Everything You Should Know About Python in Web Scraping

In today’s highly competitive market, more and more companies are using web scraping to obtain enormous amounts of data from different online resources. Python, offering many libraries, frameworks, packages, and modules, has won the title of the best programming language for web scraping, with many programmers writing better web crawling scripts using it.

But why, out of so many programming languages available, has Python now been considered the best one for extracting data from the web? This post covers everything you should know about the Python language in web scraping.

What is Python

Released in 1991, Python is an interactive and high-level programming language designed to meet the needs of programmers, computer scientists, and engineers interested in coding. It is an object-oriented programming language that can be used to develop robust software applications and websites. Python has a wide range of modules and libraries, allowing it to support different programming languages like C, C++, Java, JSON, etc.

Features of Python

Below are some of the major features of the Python language:

  • Free and Open-source – Python is an open-source programming language and is entirely free to use, even for commercial purposes.
  • Easy to Learn and Use – Simple code and structure, a straightforward syntax, and a smooth learning curve make Python easy to learn and use.
  • Object-oriented Approach – Python supports procedure-oriented and object-oriented programming and the concepts of classes, object encapsulation, inheritance, polymorphism, etc.
  • Extensible and Embeddable – Python is extensible and embeddable. Programmers can use code from other languages in their Python code, as well as embed their Python code in other languages.
  • Cross-platform Language – Python is a portable language that can run on different platforms, such as Linux, Unix, Windows, Mac, etc.
  • GUI Programming Support – It offers various toolkits, such as wxPython, PyQt5, Tkinter, and JPython, which allows for GUI’s fast and effective development.
  • Automatic Garbage Collection and Memory Management – It handles garbage collection of all python objects and eliminates the need to allocate/free memory in the code.

Python for Building a Web Scraper

Python is one of the most popular and best programming languages for web scraping. It is an all-in-one product as it can handle nearly all processes related to data extraction smoothly. Here is why Python is a preferred language for building a web scraper:

  • Ease of use – Python is easy to code, which also applies to writing scraping scripts. Scripts written in this language are generally easy and quick to read and write, requiring only a few lines of code.
  • Diverse Libraries and Frameworks – Python carries a vast collection of libraries that helps with web scraping and further manipulation of extracted data. Requests, BeautifulSoup, and Scrapy, are its three most extensively-used frameworks. These are powerful libraries designed to develop high-performing web scrapers. It also offers Selenium for the automation of web scraping tasks.
  • Flexibility – Since it is a complete solution, Python web scraping can support data acquisition, parsing, and visualization, which would be challenging with other coding languages.

Advantages & Limitations of Using Python

This section sheds light on the advantages and complexities of Python that programmers may experience when using it.

Advantages

Following are the major advantages of using Python, especially for web scraping:

  • Automation – Repeating the process of scraping countless sites and platforms can be too hard. Python web scraping codes are created and executed only one time. Then, a web scraper automatically extracts data from target sources daily, thus, saving valuable effort and time and increasing the speed of data extraction. If you’re curious to know more about web scraping with Python, check out this article just released.
  • Combination – Web scrapers built using Python can be used to scrape and attach important data, parse, import, and save it as a data frame. Sometimes, it also visualizes the extracted data. 
  • Large Community Support – Since Python is the most commonly used programming language, it has a very active community. Programmers share their knowledge on different questions and concerns, helping beginners get proficient with this language.

Limitations

The language’s known challenges are not specifically related to web scraping but are more general in nature. Here are some of the complexities that its use may have:

  • Speed Limitations – Python is an interpreted and dynamically-typed language. The line-by-line execution of code usually leads to slow execution. So, it is not a preferred language where speed is the main point of the project.
  • Database Access – As compared to more extensively-used technologies like JDBC and ODBC, Python has weaker protocols in database access. For web scraping, Python may need to integrate an additional layer to store results in the database. So, it is rarely applied in large enterprises as a result.
  • Runtime Errors – Since it is a dynamically-typed language, the data type of a variable can change anytime and cause runtime errors. So, programmers need to perform detailed testing of the applications.

Conclusion

Python is used in a number of fields, such as web development, data science, machine learning, and, most importantly, web scraping. If you need to begin writing web scraping code, it is definitely worth learning Python. The best part is that Python, compared to other programming languages, is easy to learn, read, and code.

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