Data is one of the driving forces in our world. Every aspect of our day-to-day life revolves around data. Without data, reaching the technological growth we have today is impossible. 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 a better insight. Sometimes, collecting the data from outside is necessary, 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. In the upcoming sections, we will discuss web scraping and the best python web scraping tools.
Feel free to jump to any section to learn more about python web scraping tools!
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. The data is in an unstructured format whenever you perform screen scraping, meaning no labelled data. The web data extraction process also includes managing those unstructured data into structured data using a dataframe.
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, performing the web scraping process is best 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.
Based on the requirements, web scrapers can be differentiated into four types, namely:
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 as 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. With a cloud-based web scraper, web scraping is a demanding resource, so your computer can focus on other essential tasks.
Python has widely considered the best beginner’s 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
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.
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 fast speed with high performance and efficiency. Combining both requests and LXML is best for removing data from HTML.
BeautifulSoup is probably the go-to library as one of the python web scraping tools 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.”
It is true that websites are changing rapidly and becoming more complex. Web scraping at a larger scale is becoming more difficult, like scraping data from an eCommerce website.
But import.io has a solution. With cutting-edge technology in web scraping, you can scrape multiple websites at the same time without lag. The best thing about import.io is that it is a tool that can automatically check data being scraped and do QA audits at regular intervals.
This feature can be used to avoid any null and duplicate values being scraped. You can scrape various data types, such as product details, rankings, reviews, Q&A, and product availability.
If you are a social media marketer, datastreamer is the best tool for you to scrape a large number of public data from social media websites. With DataStreamer, you can integrate unstructured data with a single API.
With DataStreamer, you can feed your data pipeline with over 56,000 pieces of content and 10,000 enrichment per second. Customize your data by filtering and aggregating it based on the questions you want to be answered.
A proxy is not an actual python tool but 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 masks your IP address and makes you anonymous online.
ProxyScrape is one of the best and most reliable proxy providers online. With three proxy services, such as dedicated datacenter proxies, residential proxies, and premium proxies. So, which is the best proxy service for web scraping? The answer is residential proxies.
Residential proxies have IP addresses resembling ISP’s IP address, which can be helpful to access data easily from the Ftarget server. The second important feature is the rotating feature. A rotating proxy is a proxy that helps to dynamically change your IP address at a fixed period of time, which can help you to deeply mask your original IP address and also make it difficult for the target server to check whether you are using a proxy or not.
Other features of residential proxies are unlimited bandwidth with concurrent connections, username and password authentication, backconnect rotating proxies, HTTP/HTTPS connections, and the ability to select the desired country servers for your work.
Python is the best for web scraping because it is beginner-friendly, and you can process multiple website requests to gather large amounts of data.
It is legal to scrape all public data, but following the web scraping guidelines is recommended 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.
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.
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. If you are a SEO/digital marketer then Python web scraping tools are a must. Python web scraping tools help you to save a lot of time and easily gather you much needed data without any difficulties. This article hopes to give enough information on the “Top 8 best Python web scraping tools”
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.