Alternatives to Web Scraping with Python

If you're reading this you probably need data for your business, a client, or a project that you’re working on. It’s also likely that you are scraping the web with Python, after all, it is the easiest way to scrape a website. Or is it? In this post, we’ll dive into the essentials of web scraping with Python, understand the pros and cons and learn about some commonly used alternatives including no-code scraping solutions.

If you are a developer, familiar with Python or just a little more code-savvy than the rest of us, scroll down a bit to read about alternative solutions to web scraping with Python.

For everyone else, let’s get started;

What is web scraping

Open source web data is probably one of the most valuable business assets currently available.

Companies are collecting data regarding target audience and competitor actions including such things as:

• Social media sentiment
• Product reviews
• Dynamic competitor pricing models
• Search engine trends
• Competitor advertising campaigns as well as audience engagement

This information empowers businesses to not merely guess what customers want or what is being successfully done in their industry but to make strategic decisions based on cold hard information.

Web scraping is the act of loading a page and extracting targeted data points into a ‘dataset’. This information can be structured and formatted as a JSON, CSV, HTML, or a Microsoft Excel file and delivered directly to the team or algorithms for analysis.

There are a number of programming languages that can be used in order to effectively scrape data from target sites - Python is at the top of this list.

Why Python?

Python is the better choice for web scraping due to the fact that it is a simple coding language that allows professionals to streamline their data collection processes. Python also enables access to numerous libraries such as NumPy, Matlpotlib, Pandas, as well as others - making the scope of access and data set manipulation much greater.

How to scrape web data with Python?

The first step in web scraping is to decide which framework to use. The framework defines which source code is supported in order to perform the web scraping task. It allows individuals to scrape websites easily and quickly.

Some examples of frameworks used for web scraping are:

• Requests: The requests module allows users to send HTTP requests using Python. The HTTP request returns a Response Object with all the response data (content, encoding, status, etc).
• BeautifulSoup: A Python library for pulling data out of HTML and XML files. It mimics user behavior for navigating and searching. It is known to save programmers hours or days of work.
• Pyquery: Also known as Python’s baby brother, easy to use but not as developed, ideal for parsing websites and fetching data, but that's about it.
• Selenium: Similar to Beautiful Soup, Selenium automates user activities that are key for navigating through a site in order to find and export the relevant data

Now that we have our framework in place, let’s dive into the scraping code itself.

Here is a simple example of a web scraper that extracts a list of laptops available on Amazon.

The best way to understand how a scraper works is by looking at the code. The code below will scrape the product name and price of every laptop on the results page.

The first code snippet imports all the needed libraries, the second sends page requests and returns the result in xml format, and the third displays all the prices and laptop names on the scrapped page respectively.

import pandas as pd
from bs4 import BeautifulSoup
import requests

url=f"https://www.amazon.co.uk/s?k=laptop"
result = requests.get(url)
soup = BeautifulSoup(result.content,'lxml')

# Display Price of Laptop
print(soup.find_all('span',{'class':'a-price-whole'})[0].contents[0])

# Display Name of Laptop
print(soup.find_all('span',{'class':'a-size-base-plus a-color-base a-text-normal'})[0].contents[0])

Bright Data, a pioneer in the web scraping industry published a helpful article on scraping with Python using BeautifulSoup, they show the full process from downloading the page, and extracting the required elements.

What developers say about web scraping with Python

Just like everything else in the world, there are always pros and cons. Here is some feedback that we received when asking Python developers how they feel about using Python for web scraping.

“Automatic extraction of data is a time saver, there is no way we could achieve our goals without using Python”.

“Structure is key when working with Python, but the secret is in the details. The better you define the data you need to extract the easier it is to scale up”.

“Python is great but it’s a coding language that is easy to break. Before starting a scraping project make sure you understand the ins and outs of Python and have a clear picture of what you are looking to achieve.”

Alternative coding languages for web scraping

There are many coding languages that can be used for web scraping other than Python. Here are the two main alternative coding languages for web scraping, JS and Ruby:

• JavaScript: JS is generally used for the development of the front-end of websites. Many applications can be developed using Node.js, a programming environment based on JS. Puppeteer is one of the most efficient libraries that is being offered by Node.js, it has the same behavior functions as chrome and chromium browsers, allowing developers to collect data as a real user would.

  
  

• Ruby: Although Ruby isn’t as popular as JavaScript or Python, it has multiple features that are beneficial for web scraping use cases like scrapping a static page or even a specific section of a page. It can offer multiple methods to parse both XML and HTML files.

  
  

No code solution for web scraping

If you’ve reached this part of the article you’ve either scrolled down to the “good part” or learned a lot about Python and the frameworks necessary for web scraping. Either way, you are about to find out how companies are scraping data at scale without prior knowledge of coding or even Python.

Web scraping services know how important data is today, it powers businesses and gives them the insight they need to thrive. What they lack is knowledge and time. Bright Data has been leading the data collection industry with its automated tools that provide a no-code solution for web scraping.

With hundreds of predefined web scraping templates built on common use cases, all users need to do is select the data they want to receive, how they want to receive it, and hit run. If you can’t find the right template for your project, they create it for you. If your passion is coding with Python, developing frameworks, and programming, you can edit templates or even create your own.

Should you use Python for web scraping?

Now that you are aware of all the options out there, you are probably asking yourself, is Python your best choice for web scraping?

The answer really depends on you;

If you are a developer or interested in coding and have the time to develop individual web scrapers for every project or If you are a developer with a passion for coding or someone that likes a good challenge, then Python is probably the best solution for you.

If you are a business owner, a manager, a marketer, or even a researcher that needs data, it is probably better to leave the web scraping to the professionals. Whether you choose to hire a developer or use a code-free service, it’s best to focus on the data you need and not how to scrape it.