How to Use Python for Web Scraping
Every passing day, digital companies need to collect more data because it helps smoothen the processes and make growth easier to attain. However, businesses can differ according to their needs and what tools they can easily use.
For instance, while some businesses use Python, others prefer C# web scraping. This is mostly due to preference and how familiar they are with the language. But most digital brands use the Python programming language, and in this brief article, we will overview why this language is considered the best language for web scraping.
What is Data Collection?
Data collection can be defined as the extended process that governs everything from harvesting data from a target destination to analyzing the data and putting it into practical usage.
It generally begins by sending out a request using a proxy and a scraping tool. It culminates when the extracted data has been applied and used to make informed business decisions.
Businesses need data collection to enable them to always have the correct data at all times. It would be almost impractical and unrealistic to carry out businesses successfully today without this process.
What is The Importance of Data Collection?
The main principle of every data collection process is to increase the availability of meaningful use data.
Other important factor of data collection includes:
- For boosting brand protection and reputation management
- For observing and monitoring prices and competition across various markets
- For generating new prospects and leads that will eventually become paying customers
- For research, the market and consumer sentiments
What is The Role of Web Scraping in Data Collection?
Data collection is, hence, a very crucial process. However, it would be extremely tedious and almost impossible without a process like web scraping.
For instance, traditionally, data collection involves:
- Acquiring a target URL.
- Sending out requests.
- Interacting with the server.
- Manually copy its content before pasting it on your computer.
Then this is repeated for the following web page and the next URL, and so on. However, web scraping automates the entire process, making it faster, easier, and more reliable.
Below are the roles of web scraping in the data collection process:
It Brings Automation
Web scraping makes data extraction automatic. Using web scraping, you can interact with any target server repeatedly and automatically harvest their content on a large scale.
The tools work intelligently to follow each embedded URL and collect data from all the web pages at once—all these help reduce the effort and time required to manage a large amount of data.
It Saves Valuable Time
Time is always vital in business, and while data collection is important, it is always better if it can be done as quickly as possible.
Web scraping introduces time-saving to the concept of data extraction by using tools that work with incredible speed and makes the process automatic.
Aside from ensuring you get the data you need sooner, this also means you will be getting user data in real-time, thereby increasing the accuracy of the data.
It Increases Data Accuracy
It is better not to use any data than to use inaccurate data. This is because decisions based on erroneous data can cause a brand loss of customers, revenue and even damage its reputation.
Web scraping prevents this by ensuring that the data collected is of the highest accuracy and contains zero errors.
The Role of Python Programming Language in Web Scraping
There are several roles that the Python programming language can play in web scraping but below are two of the most important:
Scraping for data can quickly become both a mundane and back-breaking process. This is why many eCommerce brands shy away from collecting data calling the process hectic and complicated.
However, with Python and its many frameworks and libraries, web scraping can be done smoothly and automatically.
Python has libraries and a framework for just about every web scraping process, from interacting with the target destination to harvesting and parsing the data.
And they all work in an automated manner to save both time and energy.
Another role that Python plays in web scraping is that it successfully combines all required to collect data into one huge system.
Python can be used to build tools that engage with unstructured data, extract them, append them, parse, import, and save them in a local storage system all at once.
It is also possible regardless of the amount of data involved. Not many programming languages can boast of this capacity.
Overview of Various Python Libraries and Their Capabilities
Several Python libraries can be used for different aspects of web scraping, and below are 5 top Python libraries and the capacities that they each serve:
Every web scraping exercise must begin with sending out a request to the target URL, which is why this Python library is essential.
As the name implies, the Request library sends out HTML requests such as GET and POST.
It is simple and easy to use with other advantages, including basic authentication, international URLs and domains, and proxy support.
The Request library usually cannot parse the data it helps retrieve, making it necessary to combine it with other libraries such as LXML.
The LXML library is a fast high performance and high-production quality library that parses HTML and XML files.
It is a hybrid of the Element trees and Python, combining the former’s power and speed with the latter’s simplicity.
This is a more general tool that can be used separately or combined with parsers such as LXML.
It is now very popular for this precise reason. It makes it even more attractive for web scraping because it is robust, requires only a few lines of code, and can be easily used by beginners.
The libraries mentioned above are only great at interacting and extracting from static websites and often struggle when the web pages are dynamic.
And this is where the Selenium library comes in.
Aside from this, it is also beginner-friendly and works automatically similar to how a human would scrape content.
Scrapy is the go-to web scraping solution when you are looking for a full-fledged tool that does all.
It is not just a library but functions as a framework as well. You can easily use this tool to crawl multiple websites and extract data, then parse them.
And the best part is Scrapy is asynchronous, making requests to multiple servers at once.
Examples of Other Programming Languages and How They Compare Against Python
Aside from Python, you may also choose C# web scraping or use other popular programming languages such as Node.JS, Ruby, C and C++, and PHP.
However, compared to these other languages, Python has the following advantages:
- Python introduces diversity with a broad range of libraries and frameworks that can handle any aspect of web scraping
- It is easy to write and simple to use
- Python has the simplest and most easy to understand syntaxes
- Tools built with Python are the most flexible, handling data collection, appending, parsing, importation, and visualization.
- You can easily perform hue tasks with small codes written and executed once yet used repeatedly and automatically.
Python’s popularity goes a long way to show why it is the best option for web scraping.
Python is diverse, flexible, and versatile. The tools built with this language boast high performance despite minimal coding requirements.