Web scraping being the all-important strategy that it needs all the best and most powerful tools in the market. The best tools comprising frameworks and libraries do not only help to ensure the data extraction process is made easy but also help in making data parsing seamless as well.
Among the many programming languages used in writing and developing scrapers, Python has witnessed the widest application. There are several reasons why this is so, and we will look at them shortly.
Currently, Python libraries offer the easiest form of web scraping, so it is common to hear of Selenium web scraping, BeautifulSoup web scraping, and so forth. All of which are commonly used for web scraping for different reasons.
What is web scraping?
Web scraping is defined as a technique used in gathering both structured and unstructured data from multiple sources, including websites, key marketplaces, social media platforms etc.
The process is usually automated to remove the burden associated with performing repetitive actions. It involves accessing and interacting with the source of the data, retrieving the relevant public data, then parsing it back to the client’s device before it is transformed and stored in a readable format.
The extracted data can then be analyzed and used for making critical business decisions. Some of the other uses of web scraping include:
- For monitoring price and competition
- For conducting market and price analysis
- For generating marketing leads
- For collecting relevant data that can be used for training and testing Machine Learning tools
- For important research that can facilitate business intelligence
Why Python is widely used for web scraping
There are many reasons why Python is widely used for web scraping. The first reason is that Python is highly diverse, with several libraries and frameworks that can work individually or collectively to yield excellent results.
Secondly, Python is both easy to learn and use, with even beginners writing a decent web scraping script requiring only very few lines of codes to prepare.
Another reason why people prefer to use Python for web scraping generally has to do with the robustness of the language, as libraries built with Python can be used to scrape both dynamic and static contents from different websites. Also, Python boasts of a very helpful and supportive online community so that no one is ever left to deal with a problem by themselves.
Most common Python libraries used for web scraping
The sheer number of Python libraries available for web scraping can be staggering, yet many people prefer to use the most common three: BeautifulSoup, Requests, and Selenium web scraping.
BeautifulSoup is the most popular Python library because it is the easiest to use and is well-suited for beginners. People use it mainly because of its ability to parse data in any format even though it performs this function slower than other data parsers (e.g. lxml).
BeautifulSoup can also easily and automatically detect encodings which makes it easier to handle even poorly written HTML codes.
This library also has one of the easiest and quickest ways of data navigation and parsing, meaning that data scraping can be done quickly and painlessly.
Requests is not a standard Python library (it needs to be downloaded and installed), but it is easy to use, so it is recommended for web scraping beginners. Usually, standard Python HTTP libraries are difficult to use and often require more statements to do the same thing. The goal of this library is to make HTTP requests simpler and more human-friendly.
The great thing about this library is how Selenium scraping can be done on websites with dynamic and static content. Where other Python libraries only extract static contents and fail at collecting dynamic contents, Selenium excels and does the job perfectly.
Also known as a web driver, it can automatically and easily interact with websites and other sources, collecting data as it does so.
Also, Selenium can be easily integrated with any data parser to make web scraping a less daunting task.
Web scraping is a task that can prove challenging to anyone. Yet, it is essential if a business must have enough data to use to drive growth.
Luckily tools such as Python libraries and frameworks can help take away the many burdens associated with web scraping.
And it is usually the diversity of these tools that prompt many people into preferring to use Python for web scraping. Since web scraping is a two-sided process, some of the libraries can handle the data extraction while others handle the data parsing.