Python is a popular language and the go-to language when people are looking for how to extract data from websites quickly with simple tools they can build and develop by themselves.
This is because Python is easy to read, write and understand and comes with a wide array of libraries and frameworks that can be used to develop web scraping tools.
Web scraping is a process that allows brands to have access to any content on the internet to extract and analyze them to create business insights.
While the tools used for it can be developed using other languages, Python provides the easiest route hence its continuous popularity amongst developers.
Web Scraping and How It Relates To Python
Web scraping can best be explained as the process of sourcing and extracting large quantities of data across the internet.
Data is being generated and added to the internet every second, and the process of harvesting it varies.
It can be as simple as copying a web page and pasting it on your computer or as complex as using tools to extract millions of web pages at once.
The former is called manual data extraction and is quite inefficient. First, it is slow and tedious, and impossible to perform on a large scale.
Yet the data needed by businesses need to be collected on a large scale, which makes the latter, also known as web scraping, a very important process.
It is automated to collect enormous amounts of data daily for companies repeatedly. This way, the data can also be collected and applied in real-time, making it more valid and useful.
Python is widely known for its automation, which makes it a crucial concept in web scraping. Python can then develop these automated tools that help make web scraping an easier process.
However, this is not the only role Python plays in web scraping, as we will see in the next segment.
The data extracted can be used in several ways and according to what a business needs to achieve when the data is extracted. Below are some of the most common applications of web scraping:
Market Data Aggregation
Market Data Aggregation is necessary because the data on the internet is diverse, and this can lead to confusion.
A brand that wants to create some actionable intelligence needs to gather data from different sources through web scraping and aggregate it to make better sense of it.
Monitoring Consumer Sentiments
Amongst the many things that drive the market is consumer sentiment. This behavior depends on what they buy and when they buy it. Monitoring consumer sentiments is therefore necessary to perform better in the market.
All the information generated from buyers’ sentiments can be gathered from different parts of the web through web scraping.
Monitoring Competitor’s Prices
Prices are among the key things that drive sales. When prices are set too high, they scare off buyers to those who sell similar products at lower prices, and when they are set too low, they affect the company’s revenue and result in loss.
To balance this, you need to always watch how your competitors are selling. This will allow you to determine the best prices to set your products to win more customers and increase your profit margin.
The Role of Python in Web Scraping
It Saves Time
One of Python’s roles in web scraping is saving time. It does this in various ways. First, the tools built with Python are often done with very few codes that are easy to write.
This means you can save a lot of time building your web scraping tools using Python.
Then the tools developed with Python also work very quickly during the main process of web scraping, thereby ensuring you save more time.
It Encourages Automation
Running web scraping involves visiting millions of websites each day to collect important data from all of them.
Python ensures that the process is done with the best automation possible. Several Python libraries can be used to develop tools that work automatically to extract data.
It Supplies A Wide Array of Libraries
Another important role of Python is the supply of several libraries to select from. Libraries such as BeautifulSoup, Selenium, LXML, and Requests can be used to build different types of web scraping tools.
More importantly, the libraries are open-source and free for use. Python also has a large community that can readily provide support if you ever need one.
Some Best Practices Web Performing Web Scraping With Python
Web scraping is hard work but harder if you do not follow the best practices, especially when scraping with Python.
Some of the best practices include:
- Always build exactly what you need
- Check for robots.txt files on each website and be sure to follow the conditions
- Never use malicious bots to scrape data
- Always use different scraping patterns
- Schedule your scraping to happen at off-peak hours to prevent servers from crashing
Python programming language has several applications in so many areas, including web scraping.
Its importance in web scraping rises from the ease of the language, the option of automation, and the abundance of libraries all free for use.
So if you are searching for how to extract data from a website and are looking for a language to use in developing a web scraping tool, you may want to consider this language as many people are already using it. Go to this blog article if you wish to learn even more about how to extract data from a website.