability, which often helps beginners start their journey in the programming field. For the same reason, python is very much applicable to web scraping. There are six python web scraping libraries and tools we consider to be the best. NOTE: Some of these tools consist of python libraries with a specific function in the web scraping process

1. Request Library:

Probably the most basic and standard library in python. The first step in web scraping is “requesting” the HTML data from the target website’s server to retrieve the data. The request made to the request library is GET and POST. The two main disadvantages are, one, when it comes to the request library, it cannot be used efficiently when the targeted website is made of pure javascript, and two, the request library cannot be used for parsing HTML.

Here is the python code to install the requests library:

import requests
data =requests.request("GET", "https://www.example.com")
data

NOTE: You can import requests using only Juypter notebook or Google Collab. If you use CMD on Windows, Linux, or macOS, you can install requests using the pip method. The python code to install requests is “pip install requests.” The main thing to remember is that python does come with “urllib” and “urllib2.” Urllib can be used instead of a request, but the drawback is sometimes it is necessary to use both urllib and urllib2, which leads to the increased complexity of the programming script.

2. LXML Library:

This library is an updated version of the request library. The LXML library eliminates the drawback of the request library, which parses HTML. The LXML library can extract a large amount of data at a blazing fast speed with high performance and efficiency. Combining both requests and LXML is best for removing data from HTML.

3. BeautifulSoup Library:

BeautifulSoup is probably the go-to library for web scraping because it is easier for beginners and experts to work with. The main advantage of using BeautifulSoup is that you don’t have to worry about poorly designed HTML. Combining BeautifulSoup and request is also common in web scraping tools. The drawback is that it is slower compared to LXML. It is recommended to use BeautifulSoup along with the LXML parser. The python code to install BeautifulSoup is “pip install BeautifulSoup.”

4. Scrapy:

It is safe to call Scrapy the hero of web scraping. Scrapy is not a python library but a fully-fledged web scraping framework. In the backend, Scrapy consists of a bot capable of simultaneously sending multiple HTTP requests to the source. Even though Scrapy is a robust framework for web scraping, you can add plugins to increase its functionality. The main disadvantage of Scrapy is that it does not have the ability, like selenium (which we will see in the upcoming section), to handle javascript. Scrapy can overcome this drawback by using any library that can support data extraction from a dynamic website.

5. Selenium:

Selenium was created by Jason Huggins for automated testing of web applications. The drawback of Scrapy not being able to handle Javascript pages easily is where Selenium shines most. Since Selenium can scrape dynamic web pages, it is also best to scrape data from that web page. But it is recommended to use Selenium when working on small-scale projects, and when time is not of the essence. Since Selenium runs javascript on every page of the targeted source, it tends to be slow compared to other python libraries and frameworks.

6. Proxy:

A proxy is not an actual python tool, but it is necessary for web scraping. As mentioned above, web scraping needs to be carried out carefully, since some websites do not allow you to extract data from their web pages. If you do, they will most likely block your local IP address. To prevent that, a proxy is used to mask your IP address and makes you anonymous online. ProxyScrape provides high-quality residential proxies, which are best for high demanding tasks, such as web scraping. All residential proxies were rotating proxies and represented as standard IP addresses so that you can deeply mask your IP address. Also, the target website will have a hard time tracking your IP address.

FAQs:

1. Is Python suitable for web scraping?

Python is the best for web scraping because it is beginner-friendly, and you can process multiple requests to the websites to gather large amounts of data.

2. Is it legal to scrape data online?

It is legal to scrape all public data, but it is recommended to follow the web scraping guidelines before implementing screen scraping. You can do it by checking the targeted website’s robot.txt, sitemap file, and terms & conditions of the website itself.

3. Is HTML required to perform web scraping?

It is better to first master HTML before implementing web scraping. It will help you to extract the right amount of data. When you click on the inspect option on the web page, you will get the long tail script of HTML; basic knowledge of HTML will help you save time finding the correct data.

Conclusion:

Web scraping is an essential tool for any data scientist and analyst. With it, data scientists can get better insight into data and provide a better solution for the problems in the world today. This article hopes to give enough information on web scraping, libraries, and tools to aid their quest.

DISCLAIMER: This article is strictly for learning purposes. Without following the proper guidelines, performing web scraping may be illegal. This article does not support illicit scraping web in any shape or form.


Comments Off on Best Python Web Scraping Tools in 2022

Comments are closed.

Looking for help with our proxies or want to help? Here are your options:

Thanks to everyone for the amazing support!

© Copyright 2022 – Thib BV | Brugstraat 18 | 2812 Mechelen | VAT BE 0749 716 760