Web scraping refers to the process of extracting data from websites. It covers simple, manual methods such as copying and pasting or more sophisticated approaches that infuse automation. The latter greatly enhances the speed of data retrieval and even goes ahead to parse data (convert the HTML code file) into a human-readable format that is then stored as a .csv or Spreadsheet file.
When creating automated web scraping tools, the Python programming language is preferred. In this article, we explore 3 reasons why Python is the best choice for web scraping.
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Automated Web Scraping
Businesses all over the world have embraced automated web scraping as a way of extracting vital data that, when analyzed, provides insights instrumental to their survival and success. Notably, the information collected helps in competitor monitoring, reputation tracking, lead generation, price monitoring, and more.
With more and more companies striving to have an online presence, the volume of useful data uploaded on their websites as well as third-party sites, such as e-commerce platforms, grows by the day. This is where automated web scraping comes in, as it can be used to extract large volumes of data efficiently and speedily.
About Python
Conceptualized in the 1980s and subsequently released in 1991 by its initial designer Guido van Rossum, Python has proved to be a vital programming language. So much so that it has been used to create popular platforms such as Spotify, Google, Netflix, and more. However, it is worth noting that Python’s usability extends beyond these major sites as it can also be used for other applications.
This attribute is mainly due to the fact that Python is a general-purpose language, meaning programmers can use it across multiple application domains. In this regard, Python greatly differs from languages such as HTML, SQL, CSS, and more, which are collectively referred to as domain-specific languages.
Applications of Python
You can use Python for the following applications:
- Data analysis and visualization
- Artificial intelligence (AI) and machine learning
- Game development
- Software development
- Web development
- Creating graphical user interfaces (GUIs) and application programming interfaces (APIs)
- Web scraping
Programmers prefer Python for the following broad reasons:
- Python is easy to use: coding using Python is simple as it does not require the usage of curly braces or semicolons.
- The language is easy to understand as its syntax mimics the English language: a Python code reads like a statement written in English.
- Libraries: Python has an extensive assortment of libraries, meaning programmers do not have to create their code from scratch.
- A small Python code can be used to undertake large tasks, with a prime example being Python web scraping – the tools created using a few lines of Python code can extract data from hundreds of websites.
Thanks to the easy-to-understand syntax, beginners need not have extensive coding knowledge. That said, why is Python the best choice for web scraping applications? Here are three reasons.
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Reasons Why Python is the Best Choice for Web Scraping
Notably, while the broad reasons listed above also apply to web scraping, there are still more specific reasons that influence Python web scraping. These include:
- Extensive Python web scraping libraries
- Ability to be used for data analysis applications
- Easy proxy integration
Python Web Scraping Libraries
There are plenty of web scraping-oriented libraries such as Beautiful Soup, Requests, lxml, and Selenium. Notably, you have to combine code from multiple libraries for effective Python web scraping. For instance, the Requests library can be used in concert with Beautiful Soup or lxml, which are parsing libraries.
Alternatively, you can use Scrapy, a Python framework, which itself is a complete web scraping solution.
Easy Proxy Integration
Proxies are increasingly becoming a necessity during web scraping. This is especially so given the increased use of anti-scraping techniques built into websites. Proxies ensure that each IP address is limited to only a few requests, in effect mimicking human browsing behavior.
Notably, the Python Request library comprises a tool that lets you easily add a proxy to your scraper. You can use it to send requests, follow links, download HTML code files, parse the data therein, and avail it for download in a structured format.
Data Analysis
You can extend Python’s utility beyond simply creating a web scraper. As a general-purpose language that is also used in data analysis and visualization, you can create a Python web scraper as well as a data analytics tool to help you make sense of the collected data. This will be particularly helpful in cases where you have undertaken large-scale Python web scraping.
Conclusion
Python is a versatile programming language whose utility and extensive libraries make it ideal for creating web scraping solutions. In addition, you can use it to develop data analytics tools, which will help you identify trends or make sense of the large volumes of data extracted from websites.
