Python is a popular high-level, general-purpose programming language utilized to create myriad tools and solutions, including web scrapers. In fact, Python is the fourth most preferred language by experienced developers and learners. This popularity stems from a number of factors, such as the language’s simplicity (as regards the ease of use), scalability, and an extensive repository of pre-written code (libraries), just to mention a few.

While Python is considered easy to use and learn, mainly due to its syntax and semantics, there are tips you could utilize that simplify the process even further. Therefore, this article will focus on the essential tips for web scraping with Python. 

What is Web Scraping?

Web scraping, also known as web data extraction or web harvesting, refers to the process of collecting data from websites, either manually or automatically. It is worth pointing out that the use of the term “web scraping'' often refers to the automated form of data collection. Automated web data extraction is undertaken using bots known as web scrapers. These bots handle everything, from sending HTTP or HTTPS requests to websites and parsing the data (converting it into a structured format) to storing it in a file for download.

Python in Web Scraping

Given the convenience of the bots, you might be wondering how you can access the web scrapers. If you do not have a technical/programming background, you will be pleased to hear that you can purchase or subscribe to an off-the-shelf web scraper. Created and maintained by a company whose primary focus is on such bots, off-the-shelf web scrapers offer convenience and advanced features that can only result from a collaborative team of developers.

That said, in the event, you have an extensive technical background and are open to dedicating some of your time and resources, you could consider creating a web scraper from scratch using Python. And if this option appeals to you, it is worth pointing out that you could benefit from the knowledge of several key tips for web scraping with Python.

Tips for Web Scraping with Python

You can utilize the following vital tips when web scraping:

1. Utilize Python web scraping libraries
2. Avoid common pitfalls (anti-bot/anti-scraping techniques)
3. Read robots.txt
4. Set the timeout parameter
5. Check error codes
6. Assess if the website has a public API
7. Use a multiprocessing package to increase web scraping speed

1.      Python Web Scraping Libraries

There are a number of Python web scraping libraries. These include:

• Python Requests library: it contains pre-written code that enables you to make HTTP/HTTPS requests. Find more info on it here
• Beautiful Soup: This is a parsing library
• lxml: this is a parsing library
• Scrapy: This is a Python framework that handles requests, parsing, and saving of the structured data
• Selenium: It is designed to render JavaScript code and is used alongside other libraries

Using Python libraries for web scraping eliminates the need to create everything from scratch. For instance, the Python Requests library provides a template containing numerous HTTP methods, including GET, POST, PATCH, PUT, and DELETE.

2.      Avoid Common Pitfalls

Modern websites employ anti-scraping techniques to protect the data stored in their servers. These techniques include honeypot traps, IP blocking, CAPTCHA puzzles, sign-in and login requirements, headers, and more. You can avoid these pitfalls using a headless browser, rotating proxies, an anti-detect browser, or reading the Robots.txt file (discussed below).  

3.      Read robots.txt file

The robots.txt file contains instructions that stipulate webpages that bots should not access. Adhering to these guidelines prevents IP blocking.

4.      Set Timeout Parameter

The Python Requests library is intended to make a request and will keep awaiting a response indefinitely, even in instances where the server is not available. It is therefore recommended to set a timeout parameter.

5.      Check Error Codes

It is advisable to frequently check the status codes returned by a web server to identify errors. This helps you establish whether your requests were timed out or blocked. In addition, your Python code should indicate what should be printed out should the scraper encounter an error code.

6.      Check for Public API

Some websites avail an application programming interface (API) through which you can easily and conveniently access publicly available data. Such a public API eliminates the need for creating a scraper.

7.      Multiprocessing Package

A multiprocessing package enables the system to handle multiple requests in parallel, thus speeding up the web scraping process. This comes in handy when you are dealing with numerous web pages.

Conclusion

Python is a versatile and general-purpose programming language that can be deployed to create web scrapers. If you want to create a web scraper, the tips highlighted in this article can boost your chances of success. Such tips include checking for error codes and the availability of a public API, using a multiprocessor package, setting timeout parameters, and more.