Scraping Multiple Pages using BeautifulSoup

Learn how to effectively loop through multiple pages while scraping data with BeautifulSoup, and clean salary data for better analysis.
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Scraping Multiple Pages using BeautifulSoup: A Step-by-Step Guide

Web scraping is a powerful technique used to gather data from various webpages. In this guide, we'll discuss how to scrape multiple pages using BeautifulSoup, particularly when it comes to job listings. If you're facing issues while trying to navigate through multiple pages and collect all necessary data, this guide is for you!

The Problem: Limited Data from Single Page Scraping

You might have tried scraping job listings from a website, only to realize that your current method only retrieves data from a single page. For example, if you have a URL like https://www.cvbankas.lt/?padalinys%5B0%5D=76&page=1, it may only return 50 job listings. To gather comprehensive data, you typically need to scrape multiple pages — in this instance, pages from 1 to 8.

The Solution: Looping through Pages

To scrape data from multiple pages, we can implement a for loop in our code. Here’s how you can do it step-by-step:

Step 1: Setting Up the Environment

Make sure you have the necessary libraries installed. You will need requests, pandas, and BeautifulSoup. If you haven't installed them yet, you can do so using:

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Step 2: Import Libraries

Start your script by importing the required libraries:

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Step 3: Data Collection with Loops

Here’s a sample code to scrape multiple pages effectively:

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Step 4: Understand the Code

Looping: The for i in range(1, 9) line allows you to change the page number in the URL dynamically, which is essential for navigating through all job listings.

Data Collection: Each job listing's title, company, salary, and location are extracted and stored in a list of dictionaries (all_data).

Error Handling: The try-except block helps manage situations where some data might be missing to prevent the program from crashing.

Step 5: Clean Salary Data

After collecting the data, you might want to clean up the Salary column. The data may come in various formats, such as "Nuo 2700", "Iki 2500", or a range like "1000-3000". Appropriate parsing is necessary to convert these strings into integer values for analysis. Here’s a sample function to clean the salary data:

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Conclusion

By following this step-by-step guide, you should now be able to scrape multiple pages of job listings effectively. Remember to always check the website’s robots.txt file and terms of service to ensure that you are allowed to scrape their data. Happy scraping!

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