Data and the subsequent analysis of the data collected help businesses gain a competitive advantage. This is mainly achieved by leveraging the insights generated to improve product offerings, pricing strategy, customer service, marketing, and more. So crucial has data become that segments such as big data analytics have emerged and grown tremendously. Valued at $7.6 billion in 2011, the big data market is expected to reach a value of $103 billion by 2027.
Notably, there are several methods of collecting data, one of which focuses on gathering the information available via websites. This type of data collection is known as web scraping. It can be conducted manually or automatically. The former is ideal for small-scale applications. On the other hand, the latter is reserved for situations that require agility as well as efficiency and is conducted using web scrapers, some of which you can create using the Python programming language, i.e., Python web scraping.
What is Python?
Python is a programming language first released in the early 1990s. Since then, it has been revamped and improved – new versions are released periodically. Still and even with the updates, Python still offers the same advantages that have endeared it to programmers and developers worldwide. In fact, this general-purpose language commands the largest market share, meaning it is the most popular programming language today.
The reasons for its market share include:
- Python is a general-purpose language: it can be used to build applications and software in various domains.
- Ease of use: Python does not require a programmer to use semicolons for termination purposes (it signifies separation) or curly brackets; as such, there are fewer rules than other languages.
- Libraries: there is an extensive collection of pre-written code/script, collectively known as Python libraries, which cover virtually every domain, eliminating the need for coders to create code from scratch.
- Python code is easy to understand: this programming language uses a syntax that mimics the English language.
Given Python’s ease of use and its understandability attribute, this programming language appeals to beginners and seasoned developers alike. Together, these reasons and a few others detailed below make Python an equally appealing and useful language for creating web scraping tools.
Python Web Scraping
Other factors that also determine Python’s utility in web data extraction applications include:
- Python web scraping libraries
- Python’s status as a general-purpose language
- Ability to easily integrate/add proxy serves
Python Web Scraping Libraries
Web scraping greatly benefits from the fact that Python, through its rigorously tested and optimized libraries, promotes the writing of high-quality and efficient code. In the coding world, efficiency refers to the quality of the code to be fast, reliable, and use low resources once executed. It also defines the programming methodology, i.e., the steps taken to write code from the problem statement and writing the code to implement.
As stated, automated web scraping is mainly used in large-scale web data harvesting applications or cases where efficiency is a crucial requirement. Python web scraping relies on optimized and tested web data harvesting libraries such as Selenium, Requests, Beautiful Soup, lxml, and Spatula (a new library). Alternatively, you can also use Scrapy, a Python framework that is equally optimized, tested, and proven. So, given Python’s library-driven efficiency, it is no wonder the language is preferred during web scraping. To get started with Python, check out this blog post.
Python as a General-Purpose Language
As highlighted earlier, programmers can use it to create applications and tools in multiple domains. For instance, Python code is employed in web development, artificial intelligence (AI) and machine learning (ML), data analysis, software development, and more.
When it comes to web scraping, analyzing the collected data is equally crucial if businesses are to make sense of the vast pool of structured data already gathered. Given Python is used in data analysis and visualization, you can go a step further by creating tools to analyze and visualize the data extracted from websites through your tailor-made Python web scraping solution.
Web developers frown at web scraping, especially because it involves making numerous requests, which could overload servers. As a result, they design the websites with in-built anti-scraping operations such as IP blocking, user agents, CAPTCHAs, honeypot traps, and more. These techniques help the web servers automatically identify bots as well as block them from wreaking havoc.
However, you can bypass these techniques by using proxy servers. Proxies are intermediate servers that assign your outgoing requests a unique IP that anonymizes your browsing and scraping operations. Notably, you can use the Python requests library, which contains guidelines on how to add proxies.
In sum, do you need Python web scraping? Based on the many factors discussed herein, the answer is in the affirmative. This is because it will help you improve various aspects of your business.
Python web scraping is an efficient, fast, and reliable way of gathering data about your customers and competitors. It helps businesses gain a competitive edge and guides marketing and pricing strategies, review monitoring, market research, and more.